## Experience and Education

 2017-present Associate Professor of Computer Science Cornell University Spring 2019 Visiting Scholar, Department of Statistics University of Chicago Fall 2018 Faculty Research Participant Argonne National Laboratory 2009-2017 Assistant Professor of Computer Science Cornell University 2006-2009 Courant Instructor of Mathematics New York University 1999-2006 Ph.D. in Computer Science University of California, Berkeley 1995-1999 B.S. in Math and Computer Science University of Maryland, College Park

## Awards

 2018 Cornell COE Research Excellence Award Awarded annually to two Cornell engineering professors at each level. 2018 ASPLOS Most Influential Paper Award 2018 Recognizes a historical ASPLOS paper that has had major influence on the field. 2015 Middleware Best Student Paper 2015 KDD Best Student Paper 2015 SIGEST Featured Paper Featured paper in SIAM Review from specialist journals on a rotating basis 2015 SIAG/LA Prize Awarded once every three years for the best applied linear algebra journal paper. 2014 Douglas Whitney Award Awarded annually to a Cornell Engineering faculty for teaching excellence. 2010 Sloan Research Fellowship 2008 Alston S. Householder Award Awarded once every three years for best thesis in numerical linear algebra. 1999 NSF Graduate Student Fellowship

## Publications

### Journal papers

1. P. Shi, K. He, D. Bindel, and J. Hopcroft, “Krylov Subspace Approximation for Local Community Detection in Large Networks,” ACM Transactions on Knowledge Discovert from Data, 2019. In press.
@article{2019-tkdd,
author = {Shi, Pan and He, Kun and Bindel, David and Hopcroft, John},
title = {Krylov Subspace Approximation for Local Community Detection in Large Networks},
journal = {ACM Transactions on Knowledge Discovert from Data},
year = {2019},
submit = {In press.}
}


#### Abstract:

Community detection is an important information mining task to uncover modular structures in large networks. For increasingly common large network data sets, global community detection is prohibitively expensive, and attention has shifted to methods that mine local communities, i.e. identifying all latent members of a particular community from a few labeled seed members. To address such semi-supervised mining task, we systematically develop a local spectral subspace-based community detection method, called LOSP. We define a family of local spectral subspaces based on Krylov subspaces, and seek a sparse indicator for the target community via an $\ell_1$ norm minimization over the Krylov subspace. Variants of LOSP depend on type of random walks with different diffusion speeds, type of random walks, dimension of the local spectral subspace, and step of diffusions. The effectiveness of the proposed LOSP approach is theoretically analyzed based on Rayleigh quotients, and it is experimentally verified on a wide variety of real-world networks across social, production and biological domains, as well as on an extensive set of synthetic LFR benchmark datasets.

2. M. A. Gilles, C. Earls, and D. Bindel, “A subspace pursuit method to infer refractivity in the marine atmospheric boundary layer,” IEEE Transactions on Geosciences and Remote Sensing, Jan. 2019.
@article{2019-subspace,
author = {Gilles, Marc Aur\'ele and Earls, Christopher and Bindel, David},
title = {A subspace pursuit method to infer refractivity in the marine atmospheric boundary layer},
month = jan,
year = {2019},
journal = {IEEE Transactions on Geosciences and Remote Sensing},
arxiv = {1901.06432},
doi = {10.1109/TGRS.2019.2900582}
}


#### Abstract:

Inferring electromagnetic propagation characteristics within the marine atmospheric boundary layer (MABL) from data in real time is crucial for modern maritime navigation and communications. The propagation of electromagnetic waves is well modeled by a partial differential equation (PDE): a Helmholtz equation. A natural way to solve the MABL characterization inverse problem is to minimize what is observed and what is predicted by the PDE. However, this optimization is difficult because it has many local minima. We propose an alternative solution that relies on the properties of the PDE but does not involve solving the full forward model. Ducted environments result in an EM field which can be decomposed into a few propagating, trapped modes. These modes are a subset of the solutions to a Sturm-Liouville eigenvalue problem. We design a new objective function that measures the distance from the observations to a subspace spanned by these eigenvectors. The resulting optimization problem is much easier than the one that arises in the standard approach, and we show how to solve the associated nonlinear eigenvalue problem efficiently, leading to a real-time method.

3. P. Shi, K. He, D. Bindel, and J. Hopcroft, “Locally-biased Spectral Approximation for Community Detection,” Knowledge-Based Systems, vol. 164, pp. 459–472, 2019.
@article{2019-knosys,
author = {Shi, Pan and He, Kun and Bindel, David and Hopcroft, John},
title = {Locally-biased Spectral Approximation for Community Detection},
journal = {Knowledge-Based Systems},
volume = {164},
pages = {459--472},
year = {2019},
doi = {10.1016/j.knosys.2018.11.012}
}


#### Abstract:

We propose a Locally-Biased Spectral Approximation (LBSA) approach for identifying all latent members of a local community from very few seed members. To reduce the computation complexity, we first apply a fast random walk, personalized PageRank and heat kernel diffusion to sample a comparatively small subgraph covering almost all potential community members around the seeds. Then starting from a normalized indicator vector of the seeds and by a few steps of either Lanczos iteration or power iteration on the sampled subgraph, a local eigenvector is gained for approximating the eigenvector of the transition matrix with the largest eigenvalue. Elements of this local eigenvector is a relaxed indicator for the affiliation probability of the corresponding nodes to the target community. We conduct extensive experiments on real-world datasets in various domains as well as synthetic datasets. Results show that the proposed method outperforms state-of-the-art local community detection algorithms. To the best of our knowledge, this is the first work to adapt the Lanczos method for local community detection, which is natural and potentially effective. Also, we did the first attempt of using heat kernel as a sampling method instead of detecting communities directly, which is proved empirically to be very efficient and effective.

4. C. Ponce, D. Bindel, and P. Vassilevski, “A Nonlinear Algebraic Multigrid Framework for the Power Flow Equations,” SIAM Journal of Scientific Computing, vol. 40, no. 3, pp. B812–B833, 2018.
@article{2018-sisc,
author = {Ponce, Colin and Bindel, David and Vassilevski, Panayot},
title = {A Nonlinear Algebraic Multigrid Framework for the Power Flow Equations},
journal = {SIAM Journal of Scientific Computing},
volume = {40},
number = {3},
pages = {B812--B833},
year = {2018},
doi = {10.1137/16M1109965}
}


#### Abstract:

Multigrid is a highly scalable class of methods most often used for solving large linear systems. In this paper we develop a nonlinear algebraic multigrid framework for the power flow equations, a complex quadratic system of the form ${diag}({v})\overline{Y{v}}={s}$, where $Y$ is approximately a complex scalar rotation of a real graph Laplacian. This is a standard problem that needs to be solved repeatedly during power grid simulations. A key difference between our multigrid framework and typical multigrid approaches is the use of a novel multiplicative coarse-grid correction to enable a dynamic multigrid hierarchy. We also develop a new type of smoother that allows one to coarsen together the different types of nodes that appear in power grid simulations. In developing a specific multigrid method, one must make a number of choices that can significantly affect the method’s performance, such as how to construct the restriction and interpolation operators, what smoother to use, and how aggressively to coarsen. In this paper, we make simple but reasonable choices that result in a scalable and robust power flow solver. Experiments demonstrate this scalability and show that it is significantly more robust to poor initial guesses than current state-of-the-art solvers.

5. Y. Li, K. Kloster, K. He, D. Bindel, and J. Hopcroft, “Local Spectral Clustering for Overlapping Community Detection,” ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 2, p. 17, 2018.
@article{2018-tkdd,
author = {Li, Yixuan and Kloster, Kyle and He, Kun and Bindel, David and Hopcroft, John},
title = {Local Spectral Clustering for Overlapping Community Detection},
journal = {ACM Transactions on Knowledge Discovery from Data},
volume = {12},
number = {2},
pages = {17},
year = {2018},
doi = {10.1145/3106370}
}


#### Abstract:

Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms for mining communities have focused on global graph structure, and often run in time proportional to the size of the entire graph. As we explore networks with millions of vertices and find communities of size in the hundreds, it becomes important to shift our attention from macroscopic structure to microscopic structure in large networks. A growing body of work has been adopting local expansion methods in order to identify communities from a few exemplary seed members.

In this article, we propose a novel approach for finding overlapping communities called Lemon (Local Expansion via Minimum One Norm). Provided with a few known seeds, the algorithm finds the community by performing a local spectral diffusion. The core idea of Lemon is to use short random walks to approximate an invariant subspace near a seed set, which we refer to as local spectra. Local spectra can be viewed as the low-dimensional embedding that captures the nodes’ closeness in the local network structure. We show that Lemon’s performance in detecting communities is competitive with state-of-the-art methods. Moreover, the running time scales with the size of the community rather than that of the entire graph. The algorithm is easy to implement and is highly parallelizable. We further provide theoretical analysis of the local spectral properties, bounding the measure of tightness of extracted community using the eigenvalues of graph Laplacian.

We thoroughly evaluate our approach using both synthetic and real-world datasets across different domains, and analyze the empirical variations when applying our method to inherently different networks in practice. In addition, the heuristics on how the seed set quality and quantity would affect the performance are provided.

6. C. Ponce and D. Bindel, “FLiER: Practical Topology Update Detection Using Sparse PMUs,” IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4222–4232, 2017.
@article{2017-tps,
author = {Ponce, Colin and Bindel, David},
title = {{FLiER}: Practical Topology Update Detection Using Sparse {PMU}s},
journal = {IEEE Transactions on Power Systems},
volume = {32},
number = {6},
pages = {4222--4232},
year = {2017},
doi = {10.1109/TPWRS.2017.2662002}
}


#### Abstract:

In this paper, we present a Fingerprint Linear Estimation Routine (FLiER) to identify topology changes in power networks using readings from sparsely-deployed phasor measurement units (PMUs). When a power line, load, or generator trips in a network, or when a substation is reconfigured, the event leaves a unique “voltage fingerprint” of bus voltage changes that we can identify using only the portion of the network directly observed by the PMUs. The naive brute-force approach to identify a failed line from such voltage fingerprints, though simple and accurate, is slow.We derive an approximate algorithm based on a local linearization and a novel filtering approach that is faster and only slightly less accurate. We present experimental results using the IEEE 57-bus, IEEE 118-bus, and Polish 1999- 2000 winter peak networks.

7. E. Yilmaz and D. Bindel, “Temperature Sensitivity and Shape Optimization of Solid-State Wave Gyroscopes,” IEEE Sensors, vol. 16, no. 6, pp. 6213–6221, 2016.
@article{2016-sensors,
author = {Yilmaz, Erdal and Bindel, David},
title = {Temperature Sensitivity and Shape Optimization of
Solid-State Wave Gyroscopes},
journal = {IEEE Sensors},
volume = {16},
number = {6},
pages = {6213--6221},
year = {2016},
doi = {10.1109/JSEN.2016.2580670}
}


#### Abstract:

We analyze the change of angular gain and vibration frequency of solid-state wave gyroscopes as a result of geometry perturbations due to thermal expansion. We analyze sensitivity of the device to thermal expansion effects by an isoparametric finite element analysis method, and we analyze the sensitivity to thermal changes in the material properties assuming a linear dependence on temperature. We quantify these sensitivities for common device geometries, and use our analysis as the basis for a local optimization problem that minimizes temperature sensitivity as a function of device shape.

8. D. Bindel and A. Hood, “Localization Theorems for Nonlinear Eigenvalues,” SIAM Review, vol. 57, no. 4, pp. 585–607, Dec. 2015.
SIGEST feature article.
@article{2015-sirev,
author = {Bindel, David and Hood, Amanda},
title = {Localization Theorems for Nonlinear Eigenvalues},
journal = {SIAM Review},
publisher = {SIAM},
volume = {57},
number = {4},
pages = {585--607},
month = dec,
year = {2015},
notable = {SIGEST feature article.},
doi = {10.1137/15M1026511}
}


#### Abstract:

Let $T : \Omega \rightarrow {\Bbb C}^{n\times n}$ be a matrix-valued function that is analytic on some simply-connected domain $\Omega \subset {\Bbb C}$. A point $\lambda \in \Omega$ is an eigenvalue if the matrix $T(\lambda)$ is singular. In this paper, we describe new localization results for nonlinear eigenvalue problems that generalize Gershgorin’s theorem, pseudospectral inclusion theorems, and the Bauer-Fike theorem. We use our results to analyze three nonlinear eigenvalue problems: an example from delay differential equations, a problem due to Hadeler, and a quantum resonance computation.

9. D. Bindel, J. Kleinberg, and S. Oren, “How Bad is Forming Your Own Opinion?,” Games and Economic Behavior, vol. 92, no. C, pp. 248–265, 2015.
@article{2015-geb,
author = {Bindel, David and Kleinberg, Jon and Oren, Sigal},
journal = {Games and Economic Behavior},
volume = {92},
number = {C},
year = {2015},
pages = {248--265},
doi = {10.1016/j.geb.2014.06.004},
arxiv = {1203.2973}
}


#### Abstract:

The question of how people form their opinion has fascinated economists and sociologists for long time. In many of the models, a group of people in a social network, each holding a numerical opinion, arrive at a shared opinion through repeated averaging with their neighbors in the network. Motivated by the observation that in reality consensus is rarely reached, we study a related sociological model in which individuals’ intrinsic beliefs counterbalance the averaging process and yield a diversity of opinions.

We interpret the repeated averaging process as best-response dynamics in an underlying game with natural payoffs and its limit as an equilibrium. This allows us to study the cost of disagreement by comparing between the cost at equilibrium and the social optimum. We also consider a natural network design problem in this setting: which links can we add to the underlying network to reduce the cost at equilibrium?

10. D. Bindel, M. Friedman, W. Govaerts, J. Hughes, and Y. A. Kuznetsov, “Numerical Computation of Bifurcations in Large Equilibrium Systems in MATLAB,” Journal of Computational and Applied Mathematics, vol. 261, pp. 232–248, 2014.
@article{2014-matcont,
author = {Bindel, David and Friedman, Mark and Govaerts, Willy and Hughes, Jeremy and Kuznetsov, Yuri A.},
title = {Numerical Computation of Bifurcations in
Large Equilibrium Systems in {MATLAB}},
journal = {Journal of Computational and Applied Mathematics},
volume = {261},
pages = {232--248},
year = {2014},
doi = {10.1016/j.cam.2013.10.034}
}


#### Abstract:

The Continuation of Invariant Subspaces (CIS) algorithm produces a smoothly-varying basis for an invariant subspace R(s) of a parameter-dependent matrix $A(s)$. We have incorporated the CIS algorithm into Cl_matcont, a Matlab package for the study of dynamical systems and their bifurcations. Using subspace reduction, we extend the functionality of Cl_matcont to large-scale computations of bifurcations of equilibria. In this paper, we describe the algorithms and functionality of the resulting Matlab bifurcation package Cl_matcontL. The novel features include: new CIS-based, continuous, well-scaled test functions for codimension 1 and 2 bifurcations; detailed description of locators for large problems; and examples of bifurcation analysis in large sparse problems.

11. D. Bindel and A. Hood, “Localization Theorems for Nonlinear Eigenvalues,” SIAM Journal on Matrix Analysis, vol. 34, no. 4, pp. 1728–1749, 2013.
2015 SIAG/LA award (best journal paper in applied LA in three years).
@article{2013-simax,
author = {Bindel, David and Hood, Amanda},
title = {Localization Theorems for Nonlinear Eigenvalues},
journal = {SIAM Journal on Matrix Analysis},
volume = {34},
number = {4},
pages = {1728--1749},
year = {2013},
doi = {10.1137/130913651},
arxiv = {http://arxiv.org/abs/1303.4668},
notable = {2015 SIAG/LA award (best journal paper in applied LA in three years).}
}


#### Abstract:

Let $T : \Omega \rightarrow {\Bbb C}^{n \times n}$ be a matrix-valued function that is analytic on some simply-connected domain $\Omega \subset {\Bbb C}$. A point $\lambda \in \Omega$ is an eigenvalue if the matrix $T(\lambda)$ is singular. In this paper, we describe new localization results for nonlinear eigenvalue problems that generalize Gershgorin’s theorem, pseudospectral inclusion theorems, and the Bauer-Fike theorem. We use our results to analyze three nonlinear eigenvalue problems: an example from delay differential equations, a problem due to Hadeler, and a quantum resonance computation.

12. W. Xie, G. Wang, D. Bindel, A. Demers, and J. Gehrke, “Fast Iterative Graph Computation with Block Updates,” Proceedings of the VLDB Endowment, vol. 6, no. 14, pp. 2014–2025, 2013.
@article{2013-blockgrace,
author = {Xie, Wenlei and Wang, Guozhang and Bindel, David and Demers, Alan and Gehrke, Johannes},
journal = {Proceedings of the VLDB Endowment},
number = {14},
pages = {2014--2025},
publisher = {VLDB Endowment},
title = {Fast Iterative Graph Computation with Block Updates},
volume = {6},
year = {2013},
doi = {10.14778/2556549.2556581},
code = {https://github.com/wenleix/BlockGRACE}
}


#### Abstract:

Scaling iterative graph processing applications to large graphs is an important problem. Performance is critical, as data scientists need to execute graph programs many times with varying parameters. The need for a high-level, high-performance programming model has inspired much research on graph programming frameworks. In this paper, we show that the important class of computationally light graph applications – applications that perform little computation per vertex – has severe scalability problems across multiple cores as these applications hit an early “memory wall” that limits their speedup. We propose a novel block-oriented computation model, in which computation is iterated locally over blocks of highly connected nodes, significantly improving the amount of computation per cache miss. Following this model, we describe the design and implementation of a block-aware graph processing runtime that keeps the familiar vertex-centric programming paradigm while reaping the benefits of block-oriented execution. Our experiments show that block-oriented execution significantly improves the performance of our framework for several graph applications.

13. W. He, D. Bindel, and S. Govindjee, “Topology Optimization in Micromechanical Resonator Design,” Optimization and Engineering, vol. 13, no. 2, 2012.
@article{2012-mems-opt,
author = {He, Wei and Bindel, David and Govindjee, Sanjay},
title = {Topology Optimization in Micromechanical Resonator Design},
journal = {Optimization and Engineering},
volume = {13},
number = {2},
year = {2012},
doi = {10.1007/s11081-011-9139-1}
}


#### Abstract:

A topology optimization problem in micromechanical resonator design is addressed in this paper. The design goal is to control the first several eigenfrequencies of a micromechanical resonator using topology optimization. The design variable is the distribution of mass in a constrained domain which we model via (1) the Simple Isotropic Material with Penalization Model and (2) the Peak Function Model. The overall optimization problem is solved using the Method of Moving Asymptotes and a Genetic Algorithm combined with a local gradient method. A numerical example is presented to highlight the features of the methods in more detail. The advantages and disadvantages of each method are discussed.

14. Y. Zhao, Y. Chen, and D. Bindel, “Towards Unbiased End-to-End Network Diagnosis,” IEEE/ACM Transactions on Networking, vol. 17, no. 6, pp. 1724–1737, Dec. 2009.
@article{2009-tons,
author = {Zhao, Yao and Chen, Yan and Bindel, David},
title = {Towards Unbiased End-to-End Network Diagnosis},
journal = {IEEE/ACM Transactions on Networking},
volume = {17},
number = {6},
pages = {1724--1737},
month = dec,
year = {2009},
doi = {10.1109/TNET.2009.2022158}
}


#### Abstract:

Internet fault diagnosis is extremely important for end-users, overlay network service providers (like Akamai ), and even Internet service providers (ISPs). However, because link-level properties cannot be uniquely determined from end-to-end measurements, the accuracy of existing statistical diagnosis approaches is subject to uncertainty from statistical assumptions about the network. In this paper, we propose a novel least-biased end-to-end network diagnosis (in short, LEND) system for inferring link-level properties like loss rate. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. We also design efficient algorithms to find all the MILSs and infer their loss rates for diagnosis. Our LEND system works for any network topology and for both directed and undirected properties and incrementally adapts to network topology and property changes. It gives highly accurate estimates of the loss rates of MILSs, as indicated by both extensive simulations and Internet experiments. Furthermore, we demonstrate that such diagnosis can be achieved with fine granularity and in near real-time even for reasonably large overlay networks. Finally, LEND can supplement existing statistical inference approaches and provide smooth tradeoff between diagnosis accuracy and granularity.

15. D. Bindel, J. Demmel, and M. Friedman, “Continuation of Invariant Subspaces in Large Bifurcation Problems,” SIAM Journal on Scientific Computing, vol. 30, no. 2, pp. 637–656, Feb. 2008.
@article{2008-cis,
author = {Bindel, David and Demmel, James and Friedman, Mark},
title = {Continuation of Invariant Subspaces in
Large Bifurcation Problems},
journal = {SIAM Journal on Scientific Computing},
volume = {30},
number = {2},
pages = {637--656},
month = feb,
year = {2008},
doi = {10.1137/060654219}
}


#### Abstract:

We summarize an algorithm for computing a smooth orthonormal basis for an invariant subspace of a parameter-dependent matrix, and describe how to extend it for numerical bifurcation analysis. We adapt the continued subspace to track behavior relevant to bifurcations, and use projection methods to deal with large problems. To test our ideas, we have integrated our code into MATCONT, a program for numerical continuation and bifurcation analysis.

16. Y. Chen, D. Bindel, H. Song, B. Chavez, and R. Katz, “Algebra-Based Scalable Overlay Network Monitoring: Algorithms, Evaluation, and Applications,” ACM Transactions on Networking, vol. 15, no. 5, pp. 1084–1097, Oct. 2007.
@article{2007-tons,
author = {Chen, Yan and Bindel, David and Song, Hanhee and Chavez, Brian and Katz, Randy},
title = {Algebra-Based Scalable Overlay Network Monitoring:
Algorithms, Evaluation, and Applications},
journal = {ACM Transactions on Networking},
volume = {15},
number = {5},
pages = {1084--1097},
month = oct,
year = {2007},
doi = {10.1109/TNET.2007.896251}
}


#### Abstract:

Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with end hosts, existing systems either require measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Our earlier extended abstract [Y. Chen, D. Bindel, and R. H. Katz, “Tomography-based overlay network monitoring,” Proceedings of the ACM SIGCOMM Internet Measurement Conference (IMC), 2003] briefly proposes an algebraic approach that selectively monitors linearly independent paths that can fully describe all the paths. The loss rates and latency of these paths can be used to estimate the loss rates and latency of all other paths. Our scheme only assumes knowledge of the underlying IP topology, with links dynamically varying between lossy and normal. In this paper, we improve, implement, and extensively evaluate such a monitoring system. We further make the following contributions: i) scalability analysis indicating that for reasonably large n (e.g., 100), the growth of $k$ is bounded as $O(n \log n)$, ii) efficient adaptation algorithms for topology changes, such as the addition or removal of end hosts and routing changes, iii) measurement load balancing schemes, iv) topology measurement error handling, and v) design and implementation of an adaptive streaming media system as a representative application. Both simulation and Internet experiments demonstrate we obtain highly accurate path loss rate estimation while adapting to topology changes within seconds and handling topology errors.

17. D. Bindel and M. Zworski, “Symmetry of Bound and Antibound States in the Semiclassical Limit,” Letters in Math Physics, vol. 81, no. 2, pp. 107–117, Aug. 2007.
@article{2007-symmetry,
author = {Bindel, David and Zworski, Maciej},
title = {Symmetry of Bound and Antibound States in the Semiclassical Limit},
journal = {Letters in Math Physics},
volume = {81},
number = {2},
pages = {107--117},
month = aug,
year = {2007},
doi = {10.1007/s11005-007-0178-7}
}


#### Abstract:

Motivated by a recent numerical observation we show that in one dimensional scattering a barrier separating the interaction region from infinity implies approximate symmetry of bound and antibound states. We also outline the numerical procedure used for an efficient computation of one dimensional resonances.

18. D. Bindel and S. Govindjee, “Elastic PMLs for Resonator Anchor Loss Simulation,” International Journal for Numerical Methods in Engineering, vol. 64, no. 6, pp. 789–818, Oct. 2005.
@article{2005-ijnme,
author = {Bindel, David and Govindjee, Sanjay},
title = {Elastic {PMLs} for Resonator Anchor Loss Simulation},
journal = {International Journal for Numerical Methods in Engineering},
volume = {64},
number = {6},
pages = {789--818},
month = oct,
year = {2005},
doi = {10.1002/nme.1394}
}


#### Abstract:

Electromechanical resonators and filters, such as quartz, ceramic, and surface-acoustic wave devices, are important signal-processing elements in communication systems. Over the past decade, there has been substantial progress in developing new types of miniaturized electromechanical resonators using microfabrication processes. For these micro-resonators to be viable they must have high and predictable quality factors ($Q$). Depending on scale and geometry, the energy losses that lower $Q$ may come from material damping, thermoelastic damping, air damping, or radiation of elastic waves from an anchor. Of these factors, anchor losses are the least understood because such losses are due to a complex radiation phenomena in a semi-infinite elastic half-space. Here, we describe how anchor losses can be accurately computed using an absorbing boundary based on a perfectly matched layer (PML) which absorbs incoming waves over a wide frequency range for any non-zero angle of incidence. We exploit the interpretation of the PML as a complex-valued change of coordinates to illustrate how one can come to a simpler finite element implementation than was given in its original presentations. We also examine the convergence and accuracy of the method, and give guidelines for how to choose the parameters effectively. As an example application, we compute the anchor loss in a micro disk resonator and compare it to experimental data. Our analysis illustrates a surprising mode-mixing phenomenon which can substantially affect the quality of resonance.

19. D. Bindel, J. Demmel, W. Kahan, and O. Marques, “On Computing Givens Rotations Reliable and Efficiently,” ACM Transactions on Mathematical Software, vol. 28, no. 2, pp. 206–238, Jun. 2002.
@article{2002-toms,
author = {Bindel, David and Demmel, James and Kahan, William and Marques, Osni},
title = {On Computing {Givens} Rotations Reliable and Efficiently},
journal = {ACM Transactions on Mathematical Software},
volume = {28},
number = {2},
pages = {206--238},
month = jun,
year = {2002},
doi = {10.1145/567806.567809}
}


#### Abstract:

We consider the efficient and accurate computation of Givens rotations. When $f$ and $g$ are positive real numbers, this simply amounts to computing the values of $c = f/\sqrt{f^2 + g^2}$, $s = g/\sqrt{f^2 + g^2}$, and $r = \sqrt{f^2 + g^2}$. This apparently trivial computation merits closer consideration for the following three reasons. First, while the definitions of $c$, $s$ and $r$ seem obvious in the case of two nonnegative arguments $f$ and $g$, there is enough freedom of choice when one or more of $f$ and $g$ are negative, zero or complex that LAPACK auxiliary routines SLARTG, CLARTG, SLARGV and CLARGV can compute rather different values of $c$, $s$ and $r$ for mathematically identical values of $f$ and $g$. To eliminate this unnecessary ambiguity, the BLAS Technical Forum chose a single consistent definition of Givens rotations that we will justify here. Second, computing accurate values of $c$, $s$ and $r$ as efficiently as possible and reliably despite over/underflow is surprisingly complicated. For complex Givens rotations, the most efficient formulas require only one real square root and one real divide (as well as several much cheaper additions and multiplications), but a reliable implementation using only working precision has a number of cases. On a Sun Ultra-10, the new implementation is slightly faster than the previous LAPACK implementation in the most common case, and 2.7 to 4.6 times faster than the corresponding vendor, reference or ATLAS routines. It is also more reliable; all previous codes occasionally suffer from large inaccuracies due to over/underflow. For real Givens rotations, there are also improvements in speed and accuracy, though not as striking. Third, the design process that led to this reliable implementation is quite systematic, and could be applied to the design of similarly reliable subroutines.

### Conference papers

1. K. Dong, A. R. Benson, and D. Bindel, “Network density of states,” in Proceedings of KDD, 2019.
Best research paper award
@inproceedings{2019-kdd,
author = {Dong, Kun and Benson, Austin R. and Bindel, David},
title = {Network density of states},
booktitle = {Proceedings of KDD},
month = aug,
year = {2019},
arxiv = {1905.09758},
doi = {10.1145/3292500.3330891},
notable = {Best research paper award}
}


#### Abstract:

Spectral analysis connects graph structure to the eigenvalues and eigenvectors of associated matrices. Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators. But the translation from spectral geometry to spectral graph theory has largely focused on results involving only a few extreme eigenvalues and their associated eigenvalues. Unlike in geometry, the study of graphs through the overall distribution of eigenvalues - the spectral density - is largely limited to simple random graph models. The interior of the spectrum of real-world graphs remains largely unexplored, difficult to compute and to interpret.

In this paper, we delve into the heart of spectral densities of real-world graphs. We borrow tools developed in condensed matter physics, and add novel adaptations to handle the spectral signatures of common graph motifs. The resulting methods are highly efficient, as we illustrate by computing spectral densities for graphs with over a billion edges on a single compute node. Beyond providing visually compelling fingerprints of graphs, we show how the estimation of spectral densities facilitates the computation of many common centrality measures, and use spectral densities to estimate meaningful information about graph structure that cannot be inferred from the extremal eigenpairs alone.

2. D. Eriksson, K. Dong, E. Lee, D. Bindel, and A. G. Wilson, “Scaling Gaussian Process Regression with Derivatives,” in Proceedings of NeuroIPS 2018, 2018.
@inproceedings{2018-nips-a,
author = {Eriksson, David and Dong, Kun and Lee, Eric and Bindel, David and Wilson, Andrew Gordon},
title = {Scaling {Gaussian} Process Regression with Derivatives},
booktitle = {Proceedings of NeuroIPS 2018},
month = dec,
year = {2018},
arxiv = {1810.12283.pdf},
}


#### Abstract:

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a GP to function values and derivatives at $n$ points in $d$ dimensions requires linear solves and log determinants with an $n(d + 1) × n(d + 1)$ positive definite matrix – leading to prohibitive $O(n^3 d^3)$ computations for standard direct methods. We propose iterative solvers using fast $O(nd)$ matrix-vector multiplications (MVMs), together with pivoted Cholesky preconditioning that cuts the iterations to convergence by several orders of magnitude, allowing for fast kernel learning and prediction. Our approaches, together with dimensionality reduction, enables Bayesian optimization with derivatives to scale to high-dimensional problems and large evaluation budgets.

3. J. Gardner, G. Pleiss, K. Weinberger, D. Bindel, and A. G. Wilson, “GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration,” in Proceedings of NeuroIPS 2018, 2018.
@inproceedings{2018-nips-b,
author = {Gardner, Jacob and Pleiss, Geoff and Weinberger, Killian and Bindel, David and Wilson, Andrew Gordon},
title = {{GPyTorch}: Blackbox Matrix-Matrix {Gaussian} Process Inference with {GPU} Acceleration},
booktitle = {Proceedings of NeuroIPS 2018},
month = dec,
year = {2018},
arxiv = {1809.11165.pdf},
}


#### Abstract:

Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficient and general approach to GP inference based on Blackbox Matrix-Matrix multiplication (BBMM). BBMM inference uses a modified batched version of the conjugate gradients algorithm to derive all terms for training and inference in a single call. BBMM reduces the asymptotic complexity of exact GP inference from $O(n^3)$ to $O(n^2)$. Adapting this algorithm to scalable approximations and complex GP models simply requires a routine for efficient matrix-matrix multiplication with the kernel and its derivative. In addition, BBMM uses a specialized preconditioner to substantially speed up convergence. In experiments we show that BBMM effectively uses GPU hardware to dramatically accelerate both exact GP inference and scalable approximations. Additionally, we provide GPyTorch, a software platform for scalable GP inference via BBMM, built on PyTorch.

4. K. Dong, D. Eriksson, H. Nickisch, D. Bindel, and A. G. Wilson, “Scalable Log Determinants for Gaussian Process Kernel Learning,” in Proceedings of NIPS 2017, 2017.
@inproceedings{2017-nips,
author = {Dong, Kun and Eriksson, David and Nickisch, Hannes and Bindel, David and Wilson, Andrew Gordon},
booktitle = {Proceedings of NIPS 2017},
title = {Scalable Log Determinants for Gaussian Process Kernel Learning},
year = {2017},
month = dec,
arxiv = {1711.03481.pdf}
}


#### Abstract:

For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives – leading to prohibitive $O(n^3)$ computations. We propose novel $O(n)$ approaches to estimating these quantities from only fast matrix vector multiplications (MVMs). These stochastic approximations are based on Chebyshev, Lanczos, and surrogate models, and converge quickly even for kernel matrices that have challenging spectra. We leverage these approximations to develop a scalable Gaussian process approach to kernel learning. We find that Lanczos is generally superior to Chebyshev for kernel learning, and that a surrogate approach can be highly efficient and accurate with popular kernels.

5. M. Lee, D. Bindel, and D. Mimno, “From Correlation to Hierarchy: Practical Topic Modeling via Spectral Inference,” in Proceedings of the 12th INFORMS Workshop on Data Mining and Decision Analytics, 2017.
Best student paper award
@inproceedings{2017-informs,
author = {Lee, Moontae and Bindel, David and Mimno, David},
title = {From Correlation to Hierarchy: Practical Topic Modeling via Spectral Inference},
booktitle = {Proceedings of the 12th INFORMS Workshop on Data Mining and Decision Analytics},
year = {2017},
month = oct,
notable = {Best student paper award}
}


#### Abstract:

Topic models were originally applied in text analysis for extracting high-level themes from documents, but they work equally well in any setting where users select items from an inventory. Recent work in spectral topic modeling has provided algorithms that operate only on easily-collected summary statistics, rather than exhaustively iterating over the full dataset. The “anchor word” algorithms learn topics by decomposing the co-occurrence between pairs of words into a matrix of topics over words and a matrix of relations between topics. While these algorithms provide transparent inference and provable guarantees in addition to scalability, there are several known issues: inference can be infeasible for large vocabularies and cannot learn quality topics on noisy real data with high sensitivity to learning parameters. In this paper, we solidify the foundations of anchor-based spectral inference and propose practical algorithms that can efficiently tackle each of these problems within the framework of Joint Stochastic Matrix Factorization. These algorithms preserve the provable guarantees and scalability of earlier algorithms, but are more consistent and more stable in identifying quality topics. In addition, this algorithm can also consider and learn meaningful correlations between topics, enabling correlated and hierarchical models. We demonstrate these methods on two text corpora, a corpus of user movie ratings, and a corpus of song playlists.

6. P. Shi, K. He, D. Bindel, and J. Hopcroft, “Local Lanczos Spectral Approximation for Community Detection,” in Proceedings of ECML-PKDD, 2017.
@inproceedings{2017-ecml-pkdd,
author = {Shi, Pan and He, Kun and Bindel, David and Hopcroft, John},
title = {Local Lanczos Spectral Approximation for Community Detection},
booktitle = {Proceedings of ECML-PKDD},
year = {2017},
month = sep
}


#### Abstract:

We propose a novel approach called the Local Lanczos Spectral Approximation (LLSA) for identifying all latent members of a local community from very few seed members. To reduce the computation complexity, we first apply a fast heat kernel diffusing to sample a comparatively small subgraph covering almost all possible community members around the seeds. Then starting from a normalized indicator vector of the seeds and by a few steps of Lanczos iteration on the sampled subgraph, a local eigenvector is gained for approximating the eigenvector of the transition matrix with the largest eigenvalue. Elements of this local eigenvector is a relaxed indicator for the affiliation probability of the corresponding nodes to the target community. We conduct extensive experiments on real-world datasets in various domains as well as synthetic datasets. Results show that the proposed method outperforms state-of-the-art local community detection algorithms. To the best of our knowledge, this is the first work to adapt the Lanczos method for local community detection, which is natural and potentially effective. Also, we did the first attempt of using heat kernel as a sampling method instead of detecting communities directly, which is proved empirically to be very efficient and effective.

7. K. Wilson, D. Bindel, and N. Snavely, “When is Rotations Averaging Hard?,” in Proceedings of ECCV 2016, 2016.
@inproceedings{2016-rotations,
author = {Wilson, Kyle and Bindel, David and Snavely, Noah},
booktitle = {Proceedings of ECCV 2016},
title = {When is Rotations Averaging Hard?},
month = oct,
year = {2016}
}


#### Abstract:

Rotations averaging has become a key subproblem in global Structure from Motion methods. Several solvers exist, but they do not have guarantees of correctness. They can produce high-quality results, but also sometimes fail. Our understanding of what makes rotations averaging problems easy or hard is still very limited. To investigate the difficulty of rotations averaging, we perform a local convexity analysis under an $L_2$ cost function. Although a previous result has shown that in general, this problem is locally convex almost nowhere, we show how this negative conclusion can be reversed by considering the gauge ambiguity.

Our theoretical analysis reveals the factors that determine local convexity—noise and graph structure—as well as how they interact, which we describe by a particular Laplacian matrix. Our results are useful for predicting the difficulty of problems, and we demonstrate this on practical datasets. Our work forms the basis of a deeper understanding of the key properties of rotations averaging problems, and we discuss how it can inform the design of future solvers for this important problem.

8. K. He, P. Shi, J. Hopcroft, and D. Bindel, “Local Spectral Diffusion for Robust Community Detection,” in KDD Workshop on Mining and Learning with Graphs, 2016.
@inproceedings{2016-losp-kdd,
author = {He, Kun and Shi, Pan and Hopcroft, John and Bindel, David},
booktitle = {KDD Workshop on Mining and Learning with Graphs},
title = {Local Spectral Diffusion for Robust Community Detection},
month = aug,
year = {2016}
}


#### Abstract:

We address a semi-supervised learning problem of identifying all latent members of a local community from very few labeled seed members in large networks. By a simple and efficient sampling method, we conduct a comparatively small subgraph encompassing most of the latent members such that the follow-up membership identification could focus on an accurate local region instead of the whole network. Then we look for a sparse vector, a relaxed indicator vector representing the subordinative probability of the corresponding nodes, that lies in a local spectral subspace defined by an order-$d$ Krylov subspace. The subspace serves as a local proxy for the invariant subspace spanned by leading eigenvectors of the Laplacian matrices. Based on Rayleigh quotients, we relate the local membership identification task as a local RatioCut or local normalized cut optimization problem, and provide some theoretical justifications.

We thoroughly explore different probability diffusion methods for the subspace definition and evaluate our method on four groups with a total of 28 representative LFR benchmark datasets, and eight public available real-world networks with labeled ground truth communities across multiple domains. Experimental results exhibit the effectiveness and robustness of the proposed algorithm, and the local spectral communities perform better than those from the celebrated Heat Kernel diffusion and the PageRank diffusion.

9. E. Yilmaz and D. Bindel, “Temperature Sensitivity of Solid-Wave Gyroscopes (Late News),” in Proceedings of the Hilton Head Solid-Sate Sensor and Actuator Workshop 2016, 2016.
@inproceedings{2016-hh-workshop,
author = {Yilmaz, Erdal and Bindel, David},
booktitle = {Proceedings of the Hilton Head Solid-Sate Sensor and
Actuator Workshop 2016},
title = {Temperature Sensitivity of Solid-Wave Gyroscopes (Late News)},
month = jun,
year = {2016}
}


#### Abstract:

We analyze the change of angular gain and vibration frequency of solid-wave gyroscopes as a result of geometry perturbations due to thermal expansion. We formulate a temperature sensitivity analysis by assuming a linear dependence of material properties to temperature, and quantify it for common device geometries.

10. A. E. Gencer, E. G. Sirer, R. Van Renesse, and D. Bindel, “Configuring Distributed Computations Using Response Surfaces,” in Proceedings of Middleware 2015, 2015.
Best student paper.
@inproceedings{2015-middleware,
author = {Gencer, Adam Efe and Sirer, Emin Gun and Van Renesse, Robbert and Bindel, David},
title = {Configuring Distributed Computations Using Response Surfaces},
booktitle = {Proceedings of Middleware 2015},
month = dec,
year = {2015},
notable = {Best student paper.},
doi = {10.1145/2814576.2814730}
}


#### Abstract:

Configuring large distributed computations is a challenging task. Efficiently executing distributed computations requires configuration tuning based on careful examination of application and hardware properties. Considering the large number of parameters and impracticality of using trial and error in a production environment, programmers tend to make these decisions based on their experience and rules of thumb. Such configurations can lead to underutilized and costly clusters, and missed deadlines.

In this paper, we present a new methodology for determining desired hardware and software configuration parameters for distributed computations. The key insight behind this methodology is to build a response surface that captures how applications perform under different hardware and software configuration. Such a model can be built through iterated experiments using the real system, or, more efficiently, using a simulator. The resulting model can then generate recommendations for configuration parameters that are likely to yield the desired results even if they have not been tried either in simulation or in real-life. The process can be iterated to refine previous predictions and achieve better results.

We have implemented this methodology in a configuration recommendation system for MapReduce 2.0 applications. Performance measurements show that representative applications achieve up to $5 \times$ performance improvement when they use the recommended configuration parameters compared to the default ones.

11. M. Lee, D. Bindel, and D. Mimno, “Robust Spectral Inference for Joint Stochastic Matrix Factorization,” in Proceedings of NIPS 2015, 2015, pp. 2710–2718.
@inproceedings{2015-nips,
author = {Lee, Moontae and Bindel, David and Mimno, David},
title = {Robust Spectral Inference for
Joint Stochastic Matrix Factorization},
booktitle = {Proceedings of NIPS 2015},
pages = {2710--2718},
month = dec,
year = {2015}
}


#### Abstract:

Spectral inference provides fast algorithms and provable optimality for latent topic analysis. But for real data these algorithms require additional ad-hoc heuristics, and even then often produce unusable results. We explain this poor performance by casting the problem of topic inference in the framework of Joint Stochastic Matrix Factorization (JSMF) and showing that previous methods violate the theoretical conditions necessary for a good solution to exist. We then propose a novel rectification method that learns high quality topics and their interactions even on small, noisy data. This method achieves results comparable to probabilistic techniques in several domains while maintaining scalability and provable optimality.

12. K. He, Y. Sun, D. Bindel, J. Hopcroft, and Y. Li, “Detecting Overlapping Communities from Local Spectral Subspaces,” in Proceedings of ICDM 2015, 2015.
@inproceedings{2015-icdm,
author = {He, Kun and Sun, Yiwei and Bindel, David and Hopcroft, John and Li, Yixuan},
title = {Detecting Overlapping Communities from
Local Spectral Subspaces},
booktitle = {Proceedings of ICDM 2015},
month = nov,
year = {2015},
doi = {10.1109/ICDM.2015.89},
arxiv = {1509.08065}
}


#### Abstract:

Based on the definition of local spectral subspace, we propose a novel approach called LOSP for local overlapping community detection. Using the power method for a few steps, LOSP finds an approximate invariant subspace, which depicts the embedding of the local neighborhood structure around the seeds of interest. LOSP then identifies the local community expanded from the given seeds by seeking a sparse indicator vector in the subspace where the seeds are in its support. We provide a systematic investigation on LOSP, and thoroughly evaluate it on large real world networks across multiple domains. With the prior information of very few seed members, LOSP can detect the remaining members of a target community with high accuracy. Experiments demonstrate that LOSP outperforms the Heat Kernel and PageRank diffusions. Using LOSP as a subroutine, we further address the problem of multiple membership identification, which aims to find all the communities a single vertex belongs to. High F1 scores are achieved in detecting multiple local communities with respect to arbitrary single seed for various large real world networks.

13. W. Xie, D. Bindel, A. Demers, and J. Gehrke, “Edge-Weighted Personalized PageRank: Breaking A Decade-Old Performance Barrier,” in Proceedings of ACM KDD 2015, 2015.
Best student paper award.
@inproceedings{2015-edgeppr,
author = {Xie, Wenlei and Bindel, David and Demers, Alan and Gehrke, Johannes},
booktitle = {Proceedings of ACM KDD 2015},
title = {Edge-Weighted Personalized {PageRank}:
month = aug,
year = {2015},
doi = {10.1145/2783258.2783278},
notable = {Best student paper award.},
code = {https://github.com/wenleix/EdgePPR},
slides = {present/2015-08-kdd-talk_kdd-aug15.pdf},
}


#### Abstract:

Personalized PageRank is a standard tool for finding vertices in a graph that are most relevant to a query or user. To personalize PageRank, one adjusts node weights or edge weights that determine teleport probabilities and transition probabilities in a random surfer model. There are many fast methods to approximate PageRank when the node weights are personalized; however, personalization based on edge weights has been an open problem since the dawn of personalized PageRank over a decade ago. In this paper, we describe the first fast algorithm for computing PageRank on general graphs when the edge weights are personalized. Our method, which is based on model reduction, outperforms existing methods by nearly five orders of magnitude. This huge performance gain over previous work allows us — for the very first time — to solve learning-to-rank problems for edge weight personalization at interactive speeds, a goal that had not previously been achievable for this class of problems.

14. Y. Li, K. He, D. Bindel, and J. Hopcroft, “Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach,” in Proceedings of WWW 2015, 2015.
@inproceedings{2015-www,
author = {Li, Yixuan and He, Kun and Bindel, David and Hopcroft, John},
title = {Uncovering the Small Community Structure in Large Networks:
A Local Spectral Approach},
booktitle = {Proceedings of WWW 2015},
month = may,
year = {2015},
doi = {10.1145/2736277.2741676},
arxiv = {1509.07715}
}


#### Abstract:

Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure when dealing with large networks. A growing body of work has been adopting local expansion methods in order to identify the community from a few exemplary seed members. Very few approaches can systematically demonstrate both high efficiency and effectiveness that significantly stands out amongst the divergent approaches in finding communities.

In this paper, we propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm). Different from PageRank-like diffusion methods, LEMON finds the community by seeking a sparse vector in the span of the local spectra such that the seeds are in its support. We show that LEMON can achieve the highest detection accuracy among state-of-the-art proposals. The running time depends on the size of the community rather than that of the entire graph. The algorithm is easy to implement, and is highly parallelizable.

Moreover, given that networks are not all similar in nature, a comprehensive analysis on how the local expansion approach is suited for uncovering communities in different networks is still lacking. We thoroughly evaluate our approach using both synthetic and real-world datasets across different domains, and analyze the empirical variations when applying our method to inherently different networks in practice. In addition, the heuristics on how the quality and quantity of the seed set would affect the performance are provided.

15. K. Dong and D. Bindel, “Modified Kernel Polynomial Method for Estimating Graph Spectra,” in SIAM Network Science 2015 (poster), 2015.
@inproceedings{2015-siam-ns,
author = {Dong, Kun and Bindel, David},
title = {Modified Kernel Polynomial Method for Estimating Graph Spectra},
booktitle = {SIAM Network Science 2015 (poster)},
month = may,
year = {2015}
}


#### Abstract:

The kernel polynomial method (KPM) is a standard tool in condensed matter physics to estimate the density of states for a quantum system. We use the KPM to instead estimate the eigenvalue densities of the normalized adjacency matrices of “natural” graphs. Because natural graph spectra often include high-multiplicity eigenvalues corresponding to certain motifs in the graph, we introduce a pre-processing phase that counts just these special eigenvalues, leaving the rest of the eigenvalue distribution to be estimated by the standard KPM.

16. E. Yilmaz and D. Bindel, “Effects of imperfections on solid-wave gyroscope dynamics,” in Proceedings of IEEE SENSORS 2013, 2013.
@inproceedings{2013-sensors,
author = {Yilmaz, Erdal and Bindel, David},
title = {Effects of imperfections on solid-wave gyroscope dynamics},
booktitle = {Proceedings of IEEE SENSORS 2013},
month = nov,
year = {2013},
doi = {10.1109/ICSENS.2013.6688462}
}


#### Abstract:

Solid-wave gyroscopes are symmetric resonators that sense rotation by measuring how Coriolis forces perturb a degenerate mode pair. The idealized dynamics of these devices are described by ODE models of two identical oscillators coupled by a perturbation due to rotation. In miniaturized solid-wave gyroscopes, geometric distortions due to imperfect fabrication also perturb the dynamics, and this limits sensing accuracy. In this work, we describe how geometric imperfections affect the dynamics of solid-wave gyroscopes. We also use selection rules both to find qualitative information about what types of geometry perturbations most affect sensor performance and to accelerate computations

17. T. Zou, G. Wang, M. Vaz Salles, D. Bindel, A. Demers, J. Gehrke, and W. White, “Making Time-Stepped Applications Tick in the Cloud,” in Proceedings of the Second ACM Symposium on Cloud Computing (SOCC), 2011.
@inproceedings{2011-socc,
author = {Zou, Tao and Wang, Guozhang and Vaz Salles, Marcos and Bindel, David and Demers, Alan and Gehrke, Johannes and White, Walker},
title = {Making Time-Stepped Applications Tick in the Cloud},
booktitle = {Proceedings of the Second
ACM Symposium on Cloud Computing (SOCC)},
month = oct,
year = {2011},
doi = {10.1145/2038916.2038936}
}


#### Abstract:

Scientists are currently evaluating the cloud as a new platform. Many important scientific applications, however, perform poorly in the cloud. These applications proceed in highly parallel discrete time-steps or “ticks,” using logical synchronization barriers at tick boundaries. We observe that network jitter in the cloud can severely increase the time required for communication in these applications, significantly increasing overall running time.

In this paper, we propose a general parallel framework to process time-stepped applications in the cloud. Our framework exposes a high-level, data-centric programming model which represents application state as tables and dependencies between states as queries over these tables. We design a jitter-tolerant runtime that uses these data dependencies to absorb latency spikes by (1) carefully scheduling computation and (2) replicating data and computation. Our data-driven approach is transparent to the scientist and requires little additional code. Our experiments show that our methods improve performance up to a factor of three for several typical time-stepped applications.

18. D. Bindel, S. Oren, and J. Kleinberg, “How Bad is Forming Your Own Opinion?,” in Proceedings of the 52nd IEEE Symposium on Foundations of Computer Science (FOCS), 2011.
@inproceedings{2011-focs,
author = {Bindel, David and Oren, Sigal and Kleinberg, Jon},
booktitle = {Proceedings of the 52nd IEEE Symposium on
Foundations of Computer Science (FOCS)},
month = oct,
year = {2011},
doi = {10.1109/FOCS.2011.43},
arxiv = {1203.2973}
}


#### Abstract:

A long-standing line of work in economic theory has studied models by which a group of people in a social network, each holding a numerical opinion, can arrive at a shared opinion through repeated averaging with their neighbors in the network. Motivated by the observation that consensus is rarely reached in real opinion dynamics, we study a related sociological model in which individuals’ intrinsic beliefs counterbalance the averaging process and yield a diversity of opinions. By interpreting the repeated averaging as best-response dynamics in an underlying game with natural payoffs, and the limit of the process as an equilibrium, we are able to study the cost of disagreement in these models relative to a social optimum. We provide a tight bound on the cost at equilibrium relative to the optimum, our analysis draws a connection between these agreement models and extremal problems for generalized eigenvalues. We also consider a natural network design problem in this setting, where adding links to the underlying network can reduce the cost of disagreement at equilibrium.

19. C. Bruyns-Maxwell and D. Bindel, “Modal Parameter Tracking for Shape Changing Objects,” in Proceedings of DAFx 2007, 2007.
@inproceedings{2007-sound,
author = {Bruyns-Maxwell, Cynthia and Bindel, David},
title = {Modal Parameter Tracking for Shape Changing Objects},
booktitle = {Proceedings of DAFx 2007},
month = sep,
year = {2007}
}


#### Abstract:

For interactive sound synthesis, we would like to change the shape of a finite element model of an instrument and rapidly hear how the sound changes. Using modal synthesis methods, we would need to compute a new modal decomposition with each change in the geometry, making the analysis too slow for interactive use. However, by using modes computed for one geometry to estimate the frequencies for nearby geometries, we can hear much more quickly how changing the instrument shape changes the sound. In this paper, we describe how to estimate resonant frequencies of an instrument by combining information about the modes of two similar instruments. We also describe the balance between computational speed and accuracy of the computed resonances.

20. J. Demmel, J. Dongarra, B. Parlett, W. Kahan, M. Gu, D. Bindel, Y. Hida, X. Li, O. Marques, J. Riedy, C. Voemel, J. Langou, P. Lusczek, J. Kurzak, A. Butarri, J. Langou, and S. Tomov, “Prospectus for the Next LAPACK and ScaLAPACK Libraries,” in Proceedings of PARA 2006, 2006, pp. 11–23.
@inproceedings{2006-para,
author = {Demmel, James and Dongarra, Jack and Parlett, Beresford and Kahan, William and Gu, Ming and Bindel, David and Hida, Yozo and Li, Xiaoye and Marques, Osni and Riedy, Jason and Voemel, Christof and Langou, Julien and Lusczek, Piotr and Kurzak, Jakub and Butarri, Alfredo and Langou, Julie and Tomov, Stanimire},
title = {Prospectus for the Next {LAPACK} and {ScaLAPACK} Libraries},
booktitle = {Proceedings of PARA 2006},
pages = {11--23},
year = {2006}
}


#### Abstract:

New releases of the widely used LAPACK and ScaLAPACK numerical linear algebra libraries are planned. Based on an on-going user survey (<www.netlib.org/lapack-dev>) and research by many people, we are proposing the following improvements: Faster algorithms, including better numerical methods, memory hierarchy optimizations, parallelism, and automatic performance tuning to accommodate new architectures; More accurate algorithms, including better numerical methods, and use of extra precision; Expanded functionality, including updating and downdating, new eigenproblems, etc. and putting more of LAPACK into ScaLAPACK; Improved ease of use, e.g., via friendlier interfaces in multiple languages. To accomplish these goals we are also relying on better software engineering techniques and contributions from collaborators at many institutions.

21. C. Bruyns and D. Bindel, “Shape Changing Symmetric Objects for Sound Synthesis,” in Proceedings of 121st AES, 2006.
@inproceedings{2006-sound,
author = {Bruyns, Cynthia and Bindel, David},
title = {Shape Changing Symmetric Objects for Sound Synthesis},
booktitle = {Proceedings of 121st AES},
month = oct,
year = {2006}
}


#### Abstract:

In the last decade, many researchers have used modal synthesis for sound generation. Using a modal decomposition, one can convert a large system of coupled differential equations into simple, independent differential equations in one variable. To synthesize sound from the system, one solves these decoupled equations numerically, which is much more efficient than solving the original coupled system. For large systems, such as those obtained from finite-element analysis of a musical instrument, the initial modal decomposition is time-consuming. To design instruments from physical simulation, one would like to be able to compute modes in real-time, so that the geometry, and therefore spectrum, of an instrument can be changed interactively. In this paper, we describe how to quickly compute modes of instruments which have rotational symmetry in order to synthesize sounds of new instruments quickly enough for interactive instrument design.

22. Y. Zhao, Y. Chen, and D. Bindel, “Toward Unbiased End-to-End Network Diagnosis,” in Proceedings of SIGCOMM 2006, 2006, pp. 219–230.
@inproceedings{2006-sigcomm,
author = {Zhao, Yao and Chen, Yan and Bindel, David},
title = {Toward Unbiased End-to-End Network Diagnosis},
booktitle = {Proceedings of SIGCOMM 2006},
pages = {219--230},
year = {2006},
doi = {10.1145/1151659.1159939}
}


#### Abstract:

Internet fault diagnosis is extremely important for end users, overlay network service providers (like Akamai [1]) and even Internet service providers (ISPs). However, because link-level properties cannot be uniquely determined from end-to-end measurements, the accuracy of existing statistical diagnosis approaches is subject to uncertainty from statistical assumptions about the network. In this paper, we propose a novel Least-biased End-to-end Network Diagnosis (in short, LEND) system for inferring link-level properties like loss rate. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. We also design efficient algorithms to find all the MILSes and infer their loss rates for diagnosis. Our LEND system works for any network topology and for both directed and undirected properties, and incrementally adapts to network topology and property changes. It gives highly accurate estimates of the loss rates of MILSes, as indicated by both extensive simulations and Internet experiments. Furthermore, we demonstrate that such diagnosis can be achieved with fine granularity and in near real-time even for reasonably large overlay networks. Finally, LEND can supplement existing statistical inference approaches and provide smooth tradeoff between diagnosis accuracy and granularity.

23. Y. Zhao, Y. Chen, and D. Bindel, “Toward Deterministic Overlay Diagnosis,” in ACM SIGMETRICS/Performance 2006, 2006, pp. 387–388.
@inproceedings{2006-sigmetrics,
author = {Zhao, Yao and Chen, Yan and Bindel, David},
title = {Toward Deterministic Overlay Diagnosis},
booktitle = {ACM SIGMETRICS/Performance 2006},
pages = {387--388},
year = {2006},
doi = {10.1145/1140277.1140333}
}

24. T. Koyama, D. Bindel, W. He, E. Quevy, J. Demmel, S. Govindjee, and R. Howe, “Simulation Tools for Damping in High Frequency Resonators,” in Proceedings of IEEE SENSORS 2005, 2005.
@inproceedings{2005-sensors,
author = {Koyama, Tsuyoshi and Bindel, David and He, Wei and Quevy, Emmanuel and Demmel, James and Govindjee, Sanjay and Howe, Roger},
title = {Simulation Tools for Damping in High Frequency Resonators},
booktitle = {Proceedings of IEEE SENSORS 2005},
month = nov,
year = {2005},
doi = {10.1109/ICSENS.2005.1597708}
}


#### Abstract:

This paper presents the development of HiQLab, a simulation tool to compute the effect of damping in high frequency resonators. Existing simulation tools allow designers to compute resonant frequencies but few tools provide estimates of damping, which is crucial in evaluating the performance of such devices. In the current code, two damping mechanisms: thermoelastic damping and anchor loss, have been implemented. Thermoelastic damping results from irreversible heat flow due to mechanically-driven temperature gradients, while anchor loss occurs when high-frequency mechanical waves radiate away from the resonator and into the substrate. Our finite-element simulation tool discretizes PDE models of both phenomena, and evaluates the quality factor ($Q$), a measure of damping in the system, with specialized eigencomputations and model reduction techniques. The core functions of the tool are written in C++ for performance. Interfaces are in Lua and MATLAB, which give users access to powerful visualization and pre- and postprocessing capabilities.

25. Y. Zhao, Y. Chen, and D. Bindel, “Scalable and Deterministic Overlay Network Diagnosis,” in Proceedings of ACM SIGCOMM 2005 (poster), 2005.
@inproceedings{2005-sigcomm,
author = {Zhao, Yao and Chen, Yan and Bindel, David},
title = {Scalable and Deterministic Overlay Network Diagnosis},
booktitle = {Proceedings of ACM SIGCOMM 2005 (poster)},
year = {2005}
}

26. D. Bindel, J. Demmel, W. Govaerts, and Y. Kuznetsov, “Bifurcation Analysis of Large Equilibrium Systems in MATLAB,” in Proceedings of ICCS 2005, 2005, vol. 3514, pp. 50–57.
@inproceedings{2005-iccs,
author = {Bindel, David and Demmel, James and Govaerts, Willy and Kuznetsov, Yuri},
title = {Bifurcation Analysis of Large Equilibrium Systems in {MATLAB}},
booktitle = {Proceedings of ICCS 2005},
volume = {3514},
series = {Springer LNCS},
pages = {50--57},
month = apr,
year = {2005},
doi = {10.1007/11428831_7}
}


#### Abstract:

The Continuation of Invariant Subspaces (CIS) algorithm produces a smoothly varying basis for an invariant subspace $R(s)$ of a parameter-dependent matrix $A(s)$. In the case when $A(s)$ is the Jacobian matrix for a system that comes from a spatial discretization of a partial differential equation, it will typically be large and sparse. Cl_matcont is a user-friendly MATLAB package for the study of dynamical systems and their bifurcations. We incorporate the CIS algorithm into Cl_matcont to extend its functionality to large scale bifurcation computations via subspace reduction

27. D. Bindel, E. Quevy, T. Koyama, J. Demmel, and R. Howe, “Anchor Loss Simulation in Resonators,” in Proceedings of MEMS 2005, 2005.
@inproceedings{2005-mems,
author = {Bindel, David and Quevy, Emmanuel and Koyama, Tsuyoshi and Demmel, James and Howe, Roger},
title = {Anchor Loss Simulation in Resonators},
booktitle = {Proceedings of MEMS 2005},
month = feb,
year = {2005},
doi = {10.1109/MEMSYS.2005.1453885}
}


#### Abstract:

Surface-micromachined resonators and filters are attractive for many RF applications. While existing simulation tools allow designers to compute resonant frequencies, few tools provide estimates of the damping in these devices. This paper reports on a new tool that allows designers, for the first time, to compute anchor losses in high-frequency resonators and account for sub-surface scatterers. By exercising the tool on a family of radially driven disk resonators, we show that the anchor loss mechanism for this class of devices involves a parasitic mixed-mode bending action that pumps energy into the substrate. Further, using the tool, we predict a large variation in resonator quality depending upon film thickness. Our simulation shows that the source of this variation is a complex radial-to-bending motion interaction, which we visualize with a root-locus diagram. We experimentally verify this predicted sensitivity using poly-SiGe disk resonators having $Q$’s ranging from 200 to 54,000.

28. Y. Chen, D. Bindel, H. Song, and R. Katz, “An Algebraic Approach to Practial and Scalable Overlay Network Monitoring,” in Proceedings of ACM SIGCOMM 2004, 2004.
@inproceedings{2004-sigcomm,
author = {Chen, Yan and Bindel, David and Song, Hanhee and Katz, Randy},
title = {An Algebraic Approach to Practial and Scalable
Overlay Network Monitoring},
booktitle = {Proceedings of ACM SIGCOMM 2004},
year = {2004},
doi = {10.1145/1015467.1015475}
}


#### Abstract:

Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with $n$ end hosts, existing systems either require $O(n^2)$ measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Our earlier extended abstract briefly proposes an algebraic approach that selectively monitors $k$ linearly independent paths that can fully describe all the $O(n^2)$ paths. The loss rates and latency of these $k$ paths can be used to estimate the loss rates and latency of all other paths. Our scheme only assumes knowledge of the underlying IP topology, with links dynamically varying between lossy and normal.In this paper, we improve, implement and extensively evaluate such a monitoring system. We further make the following contributions: i) scalability analysis indicating that for reasonably large n (e.g., 100), the growth of $k$ is bounded as $O(n \log n)$, ii) efficient adaptation algorithms for topology changes, such as the addition or removal of end hosts and routing changes, iii) measurement load balancing schemes, and iv) topology measurement error handling. Both simulation and Internet experiments demonstrate we obtain highly accurate path loss rate estimation while adapting to topology changes within seconds and handling topology errors.

29. D. Bindel, Z. Bai, and J. Demmel, “Model Reduction for RF MEMS Simulation,” in Proceedings of PARA 2004, 2004.
@inproceedings{2004-para,
author = {Bindel, David and Bai, Zhaojun and Demmel, James},
title = {Model Reduction for {RF MEMS} Simulation},
booktitle = {Proceedings of PARA 2004},
month = jun,
year = {2004},
doi = {10.1007/11558958_34}
}


#### Abstract:

Radio-frequency (RF) MEMS resonators, integrated into CMOS chips, are of great interest to engineers planning the next generation of communication systems. Fast simulations are necessary in order to gain insights into the behavior of these devices. In this paper, we discuss two structure-preserving model-reduction techniques and apply them to the frequency-domain analysis of two proposed MEMS resonator designs.

30. Y. Chen, D. Bindel, and R. Katz, “Tomography-Based Overlay Network Monitoring,” in Proceedings of ACM SIGCOMM Internet Measurement Conference (IMC), 2003.
@inproceedings{2003-imc,
author = {Chen, Yan and Bindel, David and Katz, Randy},
title = {Tomography-Based Overlay Network Monitoring},
booktitle = {Proceedings of ACM SIGCOMM Internet
Measurement Conference (IMC)},
year = {2003},
doi = {10.1145/948205.948233}
}


#### Abstract:

Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with $n$ end hosts, existing systems either require $O(n^2)$ measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Unlike other network tomography systems, we characterize end-to-end losses (this extends to any additive metrics, including latency) rather than individual link losses. We find a minimal basis set of $k$ linearly independent paths that can fully describe all the $O(n^2)$ paths. We selectively monitor and measure the loss rates of these paths, then apply them to estimate the loss rates of all other paths. By extensively studying synthetic and real topologies, we find that for reasonably large n (e.g., 100), $k$ is only in the range of $O(n \log n)$. This is explained by the moderately hierarchical nature of Internet routine.Our scheme only assumes the knowledge of underlying IP topology, and any link can become lossy or return to normal. In addition, our technique is tolerant to topology measurement inaccuracies, and is adaptive to topology changes.

31. J. Clark, D. Bindel, W. Kao, E. Zhu, A. Kuo, N. Zhou, J. Nie, J. Demmel, Z. Bai, S. Govindjee, K. S. J. Pister, M. Gu, and A. Agogino, “Addressing the Needs of Complex MEMS Design,” in Proceedings of MEMS 2002, 2002.
@inproceedings{2002-mems,
author = {Clark, Jason and Bindel, David and Kao, Wayne and Zhu, Ernest and Kuo, Andrew and Zhou, Ningning and Nie, Jiawang and Demmel, James and Bai, Zhaojun and Govindjee, Sanjay and Pister, Kristofer S. J. and Gu, Ming and Agogino, Alice},
title = {Addressing the Needs of Complex {MEMS} Design},
booktitle = {Proceedings of MEMS 2002},
month = jan,
year = {2002},
doi = {10.1109/MEMSYS.2002.984240}
}


#### Abstract:

In this paper, we report several advances in the Sugar 2.0 MEMS system simulation package, including reduced-order modeling techniques, simple hierarchical description of complex structures, synthesis tools, a variety of models, and a web-based interface. Examples include the modeling of a torsional micromirror with lateral actuators compared to experiment, and the prototyping of a microrobot.

32. Y. Chen, A. Bargteil, D. Bindel, R. Katz, and J. Kubiatowicz, “Quantifying Network Denial of Service: A Location Service Case Study,” in Proceedings of the International Conference on Information and Communications Security (ICICS), 2001, vol. 2229, pp. 340–351.
@inproceedings{2001-icics,
author = {Chen, Yan and Bargteil, Adam and Bindel, David and Katz, Randy and Kubiatowicz, John},
title = {Quantifying Network Denial of Service:
A Location Service Case Study},
booktitle = {Proceedings of the International Conference on
Information and Communications Security (ICICS)},
volume = {2229},
series = {LNCS},
publisher = {Springer},
pages = {340--351},
month = nov,
year = {2001},
doi = {10.1007/3-540-45600-7_37}
}


#### Abstract:

Network Denial of Service (DoS) attacks are increasing in frequency, severity and sophistication, making it desirable to measure the resilience of systems to DoS attacks. In this paper, we propose a simulation-based methodology and apply it to attacks on object location services such as DNS. Our results allow us to contrast the DoS resilience of three distinct architectures for object location.

33. J. Clark, D. Bindel, N. Zhou, S. Bhave, Z. Bai, J. Demmel, and K. S. J. Pister, “SUGAR: Advancements in a 3D Multi-Domain Simulation Package for MEMS,” in Proceedings of the Microscale Systems: Mechanics and Measurements Symposium, 2001.
@inproceedings{2001-sugar,
author = {Clark, Jason and Bindel, David and Zhou, Ningning and Bhave, Sunil and Bai, Zhaojun and Demmel, James and Pister, Kristofer S. J.},
title = {{SUGAR}: Advancements in a 3D Multi-Domain Simulation Package for {MEMS}},
booktitle = {Proceedings of the Microscale Systems:
Mechanics and Measurements Symposium},
month = jun,
year = {2001}
}


#### Abstract:

Advancements in Sugar include 1) parameterizable netlists, 2) nonlinear frequency response analysis, 3) subnets, 4) improved MNA, 5) reduced order modeling, and 6) a more accurate nonlinear beam model. Examples of these features include the simulation of a two-axis mirror with over 10,000 degrees of freedom, the reduced order modeling applied of an electrostatic gap actuator, the parameterized deflection space of a thermal actuator and serpentine flexure, and the nonlinear response of a fixed-fixed beam.

34. Z. Bai, D. Bindel, J. Clark, N. Zhou, J. Demmel, and K. S. J. Pister, “New Numerical Techniques and Tools in SUGAR for 3D MEMS Simulation,” in Proceedings of the Fourth International Conference on Modeling and Simulation of Microsystems (MSM), 2001.
@inproceedings{2001-msm,
author = {Bai, Zhaojun and Bindel, David and Clark, Jason and Zhou, Ningning and Demmel, James and Pister, Kristofer S. J.},
title = {New Numerical Techniques and Tools in {SUGAR} for {3D MEMS} Simulation},
booktitle = {Proceedings of the Fourth International Conference on
Modeling and Simulation of Microsystems (MSM)},
month = mar,
year = {2001}
}


#### Abstract:

SUGAR is a nodal analysis package for 3D MEMS simulation that owes its heritage and its name to the SPICE family of circuit simulation. SUGAR has undergone the stage of proof-of-concept which showed that nodal analysis was in fact just as accurate and much faster than finite element simulation on many MEMS problems. The upcoming major release of SUGAR is version 2.0, which includes a number of new features, such as 3D beam and gap elements, thermal expansion, linearly and rotationally accelerating frames, and user-defined models.

35. J. Kubiatowicz, D. Bindel, Y. Chen, S. Czerwinski, P. Eaton, D. Geels, R. Gummadi, S. Rhea, H. Weatherspoon, W. Weimer, C. Wells, and B. Zhao, “OceanStore: An Architecture for Global-Scale Persistent Storage,” in Procedings of the Ninth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2000), 2000.
ASPLOS Most Influential Paper Award 2018
@inproceedings{2000-asplos,
author = {Kubiatowicz, John and Bindel, David and Chen, Yan and Czerwinski, Steven and Eaton, Patrick and Geels, Dennis and Gummadi, Ramakrishna and Rhea, Sean and Weatherspoon, Hakim and Weimer, Westley and Wells, Chris and Zhao, Ben},
title = {{OceanStore}: An Architecture for Global-Scale Persistent Storage},
booktitle = {Procedings of the Ninth International Conference on
Architectural Support for Programming Languages and
Operating Systems (ASPLOS 2000)},
month = nov,
year = {2000},
doi = {10.1145/356989.357007},
notable = {ASPLOS Most Influential Paper Award 2018}
}


#### Abstract:

OceanStore is a utility infrastructure designed to span the globe and provide continuous access to persistent information. Since this infrastructure is comprised of untrusted servers, data is protected through redundancy and cryptographic techniques. To improve performance, data is allowed to be cached anywhere, anytime. Additionally, monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data. A prototype implementation is currently under development.

36. J. Clark, N. Zhou, D. Bindel, L. Schenato, W. Wu, J. Demmel, and K. S. J. Pister, “3D MEMS Simulation Modeling Using Modified Nodal Analysis,” in Proceedings of the Microscale Systems: Mechanics and Measurements Symposium, 2000, pp. 68–75.
@inproceedings{2000-mems,
author = {Clark, Jason and Zhou, Ningning and Bindel, David and Schenato, Luca and Wu, W. and Demmel, James and Pister, Kristofer S. J.},
title = {{3D} {MEMS} Simulation Modeling Using Modified Nodal Analysis},
booktitle = {Proceedings of the Microscale Systems:
Mechanics and Measurements Symposium},
pages = {68--75},
month = jun,
year = {2000}
}


#### Abstract:

The modeling, simulation, and experimental verification of several MEMS devices are presented. Simulated results include 3D mode analysis, residual stress effects, thermal expansion, nonlinear deflections, time-varying electrostatic forces, process sensitivities, induced currents, and the transient performance in accelerated reference frames. To simulate the performance of these MEMS devices a modified nodal analysis approach is used to formulate a system of ODEs that is solved by static, steady state, and transient solvers.

### Reports

1. D. Eriksson, D. Bindel, and C. Shoemaker, “pySOT and POAP: An event-driven asynchronous framework for surrogate optimization,” Aug. 2019. Submitted to ACM Transactions on Mathematical Software.
@techreport{2019-pysot,
author = {Eriksson, David and Bindel, David and Shoemaker, Christine},
title = {{pySOT} and {POAP}: An event-driven asynchronous framework for surrogate optimization},
month = aug,
year = {2019},
arxiv = {1908.00420},
status = {unrefereed},
submit = {Submitted to ACM Transactions on Mathematical Software.}
}

2. A. Hood and D. Bindel, “Pseudospectral bounds on transient growth for higher order and constant delay differential equations,” Nov. 2016. Submitted to SIAM Journal on Matrix Analysis and Applictions.
@techreport{2016-transient-tr,
author = {Hood, Amanda and Bindel, David},
title = {Pseudospectral bounds on transient growth for higher order and constant delay differential equations},
month = nov,
year = {2016},
arxiv = {1611.05130},
status = {unrefereed},
submit = {Submitted to SIAM Journal on Matrix Analysis and Applictions.}
}

3. C. Ponce and D. Bindel, “FLiER: Practical Topology Update Detection Using Sparse PMUs,” Jul. 2016. Accepted by IEEE Transactions on Power Systems.
@techreport{2016-flier-tr,
author = {Ponce, Colin and Bindel, David},
title = {{FLiER}: Practical Topology Update Detection Using Sparse {PMU}s},
month = jul,
year = {2016},
arxiv = {1409.6644},
code = {https://github.com/cponce512/FLiER_Test_Suite/},
status = {unrefereed},
submit = {Accepted by IEEE Transactions on Power Systems.}
}


#### Abstract:

In this paper, we present a Fingerprint Linear Estimation Routine (FLiER) to identify topology changes in power networks using readings from sparsely-deployed phasor measurement units (PMUs). When a power line, load, or generator trips in a network, or when a substation is reconfigured, the event leaves a unique “voltage fingerprint” of bus voltage changes that we can identify using only the portion of the network directly observed by the PMUs. The naive brute-force approach to identify a failed line from such voltage fingerprints, though simple and accurate, is slow. We derive an approximate algorithm based on a local linearization and a novel filtering approach that is faster and only slightly less accurate. We present experimental results using the IEEE 57-bus, IEEE 118-bus, and Polish 1999-2000 winter peak networks.

4. J. Chadwick and D. Bindel, “An Efficient Solver for Sparse Linear Systems Based on Rank-Structured Cholesky Factorization,” Jul. 2015.
@techreport{2015-rsc,
author = {Chadwick, Jeffrey and Bindel, David},
title = {An Efficient Solver for Sparse Linear Systems Based on
Rank-Structured {Cholesky} Factorization},
month = jul,
year = {2015},
arxiv = {1507.05593},
status = {unrefereed}
}


#### Abstract:

Direct factorization methods for the solution of large, sparse linear systems that arise from PDE discretizations are robust, but typically show poor time and memory scalability for large systems. In this paper, we describe an efficient sparse, rank-structured Cholesky algorithm for solution of the positive definite linear system Ax=b when A comes from a discretized partial-differential equation. Our approach combines the efficient memory access patterns of conventional supernodal Cholesky algorithms with the memory efficiency of rank-structured direct solvers. For several test problems arising from PDE discretizations, our method takes less memory than standard sparse Cholesky solvers and less wall-clock time than standard preconditioned iterations.

5. D. Bindel and A. Hood, “Localization Theorems for Nonlinear Eigenvalues,” Aug. 2013. Appeared in SIAM Journal on Matrix Analysis and Applications in 2013.
@techreport{2013-nep,
author = {Bindel, David and Hood, Amanda},
title = {Localization Theorems for Nonlinear Eigenvalues},
month = aug,
year = {2013},
arxiv = {1303.4668},
status = {unrefereed},
submit = {Appeared in SIAM Journal on Matrix Analysis
and Applications in 2013.}
}


#### Abstract:

Let $T : \Omega \rightarrow {\Bbb C}^{n \times n}$ be a matrix-valued function that is analytic on some simply-connected domain $\Omega \subset {\Bbb C}$. A point $\lambda \in \Omega$ is an eigenvalue if the matrix $T(\lambda)$ is singular. In this paper, we describe new localization results for nonlinear eigenvalue problems that generalize Gershgorin’s theorem, pseudospectral inclusion theorems, and the Bauer-Fike theorem. We use our results to analyze three nonlinear eigenvalue problems: an example from delay differential equations, a problem due to Hadeler, and a quantum resonance computation.

6. W. He, D. Bindel, and S. Govindjee, “Topology Optimization in Micromechanical Resonator Design,” Structural Engineering Mechanics and Materials, Department of Civil and Environmental Engineering, University of California, Berkeley, UCB/SEMM-2009/04, Dec. 2009. Appeared in Optimization and Engineering in 2012
@techreport{2009-topology,
author = {He, Wei and Bindel, David and Govindjee, Sanjay},
title = {Topology Optimization in Micromechanical Resonator Design},
number = {UCB/SEMM-2009/04},
institution = {Structural Engineering Mechanics and Materials,
Department of Civil and Environmental Engineering,
University of California, Berkeley},
month = dec,
year = {2009},
status = {unrefereed},
submit = {Appeared in Optimization and Engineering in 2012}
}


#### Abstract:

A topology optimization problem in microelectromechanical resonator design is addressed in this paper. The design goal is to control the first several eigen-frequencies of a microelectromechanical resonator using topology optimization in order to improve the resonator’s quality of resonance. The design variable is the distribution of mass in a constrained domain which we model via (1) the Simple Isotropic Material with Penalization Model and (2) the Peak Function Model. The overall optimization problem is solved using the Method of Moving Asymptotes and a Genetic Algorithm combined with a local gradient method. A numerical example is presented to highlight the features of the methods in more detail. The advantages and disadvantages of each method are discussed.

7. D. Bindel, “Structured and Parameter-Dependent Eigensolvers for Simulation-Based Design of Resonant MEMS,” PhD thesis, University of California, Berkeley, 2006. Appears as Tech Report EECS-2006-109.
2008 Householder Award (best NLA thesis over three years)
@phdthesis{2006-dissertation,
author = {Bindel, David},
title = {Structured and Parameter-Dependent Eigensolvers for
Simulation-Based Design of Resonant {MEMS}},
school = {University of California, Berkeley},
month = aug,
year = {2006},
status = {unrefereed},
submit = {Appears as Tech Report EECS-2006-109.},
notable = {2008 Householder Award (best NLA thesis over three years)}
}


#### Abstract:

This dissertation is about computational tools to aid in the design of resonant Micro-Electro-Mechanical Systems (MEMS), tiny vibrating devices built by processes like those used to make integrated circuits. Vibrating MEMS are used in accelerometers and gyroscopes, in sensors to detect chemicals and to measure pressure, and in communication devices such as cell phones. MEMS engineers can use computer simulations to design devices using fewer costly and time-consuming prototype tests, but these simulations are only as useful as the models on which they are built. In this work, we contribute new mathematical models, numerical methods, and software tools to simulate resonant MEMS, and apply these tools to analyze specific devices. We describe physical models of damped vibrations of MEMS, including anchor loss and thermoelastic effects which are widely recognized as important, but not modeled in generality by existing tools. Though the resulting systems of equations are large and non-Hermitian, and depend nonlinearly on frequency, we use the equation structure to develop efficient structured Krylov subspace projection methods for computing free vibrations and reduced-order models. We also provide efficient continuation methods for re-computing eigendecompositions under changes to design parameters or operating conditions. Our models and analysis methods are integrated into HiQLab, a new finite element tool with a particularly flexible architecture which we have designed. Using HiQLab, we simulate example resonator designs, and compare our results to laboratory measurements. Our simulations reveal a previously-unknown mode interference phenomenon, subsequently observed in experiments, which dramatically affects the amount of damping near certain critical values of geometric parameters.

8. D. Bindel and J. Demmel, “Continuation of Invariant Subspaces for Large Bifurcation Problems,” UC Berkeley Computer Science Division, EECS-2006-13, Feb. 2006. Appeared in SIAM Journal on Scientific Computing in 2008.
@techreport{2006-cis,
author = {Bindel, David and Demmel, James},
title = {Continuation of Invariant Subspaces for Large Bifurcation Problems},
number = {EECS-2006-13},
institution = {UC Berkeley Computer Science Division},
month = feb,
year = {2006},
status = {unrefereed},
submit = {Appeared in SIAM Journal on Scientific Computing in 2008.}
}


#### Abstract:

We summarize an algorithm for computing a smooth orthonormal basis for an invariant subspace of a parameter-dependent matrix, and describe how to extend it for numerical bifurcation analysis. We adapt the continued subspace to track behavior relevant to bifurcations, and use projection methods to deal with large problems. To test our ideas, we have integrated our code into MATCONT, a program for numerical continuation and bifurcation analysis.

9. D. Bindel, S. Chandresekaran, J. Demmel, D. Garmire, and M. Gu, “A Fast and Stable Nonsymmetric Eigensolver for Certain Structured Matrices,” May 2005.
@techreport{2005-compan,
author = {Bindel, David and Chandresekaran, Shivkumar and Demmel, James and Garmire, David and Gu, Ming},
title = {A Fast and Stable Nonsymmetric Eigensolver for
Certain Structured Matrices},
month = may,
year = {2005},
status = {unrefereed}
}


#### Abstract:

We consider the set of matrices which differ from symmetric, skew symmetric, or unitary by only a low rank modification. This class of matrices includes companion matrices, arrow matrices, and matrices corresponding to mechanical oscillators with localized damping. We show that Hessenberg and Schur forms for such matrices have semi-separable structure, and we use this fact to construct a backward stable eigensolver for such matrices which requires $O(n)$ space and runs in $O(n^2)$ time. We evaluate the performance and accuracy of our approach for several test problems, including the computation of all the roots of a polynomial or a matrix polynomial.

10. D. Bindel and S. Govindjee, “Elastic PMLs for Resonator Anchor Loss Simulation,” Structural Engineering Mechanics and Materials, Department of Civil and Environmental Engineering, University of California, Berkeley, UCB/SEMM-2005/01, Feb. 2005. Appeared in International Journal on Numerical Methods in Engineering in 2005.
@techreport{2005-pml-tr,
author = {Bindel, David and Govindjee, Sanjay},
title = {Elastic {PML}s for Resonator Anchor Loss Simulation},
number = {UCB/SEMM-2005/01},
institution = {Structural Engineering Mechanics and Materials,
Department of Civil and Environmental Engineering,
University of California, Berkeley},
month = feb,
year = {2005},
status = {unrefereed},
submit = {Appeared in International Journal on Numerical Methods in Engineering in 2005.}
}


#### Abstract:

Electromechanical resonators and filters, such as quartz, ceramic, and surface-acoustic wave devices, are important signal-processing elements in communication systems. Over the past decade, there has been substantial progress in developing new types of miniaturized electromechanical resonators using microfabrication processes. For these micro-resonators to be viable they must have high and predictable quality factors ($Q$). Depending on scale and geometry, the energy losses that lower $Q$ may come from material damping, thermoelastic damping, air damping, or radiation of elastic waves from an anchor. Of these factors, anchor losses are the least understood because such losses are due to a complex radiation phenomena in a semi-infinite elastic half-space. Here, we describe how anchor losses can be accurately computed using an absorbing boundary based on a perfectly matched layer (PML) which absorbs incoming waves over a wide frequency range for any non-zero angle of incidence. We exploit the interpretation of the PML as a complex-valued change of coordinates to illustrate how one can come to a simpler finite element implementation than was given in its original presentations. We also examine the convergence and accuracy of the method, and give guidelines for how to choose the parameters effectively. As an example application, we compute the anchor loss in a micro disk resonator and compare it to experimental data. Our analysis illustrates a surprising mode-mixing phenomenon which can substantially affect the quality of resonance.

11. Y. Chen, D. Bindel, H. Song, and R. Katz, “Tomography-Based Overlay Network Monitoring,” UC Berkeley Computer Science Division, CSD-03-1252, Jun. 2003. Appeared in SIGCOMM IMC 2003
@techreport{2003-tomography-tr,
author = {Chen, Yan and Bindel, David and Song, Hanhee and Katz, Randy},
title = {Tomography-Based Overlay Network Monitoring},
number = {CSD-03-1252},
institution = {UC Berkeley Computer Science Division},
month = jun,
year = {2003},
status = {unrefereed},
submit = {Appeared in SIGCOMM IMC 2003}
}


#### Abstract:

Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with $n$ end hosts, existing systems either require $O(n^2)$ measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Unlike other network tomography systems, we characterize end-to-end losses (this extends to any additive metrics, including latency) rather than individual link losses. We find a minimal basis set of $k$ linearly independent paths that can fully describe all the $O(n^2)$ paths. We selectively monitor and measure the loss rates of these paths, then apply them to estimate the loss rates of all other paths. By extensively studying synthetic and real topologies, we find that for reasonably large n (e.g., 100), $k$ is only in the range of $O(n \log n)$. This is explained by the moderately hierarchical nature of Internet routine.Our scheme only assumes the knowledge of underlying IP topology, and any link can become lossy or return to normal. In addition, our technique is tolerant to topology measurement inaccuracies, and is adaptive to topology changes.

12. D. Bindel, J. Demmel, W. Kahan, and O. Marques, “On Computing Givens Rotations Reliable and Efficiently,” LAPACK Working Notes, 128, Jan. 2001. Appeared in ACM Transactions on Mathematical Software in 2002.
@techreport{2001-givens,
author = {Bindel, David and Demmel, James and Kahan, William and Marques, Osni},
title = {On Computing {Givens} Rotations Reliable and Efficiently},
institution = {LAPACK Working Notes},
number = {128},
month = jan,
year = {2001},
status = {unrefereed},
submit = {Appeared in ACM Transactions on Mathematical Software in 2002.}
}


#### Abstract:

We consider the efficient and accurate computation of Givens rotations. When $f$ and $g$ are positive real numbers, this simply amounts to computing the values of $c = f/\sqrt{f^2 + g^2}$, $s = g/\sqrt{f^2 + g^2}$, and $r = \sqrt{f^2 + g^2}$. This apparently trivial computation merits closer consideration for the following three reasons. First, while the definitions of $c$, $s$ and $r$ seem obvious in the case of two nonnegative arguments $f$ and $g$, there is enough freedom of choice when one or more of $f$ and $g$ are negative, zero or complex that LAPACK auxiliary routines SLARTG, CLARTG, SLARGV and CLARGV can compute rather different values of $c$, $s$ and $r$ for mathematically identical values of $f$ and $g$. To eliminate this unnecessary ambiguity, the BLAS Technical Forum chose a single consistent definition of Givens rotations that we will justify here. Second, computing accurate values of $c$, $s$ and $r$ as efficiently as possible and reliably despite over/underflow is surprisingly complicated. For complex Givens rotations, the most efficient formulas require only one real square root and one real divide (as well as several much cheaper additions and multiplications), but a reliable implementation using only working precision has a number of cases. On a Sun Ultra-10, the new implementation is slightly faster than the previous LAPACK implementation in the most common case, and 2.7 to 4.6 times faster than the corresponding vendor, reference or ATLAS routines. It is also more reliable; all previous codes occasionally suffer from large inaccuracies due to over/underflow. For real Givens rotations, there are also improvements in speed and accuracy, though not as striking. Third, the design process that led to this reliable implementation is quite systematic, and could be applied to the design of similarly reliable subroutines.

13. J. Kubiatowicz, D. Bindel, Y. Chen, S. Czerwinski, P. Eaton, D. Geels, R. Gummadi, S. Rhea, H. Weatherspoon, W. Weimer, C. Wells, and B. Zhao, “OceanStore: An Architecture for Global-Scale Persistent Storage,” UC Berkeley Computer Science Division, CSD-00-1102, Mar. 2000. Appeared in ASPLOS 2000.
@techreport{2000-oceanstore-tr,
author = {Kubiatowicz, John and Bindel, David and Chen, Yan and Czerwinski, Steven and Eaton, Patrick and Geels, Dennis and Gummadi, Ramakrishna and Rhea, Sean and Weatherspoon, Hakim and Weimer, Westley and Wells, Chris and Zhao, Ben},
title = {{OceanStore}: An Architecture for Global-Scale Persistent Storage},
number = {CSD-00-1102},
institution = {UC Berkeley Computer Science Division},
month = mar,
year = {2000},
status = {unrefereed},
submit = {Appeared in ASPLOS 2000.}
}


#### Abstract:

OceanStore is a utility infrastructure designed to span the globe and provide continuous access to persistent information. Since this infrastructure is comprised of untrusted servers, data is protected through redundancy and cryptographic techniques. To improve performance, data is allowed to be cached anywhere, anytime. Additionally, monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data. A prototype implementation is currently under development.

## External Talks

### Colloquium and Seminar Talks

1. Numerical Methods for Data Science: Spectral Network Analysis, Part II
2019 Jun • Special lecture series at University of Chicago
2. Numerical Methods for Data Science: Spectral Network Analysis, Part I
2019 Jun • Special lecture series at University of Chicago
3. Numerical Methods for Data Science: Kernel Methods, Part II
2019 Jun • Special lecture series at University of Chicago
4. Numerical Methods for Data Science: Kernel Methods, Part I
2019 Jun • Special lecture series at University of Chicago
5. Numerical Methods for Data Science: Latent Factor Models, Part II
2019 Jun • Special lecture series at University of Chicago
6. Numerical Methods for Data Science: Latent Factor Models, Part I
2019 Jun • Special lecture series at University of Chicago
7. Understanding Graphs through Spectral Densities
2019 May • CS Colloquium, William and Mary
colloquium
8. Understanding Graphs through Spectral Densities
2019 May • Math Colloquium, Virginia Tech
colloquium
9. Numerical Methods for Data Science: Spectral Network Analysis, Part II
2019 May • Special lecture series at University of Maryland, College Park
10. Numerical Methods for Data Science: Spectral Network Analysis, Part I
2019 Apr • Special lecture series at University of Maryland, College Park
11. Numerical Methods for Data Science: Kernel Methods, Part II
2019 Apr • Special lecture series at University of Maryland, College Park
12. Numerical Methods for Data Science: Kernel Methods, Part I
2019 Apr • Special lecture series at University of Maryland, College Park
13. Numerical Methods for Data Science: Latent Factor Models, Part II
2019 Apr • Special lecture series at University of Maryland, College Park
14. Numerical Methods for Data Science: Latent Factor Models, Part I
2019 Apr • Special lecture series at University of Maryland, College Park
15. Stochastic Linear Algebra for Scalable Gaussian Processes
2019 Jan • Applied Mathematics Colloquium, Illinois Institute of Technology
colloquium
16. Stochastic Linear Algebra for Scalable Gaussian Processes
2019 Jan • CAM Colloquium, University of Chicago
colloquium
17. Stochastic Linear Algebra for Scalable Gaussian Processes
2018 Oct • IEMS Seminar, Northwestern University
18. Understanding Graphs through Spectral Densities
2018 Jun • Seminar at Huazhong University of Science and Tech
19. Understanding Graphs through Spectral Densities
2018 Jun • ZY-INS Colloquium, Shanghai Jiao Tong University
colloquium
20. Model Reduction for Edge-Weighted Personalized PageRank
2017 Feb • Berkeley CS Colloquium
colloquium
21. Nonlinear Eigenvalue Problems: Theory and Applications
2017 Jan • Berkeley Applied Math Seminar
22. Model Reduction for Edge-Weighted Personalized PageRank
2016 Dec • NYU Numerical Analysis and Scientific Computing Seminar
23. Fast Fingerprints for Power System Events
2016 Oct • Argonne National Lab
24. Fast Fingerprints for Power System Events
2016 Aug • Lawrence Berkeley Lab
25. Model Reduction for Edge-Weighted Personalized PageRank
2016 Apr • Purdue Computational and Applied Mathematics Seminar
26. Model Reduction for Edge-Weighted Personalized PageRank
2016 Mar • Hong Kong Baptist University
27. Nonlinear Eigenvalue Problems: Theory and Applications
2016 Mar • University of Arizona Math Colloquium
colloquium
28. Model Reduction for Edge-Weighted Personalized PageRank
2015 Oct • Stanford LA/Opt Seminar
29. Model Reduction for Edge-Weighted Personalized PageRank
2015 Oct • Berkeley Matrix Computations Seminar
30. Music of the Microspheres
2014 Apr • Oxford University NA Seminar
31. Music of the Microspheres
2014 Jan • UMCP NA Seminar
32. Music of the Microspheres
2013 Nov • Seminar at UTRC
33. Music of the Microspheres
2013 Oct • Tufts/Schlumberger Scientific Computing Seminar
34. Computer Aided Design of Micro-Electro-Mechanical Systems
2013 Apr • Civil Engineering Seminar at Duke
35. Communities, Spectral Clustering, and Random Walks
2011 Nov • University of Chicago Scientific Computing Seminar
36. Applications and Analysis of Nonlinear Eigenvalue Problems
2009 Nov • Simon Fraser University NA Seminar
37. Computer-Aided Design for Micro-Electro-Mechanical Systems
2008 Nov • Stanford LA/Opt Seminar
38. Computer-Aided Design for Micro-Electro-Mechanical Systems
2008 Nov • MIT Applied Math Colloquium
colloquium
39. Bounds and Error Estimates for Nonlinear Eigenvalue Problems
2008 Oct • Berkeley Applied Math Seminar
40. Computer-Aided Design for Micro-Electro-Mechanical Systems
2008 Jan • Rice University Applied Math Colloquium
colloquium
41. Computer-Aided Design for Micro-Electro-Mechanical Systems
2007 Nov • Temple University NA Seminar
42. Computer-Aided Design for Micro-Electro-Mechanical Systems
2007 Nov • Cornell CS Colloquium
colloquium
43. Computer-Aided Design for Micro-Electro-Mechanical Systems
2007 Mar • Columbia Applied Math Colloquium
colloquium
44. Spectral Inclusion Regions for Bifurcation Analysis
2006 Aug • Stanford NA Seminar
45. Computer-Aided Design for Micro-Electro-Mechanical Systems
2006 Apr • CU Boulder CS Colloquium
colloquium
46. Computer-Aided Design for Micro-Electro-Mechanical Systems
2006 Mar • UC Davis CS Colloquium
colloquium
47. Computer-Aided Design for Micro-Electro-Mechanical Systems
2006 Feb • Caltech CS Colloquium
colloquium
48. Computer-Aided Design of MEMS
2004 Nov • NYU Numerical Analysis Seminar
49. SUGAR: A MEMS Simulation Program
2002 Jun • Sun Microsystems

### Workshop and Conference Talks

1. Dynamics via Nonlinear Pseudospectra
2019 Jul • ICIAM 2019
minisymposium invited
2. Understanding Graphs through Spectral Densities
2019 Jul • ICIAM 2019
minisymposium invited
3. Understanding Graphs through Spectral Densities
2019 Jul • SIAG-LA lecture at ILAS 2019
invited plenary
4. Global, Robust, Multi-Objective Optimization of Stellarators
2019 Mar • Simons Collaboration on Hidden Symmetries and Fusion Energy Annual Meeting
invited
5. Nonlinear Eigenvalue Localization for Damping Bounds
2019 Feb • SIAM Computational Science and Engineering 2019, Spokane
minisymposium invited
6. LA Support for Scalable Kernel Methods
2018 Sep • Workshop on Approximation Theory and Machine Learning, Purdue
invited
7. Understanding Graphs through Spectral Densities
2018 Jul • SIAM Network Science, Portland
invited plenary
8. Stochastic LA for Scaling GPs
2018 Jun • International Workshop on Computational Math, Suzhou
invited
9. Multi-Objective Stochastic Optimization of Magnetic Fields
2018 Apr • Simons Foundation Proposal Presentation
invited
10. Scalable Algorithms for Kernel-Based Surrogates in Prediction and Optimization
2017 Nov • Workshop on Surrogate Models and Coarsening Techniques, UCLA IPAM
invited
11. Dynamics via Nonlinear Pseudospectra
2017 Jul • Foundations of Computational Math 2017
minisymposium invited
12. Stochastic estimators in Gaussian process kernel learning
2017 Jun • Householder Symposium
invited poster
13. Celebrating Charlie
2016 Jul • SIAM Annual Meeting, Boston
minisymposium organizer
14. Density of States for Graph Analysis
2016 Jul • SIAM Annual Meeting, Boston
poster
15. An Efficient Solver for Sparse Linear Systems based on Rank-Structured Cholesky Factorization
2016 Mar • TSIMF Workshop on Structured Matrix Computations with Applications
invited
16. Grumbles of a Numerical Analyst
2015 Dec • Dagstuhl Seminar on Approximate and Probabilistic Computing
invited
17. Localizing Nonlinear Eigenvalue Problems: Theory and Applications
2015 Oct • SIAM LA 2015 (Prize Lecture)
invited plenary
18. RBF Response Surfaces with Inequality Constraints
2015 Mar • SIAM CSE
minisymposium organizer
19. FLiER: Practical Topology Error Correction Using Sparse PMUs
2015 Feb • ARPAe Innovation Summit, National Harbor, MD
poster
20. Music of the Microspheres
2014 Jun • Householder Symposium
invited plenary
21. Eigenvalue Localization and Applications
2014 Apr • NEP14 Workshop
invited
22. An Efficient Solver for Sparse Linear Systems based on Rank-Structured Cholesky Factorization
2013 Jul • Workshop on Forty Years of Nested Dissection, University of Waterloo
23. Some perturbation theorems for nonlinear eigenvalue problems
2013 Jan • Workshop on Dissipative Spectral Theory, Cardiff
invited
24. Computer Aided Design of Micro-Electro-Mechanical Systems
2012 Dec • SCMS Workshop on Recent Advances in Scientific Computing, Fudan University
invited
25. Computer Aided Design of Micro-Electro-Mechanical Systems
2012 Dec • FIST Workshop at Shanghai Tech
invited
26. From Networks to Numerical Linear Algebra
2012 Oct • NYCAM
organizer
27. Numerical Analysis of Resonances
2012 Sep • Weyl at 100 Workshop (Fields Institute)
invited
28. AxFEM: Micro-Gyro Simulation and Modeling
2012 Jul • DARPA MRIG Program Meeting
29. Analyzing Resonances via Nonlinear Eigenvalues
2011 Jul • ICIAM
minisymposium invited
30. Matrix Factorizations for Computer Network Tomography
2011 Jul • Householder Symposium
invited plenary
31. Integrating Multiphysics and Multiscale Modeling Environment Together
2011 May • DSRC Meeting, Alexandria
invited
32. Structure-Preserving Model Reduction for MEMS
2011 Mar • SIAM CSE Meeting
minisymposium invited
33. Resonances: Interpretation, Computation, and Perturbation
2010 Jul • Workshop in honor of Pete Stewart at UT Austin
invited
34. Structure-Preserving Model Reduction for MEMS Modeling
2010 Jul • SIAM Annual Meeting
minisymposium invited
35. Finite Element Software for Bone Deformation and Failure
2010 Apr • BTHSCA1 Workshop, UCLA IPAM
invited
36. Bounds and Error Estimates for Resonance Problems
2009 Jul • SIAM Annual Meeting
minisymposium invited
37. Numerical Methods for Resonance Calculations
2009 Jul • MSRI Workshop on Resonances
invited
38. Numerical Methods for Resonance Calculations
2008 Oct • BIRS Resonance Workshop
invited
39. Computer-Aided Design for Micro-Electro-Mechanical Systems
2008 Jun • Householder Symposium (Householder Award Talk)
invited plenary
40. Error Bounds and Error Estimates for Nonlinear Eigenvalue Problems
2008 Jun • Householder Symposium
minisymposium invited
41. Structure Preserving Model Reduction for Damped Resonant MEMS
2007 Jul • USNCCM
minisymposium invited
42. Continuation of Sparse Eigendecompositions
2007 Feb • SIAM CSE
minisymposium organizer
43. Model Reduction and Mode Computation for Damped Resonant MEMS
2007 Feb • SIAM CSE
minisymposium invited
44. Modeling Resonant Microsystems
2006 May • Abel Symposium
invited
45. Elastic PMLs for Resonator Anchor Loss Simulation
2005 Jul • US National Conference on Computational Mechanics
minisymposium invited
46. Eigenproblems in Resonant MEMS Design
2005 Jul • SIAM Annual Meeting
minisymposium invited
47. Continuation of Invariant Subspaces of Sparse Parameter-Dependent Matrices
2005 May • Householder Symposium
invited plenary
48. Fast Hessenberg QR Iteration for Companion Matrices
2004 Jul • SIAM Annual Meeting
minisymposium invited
49. Reduced Order Models in Microsystems and RF MEMS
2004 Jun • PARA 2004
minisymposium invited
50. Simulating RF MEMS
2004 Mar • Bay Area Scientific Computing Day
invited
51. Fast QR Iteration for Companion Matrices
2003 Jul • SIAM Linear Algebra Meeting
minisymposium invited
52. SUGAR: A MEMS Simulation Program
2002 Apr • MSM 2002
tutorial

## Software

 PySOT Python Surrogate Optimization Toolbox POAP Python for Optimization with Asynchronous Plumbing GraphDoS Analyzing graphs based on global and local Density of States RSC Rank-structured Cholesky for fast PDE solves AxFEM Fast simulation of near-axisymmetric resonant MEMS MatScat 1D scattering and resonance computation in MATLAB BoneFEA Finite element analysis of human bones MATFEAP MATLAB interfaces to the FEAP finite element code MWrap A gateway language for mixing MATLAB and C Matexpr A MATLAB-like mini-language for fast inner loops in C HiQLab Finite element analysis of damping in high-frequency MEMS SUGAR System-level simulation software for MEMS CLAPACK 3.0 C interfaces to the LAPACK linear algebra library

## Teaching

 Summer19: SJTU CS 259 Numerical Methods for Data Science Summer18: SJTU CS 259 Numerical Methods for Data Science S18: CS 6241 Numerical Methods for Data Science F17: CS 5220 Applications of Parallel Computers S17: CS 4220/5223 / MATH 4260 Numerical Analysis: Linear and Nonlinear Problems F16: CS 6210 Matrix Computations S16: CS 4220/5223 / MATH 4260 Numerical Analysis: Linear and Nonlinear Problems F15: CS 5220 Applications of Parallel Computers S15: CS 4220/5223 / MATH 4260 Numerical Analysis: Linear and Nonlinear Problems S14: CS 5220 Applications of Parallel Computers F13: CS 6210 Matrix Computations F12: CS 6210 Matrix Computations S12: CS 3220 Introduction to Scientific Computing F11: CS 5220 Applications of Parallel Computers S11: CS 3220 Introduction to Scientific Computing S10: CS 5220 Applications of Parallel Computers F09: CS 6210 Matrix Computations S09: NYU G22.2112 Scientific Computing F08: NYU G22.2945 High Performance Scientific Computing S08: NYU V63.0222 Honors Calculus II F07: NYU V63.0233 Theory of Probability S07: NYU V63.0222 Honors Calculus II F06: NYU V55.0101 Quantitative Reasoning: Mathematical Patterns in Nature F05: Berkeley CS 164 Compilers and Programming Languages (TA for Richard Fateman) F01: Berkeley CS 267 Applications of Parallel Computers (TA for Kathy Yelick)

## Societies

 Member SIAM (Society for Industrial and Applied Mathematics) Member AMS (American Mathematical Society) Member ACM (Association for Computing Machinery) Member IEEE (Institute of Electrical and Electronics Engineers)

## Service

### External Activities

 2019 Reviewer NeurIPS 2019 reviewer 2019 Reviewer ICML 2019 reviewer 2018 Program committee member 2nd Black in AI Workshop 2018 Scientific committee co-chair SIAM Applied Linear Algebra Conference 2017 Program committee member IEEE International Parallel and Distributed Processing Symposium 2016 Minisymposium organizer SIAM Annual Meeting 2016 Program committee member IEEE International Parallel and Distributed Processing Symposium 2015 Program committee member IEEE International Parallel and Distributed Processing Symposium 2015 Organizing committee member Workshop on Development of Modern Methods for Linear Algebra 2015-present Scientific committee member Householder Symposium on Numerical Linear Algebra 2014 Poster commitee member Supercomputing 2013 Local organizer Fourth New York Conference on Applied Mathematics 2013 Technical program committee member Supercomputing 2012-2015 Secretary SIAM Activity Group on Linear Algebra 2012 Organizing committee member Third New York Conference on Applied Mathematics 2011 Organizing committee member Second New York Conference on Applied Mathematics 2017-2020 Associate editor Journal of Computational Mathematics 2010-present Editor Numerical Linear Algebra with Applications 2008-present Managing editor Electronic Transactions on Numerical Analysis 2007 Minisymposium organizer SIAM Computational Science and Engineering Meeting 2002-2004 Secretary IEEE 754R Standard Revision Committee

### Local Activities

 2017-2018 External committee member ECE/CAM Faculty Recruiting Committee 2017-2018 Committee member CS Chair Search Committee 2017-2018 Committee chair CS Graduate Admissions Committee 2017-2017 Committee member CS Colloquium Committee 2017-2017 Committee Member Applied Math Graduate Admissions Committee 2016-2017 Committee member CS Faculty Recruiting Committee 2015-2016 Committee member CS Graduate Admissions 2015 Committee member Applied Math Curriculum Review Committee 2014 Judge BigRed Hacks 2013-2014 Committee member Applied Math Graduate Admissions 2011-2012 Committee member Applied Math Graduate Admissions 2011-2014 Committee member Cornell Faculty Advisory Board on Information Technology 2010-2011 Committee member CS Graduate Admissions 2010-2011 Committee member Applied Math Graduate Admissions 2009-2010 Committee member CS Graduate Admissions 2009-2010 Committee member Applied Math Graduate Admissions 2009-present Organizer Cornell Scientific Computing and Numeric Seminar

### Review Activity

 ACM Journal on Emerging Technologies in Computing Systems 2014 ACM Transactions on Mathematical Software 2017 2016 2015 2014 2013 American Mathematical Monthly 2015 SIAM Journal on Applied Mathematics 2016 SIAM Journal on Matrix Analysis 2019 2018 2015 2014 2012 2009 2008 2007 2006 2005 SIAM Journal on Multiscale Modeling and Simulation 2012 SIAM Journal on Scientific Computing 2019 2017 2016 2015 2014 2009 2008 IEEE Sensors 2014 IEEE Transactions on Dependable and Secure Computing 2014 IEEE Journal of Microelectromechanical Systems 2011 2010 2009 2008 2007 2003 IEEE Transactions on Signal Processing 2007 Applicable Analysis 2007 Applied Mathematics and Computation 2017 2011 Communications in Pure and Applied Math 2010 Data Mining and Knowledge Discovery 2019 2018 International Journal of Computer Mathematics 2012 International Journal of Solids and Structures 2015 Journal of Applied Mechanics 2007 Journal of Applied Physics 2007 Journal of Computational Physics 2016 2015 2012 2011 Journal of Experimental Algorithms 2019 Journal of Micromechanics and Microengineering 2017 2016 2015 2014 2011 2009 2008 2007 Journal of Machine Learning Research 2016 Journal of Physics A: Mathematical and Theoretical 2007 Linear Algebra and its Applications 2018 2017 2015 2014 2013 2010 2009 2003 Mathematics of Computation 2007 Multiscale Modeling and Simulation 2013 Numerical Linear Algebra with Applications 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 Numerische Mathematik 2011 2009 Transactions of the Canadian Society for Mechanical Engineering 2009 DSL 2011: IFIP Working Conference on Domain-Specific Languages 2011 PARA04 conference proceedings 2004 IEEE ARITH conference 2004 ACM International Conference on Supercomputing 2003 ACM SIGGRAPH 2011 2009 Proposal referee for the Netherlands science foundation (NWO) 2003 Proposal referee for DOE 2015 2009 Proposal referee for NSF 2019 2018 2017 2016 2012 Proposal referee for NASA 2017 Proposal referee for NSERC 2019

## Students

### PhD students

 Kun Dong Applied Math Spectral graph analysis Eric Lee Computer Science Bayesian optimization Misha Padidar Applied Math Stellarator optimization Max Ruth Applied Math Nonlinear vibrations in MEMS Xinran Zhu Applied Math Numerical analysis of kernel methods David Eriksson Applied Math Ph.D., 2018 Scalable kernel methods and their use in black-box optimization Co-advised (secondary advisor: Christine Shoemaker) Moontae Lee Computer Science Ph.D., 2018 Joint-stochastic spectral inference for robust co-occurrence modeling and latent topic analysis Co-advised (primary advisor: David Mimno) Amanda Hood Applied Math Ph.D., 2017 Localizing the eigenvalues of matrix-valued functions: analysis and applications Colin Ponce Computer Science Ph.D., 2016 Network-Structured Error Flattening for Power Grids and Other Real-World Networks Erdal Yilmaz Applied Physics Ph.D., 2016 Design, Analysis and Simulation of Microscale Solid-Wave Gyroscopes Research advisor (formally advised by Richard Lovelace)

### Masters students

 Sungjun Cho Computer Science M.S., 2020 Bingqing Ma Computer Science M.Eng., 2018 Web Interface to a Finite Element Analysis Program Alvin Zhu Computer Science M.Eng., 2018 Fast non-negative projection of low rank matrices Tommy Shum Computer Science M.Eng., 2018 PySOT in the Cloud Peiyu Shi Computer Science M.Eng., 2018 A web dashboard for PySOT Israr Mahmood Computer Science M.Eng., 2018 A web dashboard for PySOT Daniel Liu Computer Science M.Eng., 2017 Bayesian optimization in PySOT Han Wen Chen Computer Science M.Eng., 2017 Jupyter Notebooks for Finite Element Analysis Michael Ficenic Computer Science M.Eng., 2017 Network Classification via Spectral Features Paul West Computer Science M.Eng., 2017 Optimization with Pre-Emptible VMs Joel Lubitinsky Computer Science M.Eng., 2017 Resource-Coupled Evolutionary Games Ioana-Maria Tamas Computer Science M.Eng., 2017 Resource-Coupled Evolutionary Games Batu Inal Computer Science M.Eng., 2016 Super Seminar Scraper Sania Nagpal Computer Science M.Eng., 2016 Fast Trial Spaces for Parameterized PageRank Problems Suarabh Netravalkar Computer Science M.Eng., 2016 Fast Trial Spaces for Parameterize PageRank Problems Shitong Jia Computer Science M.Eng., 2016 Etch-a-Sketch 3D Jing Jing Computer Science M.Eng., 2016 Super Seminar Scraper Jiankun Lu Computer Science M.Eng., 2016 Super Seminar Scraper Xiangyu Zhang Computer Science M.Eng., 2016 Edge-Weighted Parameterized PageRank: A Demo Markus Salasoo Computer Science M.Eng., 2016 Edge-Weighted Parameterized PageRank: A Demo Ziyang Tang Computer Science M.Eng., 2016 Optimizing Price of Anarchy in a Model of Opinion Formation Jia Zhang Computer Science M.Eng., 2016 Spectral Histograms on Networks Xiaoan Yan Computer Science M.Eng., 2015 Edge-Weighted Parameterized PageRank: A Demo Chen Ting-Hao Electrical Engineering M.Eng., 2013 MEMS Simulation as a Service Syed Hassan Computer Science M.Eng., 2013 MEMS Simulation as a Service Kim Young Jae Computer Science M.Eng., 2013 Teaching Support Vector Machines to Predict Profitable Stock Offers Jungmin Yun Computer Science M.Eng., 2012 Python Implementation of a Stochastic RBF Optimization Method Jesseon Chang Computer Science M.Eng., 2012 Python Implementation of a Stochastic RBF Optimization Method Chen-Yi Chen Computer Science M.Eng., 2012 A Flexible Interface to FEAP Scott Purdy Computer Science M.Eng., 2010 Process Replication for HPC Applications Peter Hunt Computer Science M.Eng., 2010 Process Replication for HPC Applications Silvio Tarca Scientific Computing (NYU) M.S., 2010 Performance Optimization of Symmetric Factorization Algorithms

 Nathaniel Rogalskyj Identifying Network Changes from Ringdown Analysis (2016) Anna Yesypenko Graph Analysis via Density of States (2016) Moteleolu Onabajo Graph Analysis via Density of States (2016) Jianqiu Wang Graph Analysis via Density of States (2016) Humam Alwassel Automated Floating Point Error Analysis (2015) Patrick Chen Super Seminar Scraper (2015-16) Jooyoung Park Maximum Entropy Method Implementation in MATLAB (2015) Sheroze Sherifdeen MatScat for Python (2015-16) Nimit Sohoni Review of the FEAST Eigensolver (2015) Leon Davis Diagonal Completion for Low-Rank Matrices (2015) Greg Rosenthal PageRank with Random Edge Weights (2015) Eric Ma Graph Analysis via Spectral Histograms (2015) Brandon Hartz Fast PageRank on Parameter-Dependent Graphs (2014) Marshall Jiang Randomly Projected GMRES (2014-15) Kyu-Young Kim Evaluating Overlapping Community Detection via Matrix Factorizations (2011-12) Jiexun Xu Fast Factorizations for Network Tomography (2008) Iva Vukicevic Error Bounds and Estimates for a Discrete Sine-Gordon Equation (2008) Daniel Parry Bounds on Biased and Unbiased Random Walks (2008) Anwis Davis MEMS Web Service (2002) Ernest Zhu MEMS Web Service (2001) Wayne Kao MEMS Web Service (2001)

### Thesis committees

#### Current

1. Sebastian Ament (Computer Science)
2. Raul Astudillo Marban (Operations Research)
3. Chris Browne (Applied Math)
4. Gabriela Calinao Correa (Material Science)
5. Kun Dong (Applied Math)
6. Matthew Hin (Applied Math)
7. Andrew Horning (Applied Math)
8. Junteng Jia (Computer Science)
9. Chaitali Joshi (Physics)
10. Fujun Luan (Computer Science)
11. Hubert Lin (Computer Science)
12. Max Lipton (Mathematics)
13. Enrique Rojas (Atmospheric Science)
14. John Ryan (Computer Science)
15. Zhengdi Shen (Applied Math)
16. Qinru Shi (Applied Math)
17. Saul Toscano (Operations Research)
18. Dongping Qi (Applied Math)
19. Calvin Wylie (Operations Research)
20. Mengqi Xia (Computer Science)