Department Directory

Cristian Danescu-Niculescu-Mizil is an associate professor of information science. His research aims at developing computational frameworks that can lead to a better understanding of human social behavior by unlocking the unprecedented potential of the large amounts of natural language data generated online. His awards and honors include a National Science Foundation CAREER Award, Google Research Faculty Award, and numerous Best Paper awards. His work has been featured in popular media outlets such as the New Scientist, NBC's The Today Show, NPR, BBC, WIRED, the New York Times, Wall Street Journal, and Washington Post. For publications, recent updates and more fun stuff, check out Cristian's webpage.

Abe Davis is an assistant professor of computer science who specializes in computer graphics, computer vision, and human-computer interaction (HCI). His group brings these areas together to work on new problems at the intersection of different disciplines. Davis completed postdoctoral research at Stanford University and Cornell Tech, and his M.A. and Ph.D. at the Massachusetts Institute of Technology, studying electrical engineering and computer science. He earned his undergraduate degree in computer science at Stanford.

Christopher De Sa is an associate professor of computer science and a member of the Cornell Machine Learning Group where he leads the Relax ML Lab. His research interests include algorithmic, software, and hardware techniques for high-performance machine learning, with a focus on relaxed-consistency variants of stochastic algorithms such as asynchronous and low-precision stochastic gradient descent (SGD) and Markov chain Monte Carlo. His work builds towards using these techniques to construct data analytics and machine learning frameworks, including for deep learning, that are efficient, parallel, and distributed. De Sa received his B.S., M.A., and Ph.D. from Stanford University in electrical engineering.

Sarah Dean is an assistant professor of computer science. She studies the interplay between optimization, machine learning, and dynamics in real-world systems. Her research focuses on understanding the fundamentals of data-driven methods for control and decision-making, inspired by applications ranging from robotics to recommendation systems. She completed her postdoctoral research at the University of Washington and earned her M.S. and Ph.D. in electrical engineering and computer science at the University of California, Berkeley. Dean received her B.S.E. in electrical engineering and mathematics from the University of Pennsylvania. 

Nicola Dell is an associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and the Cornell Ann S. Bowers College of Computing and Information Science. She advises Ph.D. and M.S. students in the fields of information science and computer science.

Saikat Dutta is an assistant professor in the Department of Computer Science. His research interests are at the intersection of software engineering and machine learning, with a particular focus on developing software testing and debugging techniques to improve the reliability of machine learning-based systems. He is also exploring how to leverage the latest machine learning techniques to solve software engineering problems. Dutta completed his postdoctoral research at the University of Pennsylvania and received his Ph.D. in computer science from the University of Illinois Urbana-Champaign. He received his bachelor's degree in computer science and engineering from Jadavpur University.

Matthew Eichhorn is a lecturer of computer science who leads large undergraduate courses on discrete mathematics and programming. His research focuses on developing tools to inform decisions with societal implications. This ranges from developing algorithms for online team formation, finding ways to fairly distribute goods in settings such as public health and education where the normative allocation criteria are often at odds, and using statistical tools from causal inference to estimate the effectiveness of an intervention that propagates through a social interference network. 

Ahmed El Alaoui is an assistant professor of statistics and data science. His research interests revolve around high-dimensional phenomena in statistics and probability theory, statistical physics, algorithms, and problems where these areas meet. Prior to joining the Cornell faculty in 2021, he was a postdoctoral researcher at Stanford University, hosted by Andrea Montanari. He received his Ph.D. in electrical engineering and computer sciences from UC Berkeley in 2018, advised by Michael I. Jordan. 

Kevin Ellis is an assistant professor of computer science. He works on program synthesis and neurosymbolic AI. Ellis earned his Ph.D. in cognitive science and his B.S. in physics with a concentration in linguistics from the Massachusetts Institute of Technology.

Deborah Estrin is the Robert V. Tishman ’37 professor of computer science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. At Cornell Tech, she serves as the Associate Dean for Impact. She is also an affiliate faculty member at Weill Cornell Medicine.

Daisy Fan is a teaching professor of computer science who teaches in the areas of programming, scientific computing, and optimization. Fan's interests include collaborative learning methodologies and technologies in computer science and engineering education, application of systems-analysis techniques for water resources and environmental problems, and development of numerically efficient optimization methods for engineering applications. Problems she has investigated include optimal control of multiple-reservoir operation using stochastic dynamic programming and river basin water quality management.

Kuan Fang is an assistant professor of computer science who conducts research at the intersection of robotics, machine learning, and computer vision. His research aims to enable robots to perform diverse and complex tasks in unstructured environments using deep learning. To achieve this, his lab develops scalable algorithms and systems for robot perception and control with the following focuses: acquiring versatile and generalizable skills for visuomotor control by learning from massive and diverse data; continuously improving the capabilities of robots through autonomous data collection and generation; and boosting generalization to novel tasks, environments, and robots by integrating prior knowledge from broad sources. Fang did his postdoctoral research at the University of California, Berkeley and received his Ph.D. and M.S. from Stanford University. His bachelor's degree is from Tsinghua University. Fang also spent time at the RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia. 

Nate Foster is an adjunct professor of computer science at Cornell University and a visiting researcher at Jane Street. He currently serves as vice chair of DARPA's Information Science and Technology (ISAT) study group, and as chair of the P4 Language Governing Board.  

Sainyam Galhotra is an assistant professor in computer science. Before that, he was a Computing Innovation Fellow pursuing postdoctoral research at the University of Chicago. The goal of his research is to develop data science tools for effective and responsible analytics.

Carla Gomes is the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, director of the Institute for Computational Sustainability at Cornell University, and co-director of the Cornell University AI for Science Institute. Gomes received a Ph.D. in computer science in artificial intelligence from the University of Edinburgh. Her research area is AI with a focus on large-scale constraint reasoning, optimization, and machine learning. 

Tanya Goyal is an assistant professor in the Department of Computer Science. Previously, she was a postdoctoral scholar at Princeton Language and Intelligence Center (2023-2024). Her research interests are primarily in the field of natural language processing. Problems that she is currently excited about include: reliable and sustainable evaluation frameworks for large language models (LLMs); factuality assessment and improvement of LLMs; and understanding LLM behaviors as a function of training data and/or alignment strategies.

Giulia Guidi is an assistant professor in the Department of Computer Science at Cornell University. She received her Ph.D. in computer science from UC Berkeley. Guidi works in the field of high-performance computing (HPC) for large-scale computational sciences and leads the Cornell HPC group. She is interested in developing algorithms and software infrastructures on parallel machines to speed up data processing without sacrificing programming productivity, and to make high-performance computing more accessible. She is also a big fan of sparse linear algebra and believed in sparse linear algebra as a computational abstraction for tackling large-scale computational challenges.

François Guimbretière is a professor of information science. Guimbretière's research explores how hardware/software co-design can deliver novel interactions in a broad set of application domains. Blending system building and design, Guimbretière and his students strive to turn future visions of seamless interactions into today’s reality. His prior work explored pen-based interfaces on paper and screen. He currently focuses on novel fabrication techniques, such as interactive printing and yarn-based fabrication. He is also interested in the intersection of HRI and sustainability, including the design of immersive and engaging remote collaboration interfaces that limit our reliance on physical travel. For more information, visit Guimbretière's ACM webpage for the most up-to-date description of his work.

In Memoriam: Joe Halpern, ‘towering’ computer scientist and mentor, dies at 72