Department Directory

Rachit Agarwal is an associate professor of computer science. His primary research interests are in systems and networking. He is also interested in theoretical problems arising out of building practical systems. Agarwal’s research has been awarded a Sloan Research Fellowship, an NSF CAREER award, a Kavli Fellowship with the National Academy of Sciences, an IRTF Applied Networking Research Prize, and multiple best paper awards at SIGCOMM and Usenix Security. Agarwal loves teaching. He received the 2025 Tau Beta Pi Professor of the Year Award and the James and Mary Tien Excellence in Teaching, the highest teaching award from Cornell Engineering for sustained excellence and innovation in engineering education.

Lorenzo Alvisi is the Tisch University Professor in Computer Science and chair of the Department of Computer Science. He is interested in the theory and practice of dependable distributed computing. His group's research aims to understand how to design and build trustworthy distributed systems. Their work investigates both foundational and applied aspects of reliable distributed computing – and at its best – leverages the former to shape the latter. Alvisi received his Laurea Summa cum Laude and Corso di Specializzazione in Physics from the University of Bologna, and his master's degree and Ph.D. in computer science from Cornell University. He is an IEEE Fellow, an ACM Fellow, a Humboldt Research Award winner, and an Alfred P. Sloan Research Fellow.   

Yoav Artzi is an associate professor of computer science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. His research focuses on developing models and learning methods for natural language understanding and generation in interactive systems.

Hadar Averbuch-Elor is an assistant professor of computer science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. Averbuch-Elor’s research interests lie in the intersection of computer graphics and computer vision, particularly in combining pixels with more structured modalities, such as natural language and 3D geometry.

Kavita Bala is the 17th provost of Cornell University and professor of computer science. Previously, she served as the inaugural dean of the Cornell Ann S. Bowers College of Computing and Information Science and chair of the Department of Computer Science. In her research, she specializes in computer vision and computer graphics, leading research in visual recognition and search; and material modeling and perception. She co-founded GrokStyle, a visual recognition AI company that drew IKEA as a client, and was acquired by Facebook in 2019. Bala is a Fellow of the American Academy of Arts & Sciences (2025), an Association for Computing Machinery (ACM) Fellow (2019), Fellow of the SIGGRAPH Academy (2020), and recipient of the Computer Graphics Achievement Award (2020).

Tapomayukh "Tapo" Bhattacharjee is an assistant professor in the Department of Computer Science at Cornell University where he directs the EmPRISE Lab. He completed his Ph.D. in robotics from Georgia Institute of Technology and was an NIH Ruth L. Kirschstein NRSA postdoctoral research associate in Computer Science and Engineering at the University of Washington. He wants to enable robots to assist people with mobility limitations with activities of daily living. His work spans the fields of human-robot interaction, haptic perception, and robot manipulation and focuses on addressing the fundamental research question of how to leverage robot-world physical interactions in unstructured human environments to perform relevant activities of daily living. 

David S. Bindel is a professor of computer science and director of the Center for Applied Math. He works at the interface of computational science and engineering, and his research mixes mathematical analysis, application modeling, and software design. Active research areas include: optimizing stellarators, verified numerics, kernel methods, parallel surrogate optimization, spectral network analysis, nonlinear eigenvalue bounds, and nonlinear waves in resonant MEMS. Bindel received his Ph.D. in computer science from the University of California, Berkeley and his B.S. in math and computer science from the University of Maryland, College Park. He is a SIAM Fellow and Sloan Fellow.

Ken Birman is the N. Rama Rao Professor of Computer Science. His research is on reliable, secure, and scalable distributed systems. Current projects include Vortex, a platform for speeding up AI and ML inference or knowledge retrieval tasks by fully leveraging cutting edge hardware accelerators and reimplementing key data paths to reduce or eliminate copying and other delays; Cascade, an exceptionally performant storage framework for Vortex; and Derecho, a highly optimized library for accelerating communication that leverages RDMA when available. Jointly, these three elements enable dramatic improvements in the cost of ML hosting and sharp reductions in ML latencies. In more entrepreneurial roles, Ken founded a series of companies. One focused on software fault tolerance and created a variety of cloud computing infrastructure solutions. Another architected and implemented the core of the New York Stock Exchange trading floor, the Swiss Exchange, the French Air Traffic Control System communication platform, and created a secure, high-speed data sharing capability for the U.S. Navy AEGIS warship. Ken received his Ph.D. and master's degrees in computer science from the University of California, Berkeley and his B.A. in computer science from Columbia University, is an ACM Fellow and IEEE Fellow, and won the IEEE Tsutomo Kanai award for his innovations in distributed computing.

Claire Cardie is the John C. Ford Professor of Engineering in the Departments of Computer Science and Information Science. She was the founding chair of the Department of Information Science and led the development of its academic programs. Cardie works in the area of Natural Language Processing (NLP) on topics ranging from information extraction, text summarization, and noun phrase coreference resolution, to the automatic analysis of opinions, argumentation, and deception in text. 

Eshan Chattopadhyay is currently an associate professor (with tenure) in the Department of Computer Science at Cornell University. He joined Cornell in 2018 after completing postdoctoral work at the Institute for Advanced Study in Princeton and the Simons Institute for the Theory of Computing in Berkeley. Prior to this, Chattopadhyay earned his Ph.D. in computer science from the University of Texas at Austin in 2016 and his B.Tech in computer science from the Indian Institute of Technology Kanpur in 2011.

Sanjiban Choudhury is an assistant professor of computer science and works on interactive AI agents that self-align through few-shot interactions with humans and their environment. His research focuses on reinforcement learning (RLHF), imitation learning (IRL), and foundation models for planning, robotics, and code generation. He also leads the PoRTaL group, which builds everyday robots for everyday users and has a mission to make robots accessible, user-friendly, and practical for tasks from cooking to cleaning. Choudhury did his postdoctoral research at the University of Washington and his M.A. and Ph.D. at Carnegie Mellon University. He earned his B.S. and M.S. in electrical engineering from the Indian Institute of Technology, Kharagpur. 

Michael Clarkson is teaching-track faculty in the Department of Computer Science at Cornell University. In 2022, after a decade of teaching a total of about 6,000 students, he received the university’s highest annual teaching award for teaching-track faculty and was appointed as a Provost’s Teaching Fellow, which is a permanent designation. He is best known for his open-source textbook on OCaml programming, which is used at Cornell and elsewhere. His accompanying YouTube channel on functional programming has received more than a million views from around the world. He also teaches courses on object-oriented programming, formal verification, computer security, and data science. Clarkson received his M.S. and Ph.D. in computer science from Cornell. 

Alex Conway is an assistant professor of computer science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science.

Preston Culbertson draws on machine learning, computer vision, and control theory to develop robots that move like humans. Prior to joining Cornell, he was a research scientist at The AI Institute in Cambridge, Mass. He received his Ph.D. in mechanical engineering from Stanford University.

Anil Damle is an associate professor of computer science. His research focuses on the development and analysis of robust and efficient algorithms in applied and computational mathematics that exploit structure coming from underlying physical or statistical models. He interfaces with a broad range of application areas and his work is inherently interdisciplinary – with the ultimate goal of developing algorithms that are usable for practitioners. He received his Ph.D. from Stanford University in computational and mathematical engineering, and his M.A. in applied mathematics and B.S. in applied mathematics and computer engineering from the University of Colorado, Boulder.

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. 

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.