Department Directory

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. 

Professor Constable stepped down as the Dean of the Faculty of Computing and Information Science at Cornell University, serving from 1999 - 2009. CIS is a college-level unit created to advance Cornell’s academic and research initiative in computing and information science. Professor Constable was formerly chairman of Cornell’s Computer Science Department for six years.

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.

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.