Directory

Chris Csíkszentmihályi is an associate professor of information science, and an artist, occasional scholar, technology critic, and technologist. His research creates artistic and engineered sociotechnical systems to increase social justice and reconfigure power for the less served. His teaching helps orient and ground students for an ethical life in technology. Prior to joining Cornell in 2020, he was a European Research Area Chair in Portugal and founded the free/libre Rootio Project, a sociotechnical platform for community radio. Csíkszentmihályi has been a professor at universities in the USA and Europe: notably founding and directing the MIT Center for Future Civic Media (C4), dedicated to developing technologies that strengthen communities, and also the MIT Media Lab's Computing Culture group, creating unique media technologies for cultural and political applications. He has been a Radcliffe Fellow and a Rockefeller New Media Fellow, and has exhibited installations and performed on most continents. He currently sits on the board of the DATACTIVE ERC project at the Univerity of Amsterdam and the EPSRC Centre for Doctoral Training in Digital Civics at Newcastle University.

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.

Jim Dai is a professor in the School of Operations Research and Information Engineering (ORIE) of Cornell University. He is currently on leave from the Chandler Family Chair of Industrial & Systems Engineering at Georgia Institute of Technology, where he has been a faculty member for 22 years. He is a Special Term Professor at Tsinghua University and a Visiting Professor in Decision Sciences at National University of Singapore. For more than twenty years, he has worked on stochastic models arising from communications, manufacturing, and service systems that include data switches, semiconductor wafer fabrication lines, call centers, and healthcare-delivery systems.

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.

Sreyoshi Das designs and offers courses on applications of statistics and data science in the industry, with specific emphasis in the areas of economics and finance. Her courses aim to integrate academic training with hands-on work experience. Before joining Cornell in 2022, she worked in economic consulting where she developed a variety of quantitative and qualitative analyses to support testifying experts, client attorneys, government agencies, and corporations. Das received her Ph.D. in economics from the University of Michigan in 2017, where she conducted research on banking and systemic risk, financial markets in emerging economies, and behavioral macroeconomics.

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.

Tom DiCiccio is an associate professor of social statistics in the ILR School.

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.

Raaz Dwivedi joined Department of Operations Research and Information Engineering and Cornell Tech at Cornell University as an Assistant Professor in Jan 2024. Prior to that, he visited Cornell ORIE in Fall 2023 and spent two years as a FODSI postdoc fellow at Harvard and MIT LIDS, and spent a summer at Microsoft Research New England. He did his Ph. D. In EECS at UC Berkeley in 2021 and bachelors in EE at IIT Bombay in 2014. His research builds statistically and computationally efficient strategies for personalized decision-making with theory and methods spanning the areas of causal inference, reinforcement learning, and distribution compression. He has won a best student paper award for work on optimal compression and teaching awards at UC Berkeley and Harvard.

David Easley is the Henry Scarborough Professor of Social Science and is a Professor of Information Science at Cornell University. His research is in the fields of economics, finance and networks. In economics, he and Lawrence Blume work on learning and wealth dynamics. In finance, his work with Maureen O'Hara focuses on market microstructure and asset pricing. In networks, he works with colleagues in the Computer Science department on trading networks and network formation.

Professor Easley recently announced a new measure developed with Cornell colleague Maureen O'Hara and Marcos Lopez de Prado of Tudor Investment Corporation that may be useful in preventing a "flash crash" similar to what happened in May 2010 when the stock market briefly erased almost $1 trillion in value and plunged the Dow Jones Industrial Average into its biggest intraday fall ever. The new volume-synchronized probability of informed trading (VPIN) metric is based on the imbalance of trade relative to the total volume. It identifies flow toxicity, which has been the primary topic of Easley and O'Hara's research over the last 20 years. 

Professor Easley and Professor Jon Kleinberg, Computer Science at Cornell, recently co-authored Networks, Crowds and Markets: Reasoning About a Highly Connected World.  The book is based on the popular undergraduate course, Networks, which they developed at Cornell. It combines different scientific perspectives in its approach to understanding networks and behavior in networked environments. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, the book describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
 

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.