The Cornell Ann S. Bowers College of Computing and Information Science is welcoming 13 new faculty members in the departments of Computer Science, Information Science and Statistics and Data Science. Collectively, their work ranges from developing robots that assist people with mobility limitations to using computational tools to study inequality and graphical models to solve real-world problems. 

The new faculty will join the collaborative faculty of the college and help shape existing and emerging fields of inquiry. As researchers and teachers, they will examine societal and ethical implications of technological innovation, including sustainability and the ongoing expansion and integration of technology into our lives. 

The following is a list of those joining the college community in the next year:

Tapomayukh Bhattacharjee, Assistant Professor, Computer Science

Bhattacharjee examines how to leverage robot-world physical interactions in unstructured human environments to perform relevant activities of daily living. Since the technologies Bhattacharjee creates are designed to help real people with real problems, it is essential he has inputs from the people who will be assisted by these robots and from other stakeholders. This means that his research and design process includes not only computer scientists and engineers, but also people with physical disabilities, caregivers, physical rehabilitation providers, and occupational therapists. It is an iterative process that relies on advice and feedback from the people who will actually use the technology.

 

Sanjiban Choudhury, Assistant Professor, Computer Science

Choudhury will join the college in Spring 2022; he is currently a researcher at Aurora and was previously a postdoctoral fellow at the University of Washington CSE. Chaudhury is interested in efficient inference for robot decision making and works on theory and algorithms at the intersection of machine learning and motion planning. Much of his research has been deployed on real-world robot systems—full-scale helicopters, self-driving cars, and mobile manipulators.

 

Sarah Dean, Assistant Professor, Computer Science

Dean is currently a Ph.D. candidate in the Department of Electrical Engineering and Computer Science at UC Berkeley and will be joining Cornell Computer Science in January 2022. Dean is interested in the interplay between optimization, machine learning, and dynamics in real-world systems. Her research focuses on developing principled data-driven methods for control and decision making, inspired by applications in robotics, recommendation systems, and developmental economics. 

 

Kevin Ellis, Assistant Professor, Computer Science

Ellis works on program induction—building machines that learn to write code—which has applications and connections to programming languages, but also to several areas of AI, including: model structure learning, neurosymbolic methods, and semantic parsing. He says: "I'm thrilled to join the Cornell CS community and contribute to its research and teaching. The department has fantastically strong presences in AI and programming languages, and I look forward to collaborating together."

 

Justin Hsu, Assistant Professor in Computer Science

Hsu returns to Cornell where he was previously a postdoctoral fellow hosted by Nate Foster, Robert Kleinberg, and Dexter Kozen. An NSF CAREER Award winner, Hsu designs methods to formally verify that programs are correct, especially programs that use randomization. His research blends randomized algorithms (from theoretical computer science) and formal verification. He seeks to apply differential privacy to optimization, machine learning, and mechanism design.

 

Allison Koenecke, Assistant Professor, Information Science

Koenecke will join Information Science beginning Summer 2022. She is currently a postdoc at Microsoft Research New England in the Machine Learning and Statistics group. Her research interests lie broadly at the intersection of economics and computer science, and her projects focus on fairness in algorithmic systems and causal inference in public health. She received a Ph.D. from the Stanford Institute for Computational & Mathematical Engineering.

 

Ian Lundberg, Assistant Professor, Information Science

Lundberg is currently a postdoctoral scholar in the Department of Sociology at the California Center for Population Research at UCLA. His research explores how computational tools can change the way we study inequality. He received a Ph.D. in Sociology and Social Policy at Princeton University. Lundberg will join Information Science beginning in 2022  

 

Emma Pierson, Assistant Professor, Computer Science and the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion with a secondary joint appointment as an Assistant Professor of Population Health Sciences at Weill Cornell Medicine

Pierson develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, and Forbes 30 Under 30 in Science. Previously, she was a Senior Researcher at Microsoft Research New England and a Ph.D. student in Jure Leskovec’s lab at Stanford, where she was supported by Hertz and NDSEG Fellowships. 

 

Alexandra Silva, Professor, Computer Science

Silva’s research involves semantics of programming languages and verification, with a particular focus on the design of methods that are general enough to be applied to different models. A lot of her research uses ideas from a classical area of theoretical computer science, automata theory, seen through the glass of the abstract frameworks of coalgebra and category theory. This perspective has enabled Silva to derive equivalence and active learning algorithms, as well as proof techniques for completeness, for a large class of automata models. She arrives most recently from the University College London, where she was a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation.

 

Angelique Taylor, Assistant Professor, Information Science

Taylor will join Information Science beginning Summer 2022. Her research lies at the intersection of computer vision, robotics, and health informatics. She develops systems that enable robots to interact and work with groups of people in safety-critical environments. She received a Ph.D. in Computer Science and Engineering from the UC San Diego.

 

Gili Vidan, Assistant Professor, Information Science

Vidan’s work looks at digital information technologies, changing notions of public trust and democratic governance, and narratives of crisis and future-making in the U.S. Her dissertation, “Technologies of Trust,” traces technical attempts to solve the problems of trust and transparency, with a focus on the development of public-key cryptography in late 20th- and early 21st-century. She is the 2019-20 Ambrose Monell Foundation Fellow in Technology and Democracy at the Jefferson Scholars Foundation.

 

Sam Wang, Assistant Professor, Statistics and Data Science

Wang’s primary research area is graphical models and a sub-field called causal discovery. Practitioners of causal discovery take observational data with multiple variables – i.e., proteins in a cell, impulses measured in different areas of the brain, or even different stocks in the stock market – and test to see which variables might have a direct effect on other variables.

He arrives to Cornell after completing a post-doctoral appointment at the University of Chicago’s Booth School of Business. He earned a Ph.D. in Statistics at the University of Washington in 2018 and worked as a management consultant prior to pursuing a doctoral degree.

 

Dana Yang, Assistant Professor, Statistics and Data Science

Yang will join Cornell in Spring 2022. She is currently a postdoc at the Fuqua school of Business, Duke University where she works with Jiaming Xu. In 2019 she received her Ph.D. degree from the Department of Statistics and Data Science at Yale University, where she was co-advised by David Pollard, Yihong Wu, and John Lafferty. She is spending Fall 2021 at Simons Institute for the Theory of Computing, UC Berkeley on a Simons-Berkeley research fellowship and is participating in the program Computational Complexity of Statistical Inference.