CSE welcomes new faculty for academic year 2021/22

With six new tenure-track faculty and one presidential postdoctoral fellow in CSE on campus this fall and another faculty member arriving in 2022, Michigan continues to expand and strengthen its scope of research and teaching activities. From theory through algorithms and cryptography to artificial intelligence, these individuals will help to define what it means to be a computer scientist.

Nikhil Bansal

Patrick C. Fischer Professor of Theoretical Computer Science
PhD, Computer Science, 2003
Carnegie Mellon University

Nikhil Bansal has joined CSE as the first Patrick C. Fischer Professor of Theoretical Computer Science. His research focuses on the design and analysis of algorithms, and he has worked in related areas such as discrete mathematics, machine learning, combinatorial optimization, and complexity. He has received several best paper awards for his work, and has seven US patents. Bansal has advised or co-advised thirteen PhD students and postdoctoral researchers, and mentored a number of master’s theses and undergraduate interns. Most recently, Bansal has been a researcher at CWI, Amsterdam and a professor at the Eindhoven University of Technology. Prior to that, he was at the IBM T. J. Watson Research Center, where he also managed the Algorithms group.

Michal Dereziński

Assistant Professor
PhD, Computer Science, 2018
University of California, Santa Cruz

Michał Dereziński’s research is focused on developing scalable randomized algorithms with robust statistical guarantees for machine learning, data science and optimization. Dereziński received the Best Dissertation Award at UCSC for his work on sampling methods in statistical learning. His work on reducing the cost of interpretability in dimensionality reduction received the Best Paper Award at the Thirty-fourth Conference on Neural Information Processing Systems. Dereziński joins CSE from the University of California, Berkeley, where he was a postdoctoral fellow in the Department of Statistics. He was previously a research fellow at the Simons Institute for the Theory of Computing (Fall 2018, Foundations of Data Science program). 

Benjamin Fish

Assistant Professor
PhD, Mathematics (specializing in Mathematical Computer Science), 2018
University of Illinois at Chicago

Ben Fish’s research develops methods for machine learning and other computational systems that incorporate human values and social context.  This includes scholarship in fairness and ethics in machine learning and learning over social networks.  Ben comes to CSE after serving as a postdoctoral fellow at the Mila Quebec AI Institute. Prior to that, he was with the Fairness, Accountability, Transparency, and Ethics (FATE) Group at Microsoft Research Montréal.  Fish has also been a visiting researcher at the University of Melbourne and at the University of Utah.

Paul Grubbs

Assistant Professor
PhD, Computer Science, 2020
Cornell University

Paul Grubbs does research in applied cryptography, security, and systems. In his work, he uses a wide array of theoretical and practical tools – everything from designing and analyzing cryptography to hacking on database internals – to make computer systems more secure. His research has impacted systems used by billions of people, including Facebook Messenger, Zoom, and Amazon Web Services. Grubbs was the recipient of a 2017 NSF Graduate Research Fellowship and a 2020 Cornell CS Dissertation Award. Grubbs was most recently a Postdoctoral Fellow at New York University.

Yatin Manerkar

Assistant Professor
PhD, Computer Science, 2021
Princeton University

Yatin Manerkar’s research lies on the boundary between systems and formal methods, and develops automated formal methodologies and tools for the design and verification of computing systems. His specific interests include developing new formalisms and verification methodologies for emerging hardware and software, hardware security, formal synthesis, and ethical and fair AI. At Princeton, Yatin received the Wallace Memorial Fellowship, one of Princeton’s highest graduate honors. He also received the 2019 Award for Excellence from Princeton’s School of Engineering and Applied Science. Prior to joining CSE, Manerkar was a postdoctoral researcher at UC Berkeley. He previously worked at Qualcomm Research.

Max New

Assistant Professor
PhD, Computer Science, 2021
Northeastern University

Max New works on the mathematical foundations of programming languages and on applying semantic models to the design and implementation of correct, safe and efficient software. His main focus is on language-based approaches to interoperability between software written in multiple languages, including gradual typing and reusable compiler intermediate languages. He aims to leverage recent advances in mathematical logic and category theory to aid in these practical issues. Max was previously a postdoctoral researcher at Wesleyan University.

Maggie Makar

Presidential Postdoctoral Fellow
PhD, Electrical Engineering and Computer Science, 2021
Massachusetts Institute of Technology

Maggie Makar joins CSE as our first Presidential Postdoctoral Fellow on a pathway to join as an assistant professor. Her research interests lie at the intersection of machine learning and causal inference. Her work leverages causal ideas to make ML models robust to distributional shifts, and utilizes ideas from machine learning to make causal inference more statistically efficient. She focuses on developing ML and causal models to guide decision making, particularly in the field of healthcare. Her work has appeared in ICML, AAAI, JSM, JAMA, Health Affairs, and Epidemiology. 

Wei Hu – Joining in Fall 2022

Postdoctoral Researcher, University of California, Berkeley
PhD, Computer Science, 2021
Princeton University

Wei Hu is broadly interested in the foundations of modern machine learning. He aims to obtain a solid, rigorous and practically relevant theoretical understanding of machine learning methods, as well as to develop principles to make machine learning systems better in terms of reliability and efficiency. His PhD research primarily focused on the theory of deep learning, while he also worked on topics in representation learning, optimization, and online learning. He is a recipient of the Siebel Scholarship Class of 2021. Hu is a postdoctoral researcher at the University of California, Berkeley and will join CSE as an assistant professor at Michigan in Fall 2022.