Rate My Professor Chinmay Hegde

Chinmay Hegde is an Associate Professor with joint appointments in the Computer Science and Engineering Department and the Electrical and Computer Engineering Department at the NYU Tandon School of Engineering, where he leads the Data, Intelligence, and Computation in Engineering (DICE) Lab. He earned his Ph.D. in Electrical and Computer Engineering from Rice University in 2012 under the supervision of Richard G. Baraniuk, with a thesis titled “Nonlinear Signal Models: Geometry, Analysis, and Algorithms,” which received the Ralph Budd Award for Best Thesis in the School of Engineering. Hegde also holds an M.S. in Electrical and Computer Engineering from Rice University in 2010 and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Madras in 2006.

Prior to NYU, starting in Fall 2019, Hegde served as an Assistant Professor in the Electrical and Computer Engineering Department at Iowa State University, where he held the Black & Veatch Faculty Fellowship. He completed postdoctoral research as an Associate in the Theory of Computation group at MIT's CSAIL, working with Piotr Indyk. Hegde's research focuses on principled, fast, and robust algorithms for machine learning, with applications in imaging and computer vision, materials design, and transportation; additional interests include algorithms, big data, signal and image processing. He has received the NSF CAREER Award (2018–2023), Best Paper Award at the International Conference on Machine Learning (2015), NSF CISE Research Initiation Initiative Award (2016), and NeurIPS Top Reviewer Award (2019). Key publications include “Model-based Compressive Sensing” (IEEE Transactions on Information Theory, 2010; over 2,200 citations), “Livebench: A Challenging, Contamination-Free LLM Benchmark” (ICLR, 2025), “On the Computational Complexity of Self-Attention” (International Conference on Algorithmic Learning Theory, 2023), and “Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees” (IEEE International Conference on Acoustics, Speech and Signal Processing, 2018). His contributions have advanced compressive sensing, structured sparsity, and AI robustness.