Ritwik Gupta profile picture

Ritwik Gupta

ritwikg [at] umd.edu

AI in chaotic environments and governance of dual-use military-civil AI

Assistant Professor, University of Maryland

Recent Publications

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Learning the Shared Latent Structure of Cryo-EM Inverse Problems

Nithin Chalapathi, Theo Sternlieb, Ethan Han, Sonya M Hanson, Pilar Cossio, Aditi S Krishnapriyan, Ritwik Gupta

EMber is a self-supervised foundation model for cryo-EM that learns transferable representations of molecular structure, achieving state-of-the-art performance on conformational analysis, population estimation, and 3D reconstruction.

arXiv preprint paper github
Artwork for Learning the Shared Latent Structure of Cryo-EM Inverse Problems
Tinted Frames: Question Framing Blinds Vision-Language Models

Wan-Cyuan Fan, Jiayun Luo, Declan Kutscher, Leonid Sigal, Ritwik Gupta

VLMs are selectively blind. They decide how much to look at an image based on question framing (open-ended vs Yes/No vs MCQ), even when the same visual reasoning is required. We analyze this phenomenon and mitigate it in this paper.

Conference on Empirical Methods in Natural Language Processing (EMNLP) 2026 website paper github
Artwork for Tinted Frames: Question Framing Blinds Vision-Language Models
TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale

Milton Gomez, Marie McGraw, Saranya Ganesh S., Frederick Iat-Hin Tam, Ilia Azizi, Samuel Darmon, Monika Feldmann, Stella Bourdin, Louis Poulain-Auzéau, Suzana J. Camargo, Jonathan Lin, Dan Chavas, Chia-Ying Lee, Ritwik Gupta, Andrea Jenney, Tom Beucler

TCBench is a benchmark for evaluating global, short to medium-range (1-5 days) forecasts of tropical cyclone track and intensity. It builds on the IBTrACS observational dataset and includes state-of-the-art dynamical and neural weather models. Designed for accessibility, TCBench helps AI practitioners tackle domain-relevant TC challenges.

arXiv preprint paper github
Artwork for TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale