Summary
Kulin Shah is a Ph.D. student and research assistant at UT Austin focused on the theoretical and empirical foundations of generative models, with a recent Outstanding Paper Award at ICML 2025 for work contrasting diffusion and autoregressive language models. He has a decade of research experience spanning Microsoft Research India, Google, and IIIT Hyderabad, and has published across top venues including NeurIPS, AISTATS, AIES, UAI and AAAI. His recent projects target improving reasoning, planning, and search in large language models and produced a dataset adopted in BIG-Bench Extra Hard and used to evaluate major models like Gemma 3 and Gemini Diffusion. Comfortable bridging theory and applied systems, he brings experience in representation learning, fairness, and practical engineering from internships and a startup backend role.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at Austin
High School, 99.51 Percentile, High School, 99.51 Percentile at Divyapath Campus, Ahmedabad
Bachelor of Technology, Computer Science, Bachelor of Technology, Computer Science at International Institute of Information Technology
English, Hindi, Gujarati