|
Vatsal Baherwani Hello, welcome to my website! About MeI am a PhD student at New York University, advised by Pavel Izmailov and Andrew Gordon Wilson. I am grateful to be supported by the NSF Graduate Research Fellowship. I previously graduated with a bachelor's degree from the University of Maryland, where I was fortunate to work with Pete Kyle (who first introduced me to academic research), Abhinav Shrivastava, Ashwinee Panda, and Tom Goldstein. This summer I will be at Q Labs as a research intern in San Francisco. In summer 2025, I was a visiting researcher at the Center for Human-Compatible AI working with Raj Movva and Emma Pierson on practical applications for ML interpretability. Check out my blog to learn more about me. Email | Google Scholar | Twitter | GitHub | LinkedIn |
|
Publications |
|
DynaGuard: A Dynamic Guardrail Model With User-Defined Policies
Monte Hoover, Vatsal Baherwani, Neel Jain, Khalid Saifullah, Joseph Vincent, Chirag Jain, Melissa Kazemi Rad, C. Bayan Bruss, Ashwinee Panda, Tom Goldstein ICLR 2026 paper | code | models Guardian models are useful for monitoring the safety and quality of deployed LLMs, but prior models fail to properly enforce domain-specific guardrails. We develop a dynamic guardian model that adapts to arbitrary user-specified rules and constraints at runtime. |
|
Dense Backpropagation Improves Training for Sparse Mixture-of-Experts
Ashwinee Panda*, Vatsal Baherwani*, Zain Sarwar, Benjamin Therien, Supriyo Chakraborty, Tom Goldstein NeurIPS 2025 paper | code One of the main challenges in training a very sparse mixture-of-experts model is that you only get to update a small subset of your parameters in each optimization step. We develop a method to train inactive experts by estimating their gradients, which leads to significant improvement in training speed with negligible computational overhead. |