معرفی
Tanya Marwah is a Research Fellow at the Simons Foundation, collaborating with Polymathic AI. She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute.
Her research bridges Machine Learning and Scientific Computing, focusing on generative modeling, inverse problems, and building scientific agents. Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators, memory mechanisms, and edge embeddings in GNNs.
Recent publications highlight trends in PDE solvers via LLMs, cross-modal adaptation, and implicit regularization in SGD. She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.
