Hello! I am a PhD student at the Machine Learning Department, CMU, where I am co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton. I am interested in generative modeling, dynamical systems/sequence modeling and the use of AI for scientific discovery (AI4Science) and mathematics in general.

Most of my work involves learning about and then applying knowledge from different domains like differential equations (ODEs/PDEs/SDEs), statistics and machine learning towards a more fundamental understanding of how ML models learn and also use these insights to design better ML algorithms.

In the Summer of 2022, I was an intern at the Blueshift Team at Google, where I had the pleasure to work with Guy Gur-Ari, Jascha Sohl-Dickstein, Yasaman Bahri and Behnam Neyshabur and worked on understanding the out-of-distribution generalization of large language models using synthetic data.

These days I am thinking mostly about modeling and sampling discrete data like graphs and language, and modeling temporal data—with a special focus on PDEs. Feel free to reach out to me if you are interested in any of these topics!!

Previously, I completed my Masters in Robotics at CMU, during which I was advised by Prof. Kris Kitani and was a recipient of the Siebel Scholarship, 2019. I did my Bachelors with Honors in Electrical Engineering from Indian Institute of Technology, Hyderabad where I worked with Prof. Vineeth N. Balasubramanian.

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Here is my Github, and Google Scholar. The easiest way to reach me is through email, my id is tmarwah [at] andrew [dot] cmu [dot] edu.

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Publications

Deep Equilibrium Based Neural Operators for Steady-State PDEs
Tanya Marwah^*, Ashwini Pokle^*, J. Zico Kolter, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski
Neural Information Processing Systems (NeurIPS), 2023

Neural Network approximations of PDEs Beyond Linearity: A Representational Perspective
Tanya Marwah, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski
International Conference on Machine Learning (ICML), 2023

Disentangling the Mechanisms Behind Implicit Regularization in SGD
Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary C. Lipton
International Conference on Learning Representations (ICLR), 2023

Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Tanya Marwah, Zachary C. Lipton, Andrej Risteski
Neural Information Processing Systems (NeurIPS), 2021 (Spotlight)

Attentive Semantic Video Generation using Captions
Tanya Marwah^*, Gaurav Mittal^*, Vineeth N Balasubramanian
IEEE International Conference on Computer Vision (ICCV), 2017

Sync-DRAW: Automatic video generation using deep recurrent attentive architectures
Tanya Marwah^*, Gaurav Mittal^*, Vineeth N Balasubramanian
25th ACM international conference on Multimedia (ACM-MM), 2017 (Oral)