Brooklyn, New York, United States
I am a physicist at heart and a data scientist by profession. I currently work as a Deep Learning Researcher at NYU DICE Labs. My research focuses on developing a deep learning model for solving continuous dynamical systems. I am also a Teaching Assistant for the Natural Language Processing course (CSCI-UA 0469) at NYU. I love poetry and hiking. I am exploring movies and creative storytelling.
Course: CSCI-UA 0469 - Natural Language Processing, by Prof. Adam Meyers 1. Provided guidance and mentorship to 18 undergraduate teams for the course projects. 2. Conducted one-on-one tutoring sessions, graded assignments, and provided feedback to help students learn NLP concepts.
Developing a Foundation Model for solving Continuous Dynamical Systems @ DICE (Data, Intelligence, and Computation in Engineering) Lab 1. Developed comprehensive dataset of 10K+ fluid simulations (8TB) (FlowBench, akin to ImageNet), and evaluated performance of 13 state-of-the-art deep learning models, providing insights into their performance and limitations 2. Created dataset for bubble dynamics modeling (MPFBench) using 10K GPU simulations (10TB) and developed multi-GPU training pipeline for sequence-based neural operators with less than 10% error in time-series forecasting
Worked as a Data Engineer, Data Scientist and Machine Learning Engineer for various Fortune 500 clients
Co-authored two papers on nonlinear vibrations and discrete breathers in triangular lattices, utilizing computational simulations refuting claims about alpha-uranium heat capacity and analyze macroscopic properties https://doi.org/10.1007/s11071-020-06015-5 https://doi.org/10.1016/j.cnsns.2022.106541
Improved and modified the Transport Measurement System to increase the system's Data Accusation Rate by 100x for exploring the quantum nature of electron