I'm a final-year PhD candidate working at the intersection of causal inference and machine learning. My research focuses on developing robust, interpretable systems that can reason about cause and effect in complex environments.
Education
2021—Present
Stanford University
Ph.D. in Computer Science
Advisor: Prof. Sarah Johnson
2017—2021
Massachusetts Institute of Technology
B.S. in Computer Science and Mathematics
Thesis: Algorithmic Approaches to Causal Discovery
Publications
Experience
Summer 2023
Research Intern — DeepMind
Advisor: Peter Wang
Developed novel algorithms for causal structure learning in reinforcement learning settings
Summer 2022
Research Intern — Google Research
Manager: Elise Brown
Worked on improving robustness of large language models to distribution shifts
Portfolio
Causal Discovery Framework
PythonPyTorchReact
A framework for discovering causal relationships in high-dimensional time series data using state-of-the-art machine learning techniques.