Publications: ICML'19, NeurIPS'19, AISTATS'23, ICML'23, NeurIPS'24, NeurIPS'25; Awards: Winner of the 2025 Communicate Your Science & Engineering (CYSE) contest; Projects: Key member of the ALICE team, core contributor to EconML.
Research Experience
Work Experience: Senior Data and Applied Scientist at Microsoft Research, core contributor to EconML, an open-source Python library for causal machine learning. Internships: Netflix and Brookhaven National Laboratory.
Education
Degree: PhD in Computer Science; School: Cornell University (Cornell Tech); Advisor: Prof. Nathan Kallus; Graduation: 2026; Major: Computer Science. Additionally, holds an A.B. in Physics and Mathematics from Harvard University.
Background
Research Interests: Developing machine learning methods for causal inference, reinforcement learning, and decision-making under uncertainty. Background: PhD Candidate in Computer Science at Cornell University (Cornell Tech), DOE Computational Science Graduate Fellow. Goal: To develop trustworthy and effective models for real-world, data-limited, and high-impact settings such as healthcare, scientific research, and AI-driven decision-making.
Miscellany
Interests: Board member of the Summer Science Program (SSP), supporting its mission and helping shape its future.