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Assistant professor at Columbia University. AI for decision-making in planetary health
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Columbia University
Data Scientist
June 19, 2026 – Present
combinatorial-rmab
March 1, 2025 – March 7, 2025
Code for the ICLR 2025 paper: Reinforcement learning with combinatorial actions for combinatorial restless bandits
View Projectonline-rmab
December 8, 2022 – April 11, 2023
Code for the AAAI 2023 paper: "Optimistic Whittle Index Policy: Online Learning for Restless Bandits"
View Projectartificial-replay
September 29, 2022 – January 26, 2023
artificial-replay — GitHub repository
View ProjectrankedCUCB
May 11, 2022 – May 12, 2022
Code for IJCAI 2022 paper "Ranked Prioritization of Groups in Combinatorial Bandit Allocation" including RankedCUCB algorithm
View Projectmirror
September 22, 2021 – May 10, 2022
Robust Reinforcement Learning Under Minimax Regret for Green Security (UAI-21)
View Projectdual-mandate
September 13, 2020 – June 3, 2021
Implement and evaluate algorithms from "Dual-Mandate Patrols" paper, including the proposed LIZARD algorithm
View ProjectPAWS-public
June 12, 2019 – June 3, 2021
Protection Assistant for Wildlife Security - prediction and prescription to combat illegal wildlife poaching
View Projectwebtivity-psychonice
July 8, 2015 – September 23, 2015
webtivity-psychonice — GitHub repository
View ProjectCultural Fit Analysis
The candidate's project history is heavily focused on academic research and personal projects, primarily in the domain of Reinforcement Learning and combinatorial optimization. While this demonstrates strong technical depth in a specific area, the lack of diverse project types (e.g., team-based, production systems, varied industries) makes it difficult to assess broader cultural fit for a typical industry Data Scientist role. The current role at Columbia University as a Data Scientist aligns with the target role, but without further details on responsibilities, it's hard to gauge the full scope of industry relevance. The experience level is listed as 0, which contradicts the depth of projects, suggesting a potential mismatch in how experience is categorized.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's profile primarily highlights technical project work and academic contributions.