Welcome to my portfolio.
I've studied and implemented automated decision making in both tech and academia.
Here are my top projects.
Multi-Intervention Sequential Decision Modeling
Built an end-to-end experimental pipeline to study reinforcement learning in sequential decision settings where multiple interacting interventions must be selected in combination at each timestep.
Demand Estimation for Dockless Bike-Share
Built an end-to-end machine learning system to estimate demand for dockless bike-share from censored usage data, supporting city planners on bike-share policy decisions.
Disturbance-Based Local Search for VRPs
Built a top-performing, large-scale vehicle routing problem solver using a disturbance-based local search approach I designed.
Dynamic Speech-to-Text Parameters for Alexa
Implemented reinforcement learning agents to dynamically set key search parameters in Alexa's speech-to-text pipeline and reduced latency by 30%.
Under Amazon NDA
Education
Columbia University
MS in Operations Research (PhD Coursework)
Obtained October 2025, qualified for PhD
Brown University
BS in Applied Math & Computer Science
Graduated with honors in May 2023
Experience
Short-term internship in cybersecurity at Margin Research.
Completed doctoral coursework and earned an accelerated MS in operations research in the IEOR department, qualifying for the PhD. Conducted research under Prof. Lily Xu focused on decision-making systems for healthcare applications.
Built an end-to-end system for modeling and evaluating multi-intervention healthcare decisions, enabling intervention-level value estimation using reinforcement learning and mentoring a student contributor in its development.
Served as Head Teaching Assistant for a graduate machine learning course (70+ students) under Prof. Christian Kroer, holding office hours and creating the official exams.
Delivered production-grade full-stack features and built a data validation framework to safely support a 90% load increase for an international hiring software release.
Built and deployed an end-to-end machine learning demand modeling system for dockless bike-share planning under Prof. Alice Paul, translating usage data into actionable guidance for local transportation policy, and received recognition as an INFORMS Undergraduate Operations Research Prize finalist in 2023 for an honors thesis in the applied math department on this work.
Implemented reinforcement learning-based tuning in Alexa's audio processing pipeline, achieving a 30% reduction in speech-to-text latency and earning a full-time return offer.
Restructured the syllabus and built all assignments and exams for the course Discrete Structures and Probability under Prof. Michael Littman, leading a team of teaching assistants to support 100+ students.
Translated user needs into a routing optimization roadmap and redesigned the backend routing algorithm for bike navigation startup Pointz, reducing their routing time by 50%.