I build human-in-the-loop ML systems for evaluating and improving GenAI quality and safety. I combine technical depth in data pipelines and scalable infrastructure with experience designing human-facing experiments and extracting reliable signals from noisy data.
Technical lead defining and executing the evaluation strategy for Stitch Fix’s inventory purchasing model, a company-critical system driving millions of dollars in purchasing decisions.
Set priorities for impactful evaluation metrics, built backtesting infrastructure, and surfaced the team’s first daily model performance metrics. Implemented data-backed model improvements to unblock rollout.
Trained computer vision models to predict the performance of new merchandise and productionized the company’s first internal tooling for image-based deep learning.
Investigated methods for improving human–agent collaboration with minimal human data, training hundreds of reinforcement-learning agents and running a human-subject study on Overcooked.
Found that population training fails to improve coordination with humans; led a research team spanning faculty, graduate, and undergraduate collaborators.
Researched the intersection of computer vision and human perception, leading to peer-reviewed publications in top venues (ECCV, CVPR, CHI).
Built a computational model of video memorability, deploying an interactive web platform to collect 10K human judgments and training a model to predict memory decay over time.
Developed novel datasets, interfaces, and models for human visual attention, including the first multi-duration saliency model and a toolbox of interfaces for crowdsourcing attention data.
