Nov 2021 - Present | Bellevue, WA

• Designed and ran large-scale A/B and quasi-experiments to evaluate new product features, directly informing multi-billion-dollar revenue decisions.
• Defined and operationalized north-star metrics for ad quality and user experience that became standards across product and engineering teams.
• Built production data pipelines in SQL and Python to integrate surveys with experimentation, enabling self-serve analytics at org scale.
• Partnered with product and engineering on trust and safety systems to balance fraud risk, implementation cost, and customer experience.
• Deployed large language model pipelines to classify millions of pieces of user feedback, creating new quality signals for ranking models.
• Delivered dashboards that merged behavioral data, surveys, and ML outputs, adopted by hundreds of stakeholders for decision-making.
• Served on the Ads core leadership team, shaping strategy and execution for a group responsible for a significant share of company revenue.

Mar 2021 - Nov 2021 | Berkeley, CA

• Built software classifiers to automatically detect poor-quality retinal scans, improving diagnostic reliability.
• Collaborated with hardware engineers to embed the detection system into devices, enabling real-time rejection of invalid scans.
• Contributed to patent US20250057414A1 on retinal disease detection methods (https://patents.google.com/patent/US20250057414A1).

Aug 2015 - Mar 2021 | Austin, TX

• Designed computational models of human perception using Bayesian inference, machine learning, and large-scale neuroimaging data.
• Built reproducible pipelines and simulation frameworks to study cognitive performance and visual attention under uncertainty.
• Published first-author research in venues such as Current Biology and AAAI, translating findings into perception and decision-making algorithms.
• Partnered across neuroscience, psychology, and computer science to prototype ML-driven methods and secure multidisciplinary funding.