About me

I work on machine learning and empirical analysis for questions about supply chains, firms, and markets. My current research measures supply-chain risk in corporate disclosures and examines its relationship with stock-market reactions. My projects also examine how modeling and evaluation choices affect prediction, from chronological validation to numerical optimization.

My background is in mechanical engineering and manufacturing. At Apple, I worked on product design, validation, and failure analysis. After leaving Apple, I designed a robotic system for performing maintenance on autonomous vehicles. Shortly after, I founded Reframe, an AI procurement tool for mechanical hardware, as part of Y Combinator’s W26 batch.

I am seeking research associate and fellowship opportunities in applied machine learning and operations research as I prepare for doctoral study.

Selected work

• Supply-chain language and market reactions: A 2010–2019 study combining dictionary-based NLP, event studies, and an investigation of how tied scores affect portfolio comparisons. Research overview
• Predicting Hacker News engagement: Chronological evaluation of prediction models on a corpus of 4.7 million posts, with an emphasis on data quality and information available at submission. Selected projects
• Reconstructing Adam experiments: NumPy implementations of five optimizers and manual backpropagation, including an investigation of results that differ from the original paper. Code and experiments

I hold a B.S. in Mechanical Engineering from Cornell University.