Hi! I'm Neil,

My most recent work has been at Nominal, a startup that focuses on automating the hardware test and validation lifecycle. At Nominal, I focused most on our data systems and processing, particularly on data ingest and internal computation. Before Nominal, I got a taste of policy at the United States Department of Commerce, where I used machine learning to help shape macroeconomic policy tailored to alleviate geographic inequality.

In addition to industry work, I've also done some cool research! One recent project I worked on centered on modelling polymer crystallization using reinforcement learning, presented at the ICML 2023 Workshop on Structured Probabilistic Inference and Generative Modeling. Recently, I've been working on leveraging natural language processing to analyze the spread of misinformation online.

Recently, I've also found myself teaching a lot. This year, I co-taught a machine learning course to graduate students at La Universidad de Ingeniería y Tecnología in Peru, with a focus on combining theory with actionable projects and startups. Last, year I taught web design and machine learning to undergraduate students and young professionals in Jordan with Injaz :)

My current interests are in computer security and natural language processing, and I'm excited to work on projects that apply them to a policy context.