- Research Program Mentor
PhD candidate at Princeton University
BioI'm a PhD student in machine learning and a strong believer in the value of breadth > depth. AI pulls from an enormous number of fields: probability, linear algebra, algorithms, computational neuroscience, statistical physics, information theory, etc. etc. etc. I like to build ML systems with a bit of whimsy and flair, and my favorite part of the experience is forming spicy (and often novel) connections between ideas in wildly different disciplines. Please reach out if you're interested in AI and like to let your personality shine through in your work! My technical background includes a BS/MS in Computer Science from UPenn and a business degree from Wharton. I've worked in software at EA Games, Palantir, and IBM, and I used to be an adjunct lecturer for deep learning at UPenn. I currently moonlight as a quant at Vise (employee #4), which recently became a unicorn startup.
Robot Ed Sheeran: Making an AI Composer
Current state-of-the-art ML for music (see: OpenAI, Google Magenta) is bad at learning long-form structure. I believe this is because their approach of swallowing and vomiting MIDI files does not reflect the vast amount of prior information we have as humans: we have ears that pick up on the subtle ways that pitches interact, and we're hard-wired to understand linguistic structures. Let's build a better AI with these inductive biases and generate some funk!