I’m a machine learning researcher focusing on adaptive experimentation, Bayesian optimization, and Gaussian processes. I’m currently a research scientist at Intel. Before this, I was a research engineer at SigOpt, a startup that was acquired by Intel in late 2020, and I moved with the company to where I currently am now within Intel.
I graduated magna cum laude from UC Berkeley in 2015 with a B.A. in applied mathematics. Following that, I received my M.S. and Ph.D. in computer science from Cornell University in 2020, where I received formal training in numerical analysis, numerical optimization, and matrix computations before doing my doctoral research in machine learning. My research in particular concerned optimization under budgetary constraints (money, power, time, etc), which proved to have a number of different practical applications.
I spent some time during my doctorate working at SigOpt (which is how I came to be there post-graduation) and Amazon AWS, where I worked with the SageMaker team on hyperparameter optimization.
I also did a short internship at Cray, an old company many don’t know. Cray makes supercomputers, though in more modern times they have received much less attention because distributed computing has thoroughly dominated the idea of one single brawny machine . Anyway, I had fun at Cray, where I wrote some high performance matrix factorization code (that ended up being about 2 times faster than production for the pivoted QR).