Neal Parikh

Adjunct Associate Professor (current: AI: A Survey for Policymakers)
School of International & Public Affairs
Columbia University

I’m a computer scientist with a background in machine learning, optimization, and statistics. Most recently, I was the first Director of AI for New York City, working on AI policy, legislation, and ethics, often in collaboration with other city governments internationally. I also led the work on the NYC AI Strategy.

Before that, I was a quant at Goldman Sachs, working on a portfolio optimization system managing $100 billion in investments across around 1000 funds. I introduced the use of machine learning and natural language processing models and designed what might now be called “MLops” to make the platform more efficient and robust. I then did my Ph.D. at Stanford on machine learning and convex optimization, where two research monographs I wrote are now standard references, with over 30,000 citations. A related paper on conic optimization led to the solver SCS, which ships with CVXPY and is downloaded millions of times per month.

While at Stanford, I co-founded SevenFifty, a platform for the wholesale wine and spirits industry used by over 100,000 restaurants, bars, and retailers, which was acquired in early 2022. I currently teach AI: A Survey for Policymakers at Columbia’s School of International & Public Affairs, drawing on my experience in city government; my previous courses are more technical, like a graduate course on machine learning at Cornell. Please feel free to get in touch if any of the above interests you.