Neal Parikh

Director of Artificial Intelligence
Mayor’s Office of the Chief Technology Officer
New York City

I’m a computer scientist and startup founder with a background in machine learning, convex optimization, and statistics. I’m currently Director of AI for NYC in the Mayor’s Office of the CTO, a newly created position that touches on a wide range of topics related to AI, including policy and legislation, internal technical advisory work, and external partnerships. An important aspect of the work is the ethics and responsible use of AI, and I also often work with other city governments internationally.

I’m interested in technology, math, business, and policy and have worked in a diverse mix of roles in the last 15 years. Long ago, I was a quant at Goldman Sachs and built a portfolio optimization system used to manage $100 billion in investments across around 1000 funds. Among other things, I introduced the use of machine learning and natural language processing models as well as designing what now might be referred to as “MLops” to make the portfolio optimization platform and process more efficient and robust.

After that, I did my Ph.D. at Stanford on machine learning and optimization, and two research monographs I wrote are now standard references, with over 15,000 citations; you can see more of my academic work here, which focused on distributed and decentralized convex optimization for large-scale machine learning and statistical problems. The work has strong theoretical underpinnings, but there was a strong focus on applications and the interaction between algorithms and systems throughout.

While at Stanford, I co-founded a startup, SevenFifty, a platform and marketplace for the wholesale wine and spirits industry. SevenFifty works with around 80,000 restaurants, bars, and retailers across the US, has raised over $10 million in financing from leading investors, and does 8 figures in annual revenue with over 80 employees. You can read more on this here.

I also enjoy teaching and speaking, and most recently taught a graduate course on machine learning at Cornell. Before working for the NYC government, I spent a summer at the Aspen Institute exploring a new interest in technology policy and public service, which helped pave the way to my current position.

Please feel free to get in touch if any of the above interests you. (You can also follow me on Twitter, though so far I don’t post much.)