Neal Parikh

I’m a computer scientist and startup founder with a background in machine learning, convex optimization, and statistics. Most recently, I was 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 often worked with other city governments internationally. I also led the work on the NYC AI Strategy.

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 convex optimization, and two research monographs I wrote are now standard references, with around 20,000 citations. My academic work focused mostly on distributed and decentralized convex optimization for large-scale machine learning and statistical problems; you can see more here. The work has strong theoretical underpinnings, but the main focus was on applications and the interaction between algorithms and systems. I also wrote a few papers more explicitly about the interaction between optimization and systems (automatic code generation, conic solvers, etc).

While at Stanford, I co-founded a startup, SevenFifty, a platform and marketplace for the wholesale wine and spirits industry. I served as CTO, though my role was essentially that of a technical co-CEO, for lack of a better term. SevenFifty works with around 80,000 restaurants, bars, and retailers across the US, has raised around $30 million in financing from leading investors, and does 8 figures in annual revenue with over 100 employees. You can read some reflections here or check out the company site.

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 an interest in technology policy and the broader social impacts of AI, 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.)