MEET THE Mentor

Philip Maymin

Dr. Philip Maymin is a portfolio manager and a director of asset allocation strategies at Janus Henderson. He is also the endowed Schramm chair of analytics and the MSBA program director at Fairfield Dolan. He is also a contributor to Forbes.com on AI and finance.

He is the CTO for Swipe.bet, an instructor for Analytics.bet, and an advisor to AI and Faith, and has been an Insight Partner with Essentia Analytics, an advisor to Athletes Unlimited, an affiliate of the Langer Mindfulness Institute, and an analytics consultant with several NBA teams. He was also the founding managing editor of Algorithmic Finance, the co-founder and co-editor-in-chief of the Journal of Sports Analytics, and the co-founder and co-editor-in-chief of the Journal of Sports Betting.

He has been a portfolio manager at Long-Term Capital Management, Ellington Management Group, and his own hedge fund, Maymin Capital Management. He has also been a policy scholar for a free market think tank, a Justice of the Peace, a Congressional candidate, a professor of finance and risk engineering at NYU, a professor of analytics and finance at the University of Bridgeport, and an award-winning journalist and columnist.

He was a finalist for the 2010 Bastiat Prize for Online Journalism. He was awarded a Wolfram Innovator Award in 2015. He won the Wolfram Live Coding Challenge in 2016 and second place in 2018, and he won the Wolfram One-Liner Competition in 2015, 2016, 2018, and 2019. He was named one of the Top 50 Data and Analytics Professionals in the US and Canada by Corinium in 2018. He is the only person to have won both the Grand Prize for Best Research Paper (2018) and the Hackathon (2020) at the MIT Sloan Sports Analytics Conference.

Dr. Philip Maymin holds a Ph.D. in Finance from the University of Chicago (dissertation chair: Richard H. Thaler), a Master's in Applied Mathematics from Harvard University, and a Bachelor's in Computer Science from Harvard University. He also holds a J.D. and is an attorney-at-law admitted to practice in California.

Courses

COMPUTER SCIENCE

AI from First Principles to Practice

Artificial intelligence is moving fast, but the principles behind it are simple enough to learn, test, and use. This course is about turning that simplicity into power. We ask what today’s models can and cannot do, and then we try it ourselves.

You will learn how to get models to answer, to argue, to draw, to sing, to film, to play. You will see where they shine, where they stumble, and how to chain them together into something sturdier than a single prompt. Each week is a mix of short talk, live build, and peer critique. By the end, you will have two things: a computational essay that tracks your thinking, and a working artifact that shows it. It might be a workflow, an agent, an app, a demo, a short film, a song. Whatever it is, it will be yours, and it will work.

Ready to explore new ideas?

Take the next step in your intellectual journey.

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