Theory, Money, and Learning

Written by: Stephen Hsu

Primary Source: Information Processing

After 25+ years in theoretical physics research, the pattern has become familiar to me. Talented postdoc has difficulty finding a permanent position (professorship), and ends up leaving the field for finance or Silicon Valley. The final phase of the physics career entails study of entirely new subjects, such as finance theory or machine learning, and developing new skills, such as coding.

My most recent postdoc interviewed with big hedge funds in Manhattan and also in the bay area. He has accepted a position in AI — working on Deep Learning — at the Silicon Valley research lab of a large technology company. His compensation is good (significantly higher than most full professors!) and future prospects in this area of research are exciting. With some luck, great things are possible.

He returned the books in the picture last week.

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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