[D] Physicist-turned-ML-engineer looking to get into ML research. What's worth working on and where can I contribute most?

Reddit r/MachineLearning / 4/3/2026

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Key Points

  • A physicist with a PhD in string theory, later moving through quant finance and building/selling an ML startup, is looking to re-enter independent ML research and needs guidance on where to contribute most.
  • The post emphasizes transferable strengths from physics—differential geometry/topology, numerical PDE/SDE methods, and (statistical) quantum field theory—plus extensive production engineering experience.
  • The author explicitly asks the ML research community for advice on what research directions are promising and how to navigate today’s fast-moving ML landscape.
  • This is framed as a community-driven question rather than an announcement, highlighting gaps in the author’s current understanding of modern research compared with their prior research and engineering exposure.

After years of focus on building products, I'm carving out time to do independent research again and trying to find the right direction. I have stayed reasonably up-to-date regarding major developments of the past years (reading books, papers, etc) ... but I definitely don't have a full understanding of today's research landscape. Could really use the help of you experts :-)

A bit more about myself: PhD in string theory/theoretical physics (Oxford), then quant finance, then built and sold an ML startup to a large company where I now manage the engineering team.
Skills/knowledge I bring which don't come as standard with Physics:

  • Differential Geometry & Topology
  • (numerical solution of) Partial Differential Equations
  • (numerical solution of) Stochastic Differential Equations
  • Quantum Field Theory / Statistical Field Theory
  • tons of Engineering/Programming experience (in prod envs)

Especially curious to hear from anyone who made a similar transition already!

submitted by /u/BalcksChaos
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