•1 min read•from Machine Learning
[D] Physicist-turned-ML-engineer looking to get into ML research. What's worth working on and where can I contribute most?
Our take
Transitioning from a successful career in physics and machine learning to independent research offers an exciting opportunity to explore innovative solutions in the ML landscape. With a PhD in theoretical physics and extensive experience in both quantitative finance and product development, you possess a unique skill set that can significantly contribute to the field. As you seek direction, consider areas where your expertise in differential geometry, topology, and advanced numerical methods can intersect with current research trends.
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!
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