Physicist-turned-ML-engineer Seeks Advice on ML Research
A physicist-turned-ML-engineer with a background in theoretical physics and engineering is looking to transition into ML research. They are seeking advice on the current research landscape and areas where they can contribute most.
Why it matters
This post highlights the potential for cross-pollination between fields like physics and ML, and the value that researchers with diverse backgrounds can bring to the ML research community.
Key Points
- 1The author has a PhD in string theory/theoretical physics and experience in quant finance and building an ML startup
- 2They have skills in differential geometry, PDEs, SDEs, quantum field theory, and extensive engineering/programming experience
- 3The author is trying to find the right direction for independent ML research after years of focus on building products
Details
The author, a physicist-turned-ML-engineer, is looking to transition into ML research after years of building products. They have a strong background in theoretical physics, including a PhD in string theory from Oxford, as well as experience in quant finance and building and selling an ML startup. The author believes their unique skill set, which includes expertise in areas like differential geometry, PDEs, SDEs, and quantum field theory, could be valuable in ML research. They are seeking advice from experts on the current research landscape and areas where they could contribute most effectively.
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