Physicist (PhD 2017, hep theory/phenomenology) currently working as a researcher (in the context of AI and robotics) in a Silicon Valley startup.
I am greatly excited about ideas at the interface of probabilistic inference and quantum/statistical physics. On the one hand, these should help create better ML models/algorithms; on the other hand, I believe that tools from probabilistic inference will help better understand emergent phenomena in complex systems. The former is what I'm focusing on right now, the latter, I think might take a couple of decades.
When graduating with a PhD and thinking about what I'd like to do next, I didn't think I was a good fit for life on the academic track (post-doc, tenure-track, etc), given the kind of questions I wanted to think about and the manner in which I wanted to pursue them. I also wanted to gain some experience writing software and applying ML to real world problems (as a "regularizing" effect on my theorizing), so I took the path I did.
I've come to realize that I'm a researcher at heart, and it's difficult for me to not spend time exploring new ideas. I just need to find the time and space to do that, and I'm trying to structure my life so that I can.
> Also I have noticed a growing trend of physicists becoming data scientists post phD. Although I understand the money factor, are there any other reasons for this as well?
I think this has always been the case, at least as far back as software in the '90s and then finance and now "data science" added to the mix. The typical physics education/training makes one a generalist with a broad background in problem solving and mathematical tools, and a flexible mindset, so that one can adapt to be effective on the problem du jour, while there is a dearth of specialists with the specific skills necessary. I imagine this overarching trend continue to be true going forward as well.
I am greatly excited about ideas at the interface of probabilistic inference and quantum/statistical physics. On the one hand, these should help create better ML models/algorithms; on the other hand, I believe that tools from probabilistic inference will help better understand emergent phenomena in complex systems. The former is what I'm focusing on right now, the latter, I think might take a couple of decades.
When graduating with a PhD and thinking about what I'd like to do next, I didn't think I was a good fit for life on the academic track (post-doc, tenure-track, etc), given the kind of questions I wanted to think about and the manner in which I wanted to pursue them. I also wanted to gain some experience writing software and applying ML to real world problems (as a "regularizing" effect on my theorizing), so I took the path I did.
I've come to realize that I'm a researcher at heart, and it's difficult for me to not spend time exploring new ideas. I just need to find the time and space to do that, and I'm trying to structure my life so that I can.
> Also I have noticed a growing trend of physicists becoming data scientists post phD. Although I understand the money factor, are there any other reasons for this as well?
I think this has always been the case, at least as far back as software in the '90s and then finance and now "data science" added to the mix. The typical physics education/training makes one a generalist with a broad background in problem solving and mathematical tools, and a flexible mindset, so that one can adapt to be effective on the problem du jour, while there is a dearth of specialists with the specific skills necessary. I imagine this overarching trend continue to be true going forward as well.