Career Thoughts

  • Last few years I have been thinking of the general direction in which my life has been going and where my career will go from here on.
  • So far there have been only three subjects/topics I have wished I could make a career out of. Things that have interested me and captured my imagination and things, I have felt, I have a natural talent for –
    • Physics in BASE – more specifically statistical physics, rigid body dynamics, fluid mechanics.
    • Economics when I was in Surathkal – The statistical aspect of economics and some of the social theories around it. One of the books that really got me hooked to the subject and showed nuance in the different narratives was ‘Fault Lines’ by Raghuram Rajan. It was brilliant.
    • Statistical Machine Learning in grad school – The focus of my grad school was graphical models and inference procedures. Unlike the other two, came closest to actually build a career around this( probably why it hurt the hardest when I missed it)
  • All three subjects have a strong aspect of statistics to them and machine learning and econometrics borrows a lot from statistical physics. In statistical machine learning especially, a lot of the framing of ideas came from Physics. One can see this by reading the narrative around concepts like partition functions or undirected graphical models. Models like Restricted Boltzman Machine or any Energy Model for that matter use Gibbs distribution to represent distribution over different states. Concepts like variational inference, or markov chain monte carlo came from statistical physics. The language adopted in these fields also comes from Physics.
  • Which is also why people from statistical physics find it easier to move into lucrative careers in Data Science/ Machine Learning and Econometrics.
  • Hopefully I should be able to do something meaningful in one of them. ( By machine learning I don’t mean the big data jobs.)
  • And do something useful here –
  • Unfortunately I feel like I have lost my edge in the last couple of years, like here – and


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