Grad School

• You are really interested in a topic and want to pursue it more…understand the intricacies, etc.
  • Is that what *you* really want to optimize? It's kinda built in for you, but basically is that God's will for you? :) idk yet…

• Math is a language for describing things. A bad design/assumption in one language might look prettier in another language, but it's still bad. Our world around us is physics, and humans have a built-in intuition for that, especially physicists/engineers! Use that too when you're designing a solution.
• Can't advance in corporate into established companies tackling hard problems unless you have a PhD because you won't get those hard problems (because it's not efficient for them (the PhD managers) to give you them vs a PhD student)
  • Me: However, there's *a ton* of room to apply what smart people have already done into new fields.
  • Me: Do we still need to improve the status quo by solving hard problems. Do we need a ton faster internet or phone signal or processor? I don't know…few thought we'd need more than a 100MB hard drive either for the amount of work we've put into it. So..yes? Maybe I just don't want to do the hard problems that take a ton of math.
• Also, just “I can program” is becoming less and less of a unique thing these days, especially with outsourcing. Will be interesting to see what happens.
• Academia, of course you need a PhD to teach / research.

• Asians are really smart, especially at math. Maybe not as much in innovation?

• PhD looks good by your name for promoting your own ideas. Says you know a few things.
• Universities can get very research-oriented, up until the point that if your PhD isn't bringing in grant funding for the university, you won't be looked upon very nicely.
  • Research-oriented is fine though, just optimized for different things. You can crank out theory after theory (most usefully math) of things that look “interesting”, that you want/need to prove rigorously to be correct, which you should, and is especially emphasized at good schools.
  • These places optimize following the grants, which apparently doesn't line up with future product ideas?!
  • Bigger research-oriented schools might have the classes, people, and professors to stimulate you working on an ambitious PhD topic (and help you get a lot of the way there)
• Product-focused mindset can be counter to that. The mathematical derivation and insight is usually a stepping stone to an end-goal of using the algorithm or understanding in a product for people to use. The math you just happen to publish and don't care if it's true in every last case.
  • Smaller universities tend to be more flexible in this regard (and the PhD looks the same!). However, smaller universities might have the same depth of classes as a larger universities, is that right?

My own story:
After I finished my B.A. in CS and Math, I went to work at the Advanced Machine Intelligence Lab at GTE in Massachusetts. At first I was very excited by my pay- check and the great feeling of being independent. I also really enjoyed my area of research at the time: pattern recognition and classification. I was working with frame-of-reference transformations involving eigenvectors of autocorrelation matrices. It was exciting! However I quickly realized that I wanted to know more. I wanted to know why some algorithms pro- duced good results and others didn’t. I wanted to come up with my own algorithms. I worried that I didn’t have enough of a mathematics background to answer my own questions. In sum- mary, I wanted to delve deeper. Everyone around me thought I was odd for wanting these things. I left after 2 years and went to graduate school. That first month of graduate school I looked around and realized that everyone there was just as weird and obsessed as I was, and I knew I had made the right decision.