Jennifer Rexford’s advice for new grad students is also good for old grad students and new post-docs. See it on the Freedom to Tinker Blog.
Monthly Archives: September 2010
School is starting up, and I’m absolved of teaching duties for my first year as a prof. Very nice, but it is strange to see the trees turning towards fall without classes keeping me busy. I’m going to try to look over the shoulder of the new IHME students. They’re almost all taking an intro biostats course, which is stuff that I should know. I never took a class in it, so I suspect there are gaps in my knowledge… I don’t even know what I don’t know.
Meanwhile, in Colorado, Aaron Clauset is giving a class that I wish I had taken in grad school, Inference, Models and Simulation for Complex Systems. The reading list is full of things I like, so maybe I’ll pretend I’m still a student and read the ones I haven’t yet.
I’ve been flipping through the titles of SODA acceptances listed on the blogs, and wondering if I’m losing touch with TCS research. It’s a good chance for me to think about what algorithms (discrete or otherwise) have been really big in the health metrics work I’ve been doing recently.
- Markov Chain Monte Carlo (MCMC): This is the workhorse algorithm for me when fitting statistical models. There are a few MCMC-sounding titles in the SODA list; does anything have an exciting new step method to speed up my life?
- Mixed Integer Programming (MIP): This classic formulation of operations research must make an appearance in some of the approximation algorithms or other analysis in SODA. Is there any work there that’s taking it on directly?
- Stochastic Programming: There was a lot of excitement about two-stage stochastic programming a few years ago, but the fad seems to have died down in theory land. Too bad for me, because two-stage formulations are not really what I need, and my StoPro needs are growing.
- Random Forests: I really didn’t get enough education on machine learning in grad school. What I do know is very much on the theoretical side of the spectrum. But this Random Forests business has been pretty promising so far, and I just made a bet 10-to-1 that it will out-perform an ad-hoc method for verbal autopsy. I believe the odds, but I wasn’t paying enough attention to the stakes…
- Nonlinear optimization: I love MCMC, but I don’t love waiting around for my chains to mix. The alternative approach to statistical modeling, where you make due with a maximum likelihood estimate is starting to look pretty appealing. This is pretty outside of the SODA realm. I tried to convince Steven Rudich to include Newton’s Method in his course “Great Theoretical Ideas in Computer Science” some years ago, but I didn’t succeed.
- Automatic Differentiation: If I’m getting into nonlinear optimization, I will at least be a user of automatic differentiation, since the nonlinear optimizer wants to know the gradient, and I’m sure not going to be computing it if I don’t have to be.
So I guess my research needs are not squarely within the SODA realm. But they are not disjoint from it either. I’m still touching theory, if not totally in touch. Maybe one day soon I’ll even have time to prove something.
It’s been a busy two weeks since I got back in town. The PBFs who went to “the field” for their summer abroad have returned with lots of fun and interesting stories. A new batch of PBFs and PGFs has arrived, bringing IHME to it’s planned capacity of around 100 heads. And I’ve been getting deeply into experimental analysis of a gaussian process regression technique, much like the one we used for estimating child mortality rates.
Maybe I’ll work on it publicly here on healthy algorithms. I’ll see if that seems too boring as I proceed.
For the moment, I’m just looking for reading suggestions. I was very inspired by David Johnson’s papaer A Theoretician’s Guide to the Experimental Analysis of Algorithms when I read it, but that was years ago. I’m going to have to read it again. What else do you recommend like this?