Hi everyone,I am curious what is the general view on the following PhD programs in terms of preparing for a quant career. MIT Operations Research: optimization, stochastic analysis, statistical learning. I guess MIT is very strong in optimization but little concern about their work on stochastic analysis and statistics. Plus, their industrial focus appears to be in industrial production and transportation and not much in finance. Though there are some big names working on finance-related research: Andrew Lo, Jaillet, Bertsimas (C.Merton is nearby but I dont know if he supervises PhD student anymore). So how employable are those quants with background in statistical learning + optimization (no background on numerical PDE + stochastic calculus)?Princeton Operations Research & Financial Engineering: stochastic calculus, stochastic control, financial engineering (derivatives pricing, risk analysis, portfolio optimization). They look strong in the stochastic department but their PhD students seem to take such long time to graduate (5+ years. I dont know whether that is their policy). NYU Applied Mathematics: scientific computing, numerical methods for PDE, mathematical finance. They are the top applied math department in US and in NYC but perhaps too theoretical. Brown Applied Mathematics: stochastic PDE, numerical methods for PDE, scientific computing. They used to be very strong (best?) in application of stochastic calculus in finance but, probably, not anymore (I dont know if this should matter to anyone besides academia). Plus, it takes too long to graduate from their program.So what you guys think about those PhD programs? I greatly appreciate any comment or advice here.
Last edited by DrFaust
on March 3rd, 2011, 11:00 pm, edited 1 time in total.