Which among {CS, EE, OR, Stat} should be my choice for PhD? Interest = optimization, machine learning

[Desi_Mama]

I consider myself a very mathematically bent person (theorem-proof basis) and my primary interest is in convex optimization. An exciting application of the same which fascinates me is machine learning and equivalent names in other fields. I would like to pursue a PhD in the same and would greatly appreciate suggestions regarding which department is best suited for an optimization+data work.

I have a bachelors in mechanical engineering with industrial engineering focus. Hence all the above departments are new to me, barring OR. I have of course done courses on optimization, algorithms, stochastic processes, introductory machine learning, networks etc.

In CS, the only areas that interest me are artificial intelligence and algorithm design (combinatorial optimization). In EE, I am interested in signals and systems. In OR, I would look to focus on mathematical optimization with applications in data.

After PhD, my intention is to work in industrial research labs (like Microsoft, Google, IBM, Yahoo etc). I know that majority of candidates here are CS and EE grads. How OR and Stats fare in job market? Would I be eligible for the same jobs regardless of what I do my PhD in (among the departments listed). I am personally most comfortable with OR, followed closely by EE due to large overlap with my undergrad coursework. I may find it hard to get into the *top* CS programs, whereas I might stand a much better chance for their IEOR or EE programs.

 

Do share your thoughts

[Desi_Mama]

Anyone?