MCMC

No problem. While working on my own research, I have come across many high-quality papers from Yale faculty and Yale graduates on ML as it relates to high-dimensional data analysis, bioinformatics, and statistical genetics. When I applied to Ph.D. programs a couple years ago, I heard from statistics and computer science professors that I should apply to Yale if I wanted to study ML and apply it to these kinds of areas. These professors mentioned that Yale Stats had a great reputation in ML because the department was actively supporting that type of research while also encouraging collaboration with researchers from Yale’s CS department. Best of luck in making your decision.

I would choose Yale Stats if I were in your position. I expect you to have a much better chance of achieving your specific career goal – getting a satisfying job in Asia -- after graduating from Yale Stats than UW Biostats. I think you’d have more flexibility during your job hunt in Asia because of Yale’s outstanding placements in non-academic positions in Asia, which may end up suiting your interests better than academia since you say you have interest in a wide variety of fields. Even if you end up seeking a senior faculty member to advise your ML PhD dissertation to enter academia in Asia, I still believe Yale would be the superior option. Although there are more faculty at UW working on ML than at Yale, ML is more ingrained in Yale’s department (I think the Chair of Yale Stats works on ML) and ML has been a historical strength of Yale’s. The reputation of these departments probably lags a few years behind reality, so even if UW continues hiring great ML researchers while you are a PhD student, you may not reap the rewards in time to give you superior job placement in Asia.