harry_stats

Would people be willing to comment on what they have heard from their respective universities (if anything) with regards to COVID, funding and possible reductions in cohort sizes? It is early in the process, but I wanted to get a sense of how admissions may be affected for next fall.

From what I hear, the covid situation is seriously affecting many universities. Any ideas of how covid will impact the funding situation for students joining stats PhD programs in fall 2021? Will public universities such as UC Berkeley be in a worse situation than private universities such as Duke?

You should be able to get into a lot of your listed schools assuming that you get a quantitative GRE score ~163 or above. UChicago and Stanford are traditionally the tough eggs to crack in terms of masters programs and will be out of reach given your profile, but the rest of the programs have fairly high acceptance rates. Remember you are paying to do your masters and universities make money off their masters students.

It looks like we have similar interests. I did some research and to me these are some of the top schools and faculty in machine learning theory from the statistics side. MIT actually does not seem to have a ton of faculty working in the statistics side of ML theory. Other than Tamara Broderick, faculty in the MIT Statistics & Data Science Center (https://stat.mit.edu/people_categories/core/) seem to focus on topics in extremely mathematical statistics (such as optimal transport, etc.) or high-dimensional econometrics. Stanford statistics (Tibshirani, Hastie, Duchi, Ma) Duke (Dunson, Rudin, Parr) CMU statistics / ML (entire list of statml theory group faculty) - seem to have a lot of junior faculty / more recent hires working in this area Berkeley statistics (Yu, Wainwright, Jordan, Steinhardt, Bartlett) Univ of Washington statistics / biostatistics (Shojaie, Witten, Harchaoui, Kakade) - assuming you place out of the masters level coursework Univ of Michigan statistics (Nguyen, Regier, Tewari)

@StatsG0d - thanks! I did some extra research on programs that would allow me to take fundamental statistics courses early and get involved in ML research after year one. From what I gathered, the best choices as follows, Stanford statistics (Tibshirani, Hastie, Duchi, Ma) Duke (Dunson, Rudin, Parr) CMU statistics / ML (entire list of statml theory group faculty) Berkeley statistics (Yu, Wainwright, Jordan, Steinhardt) Univ of Washington statistics / biostatistics (Shojaie, Witten, Harchaoui, Kakade) - assuming you place out of the masters level coursework Univ of Michigan statistics (Nguyen, Regier, Tewari) Are there are any other top 5-10 programs I am missing here? The other programs in the rankings including UPenn, Harvard, Chicago do not seem to fit the bill in terms of providing the expertise or ability to do meaningful ML research.

Thanks! Two follow up questions: 1. In the older thread, @StatsG0d mentioned that if my interests are just in ML it may make sense to find stats departments that have ML embedded in the curriculum. Any sense of what are the top departments where that is the case? 2. @Stat Postdoc Soon Faculty mentions that you need a masters or research experience to get into a CS program. I really do not have either of these, so not sure that route would work out for me. Any reason why there is such a discrepancy in CS and stats programs admission criteria? My thinking is that a quantitatively strong student (with background in probability, analysis, discrete math, algorithms, etc.) would be able to make meaningful research contributions in either field. Since the ML research in CS depts is actually more applied, it seems like proving research potential there should be easier? It seems that it would be more important to have a masters (proving mathematical maturity or research potential) in statistics given the research is a bit more theoretical.

I am going to be applying to PhD programs for next year. My background is that I went to an Ivy League school and majored in OR. After college, I am working as a software engineer and have taken some math classes to beef up my profile. I am planning on taking the Math GRE next fall. My interests lie in machine learning and modern statistics. Some areas that I may want to work on down the line are optimization, variational inference, robustness and theory of deep learning. The research I find most meaningful both develops new algorithms and provides theoretical guarantees. I am not interested in working on specific applied areas such as NLP, computer vision, robotics. Should I be applying to statistics or CS programs? What are the best programs to do more statistically rigorous, theoretical machine learning research? Looking at course requirements in statistics programs, it looks like many schools (Stanford, University of Washington, UChicago, etc.) require advanced probability or measure theory sequences which may prove useful but are much more important for someone doing research in classical statistics, program evaluation or stochastic models. Course requirements in the CS programs at these same schools look incredibly flexible, allowing me to take whatever courses I would want across several departments. But I am finding that most of the research papers I find interesting tend to be by professors with joint CS and statistics appointments and many times require a very deep statistical background.

Thanks @icantdoalgebra and @bayessays. Is there any reason to believe it is harder to get into Penn than say a CMU/Michigan other than the cohort size? What sort of things would make an applicant get into one and not the other?

What makes Michigan/CMU different from Stanford/Penn? Are you saying that the admissions difficulty is not that different from top biostats programs for Michigan/CMU because they are less theoretical? I know that Stanford is considered a league of its own, but my research advisor (assistant prof at a top 10 stats program) told me he who would rank Michigan/CMU above Penn hence I am surprised you would say they Penn is harder to get into than Harvard/Hopkins.