gradprospect5400

Hey everyone! I posted some time ago, but I was a little less clear on my interests at the time of my posting that and also put it under "statistics" instead of "biostats." I've now gotten some better sense of the space I would be interested in, specifically developing statistical models that can be used by more applied medical researchers targeting individuals. By example, I mean something along the lines of understanding protein folding, not for epidemiology. Tibshirani's work seems like the canonical example of what I had in mind. This is definitely still *extremely* vague, but it's a step closer than I was a couple months ago. Anyway, with that said, I think my interest falls into the more theoretical side of biostats and more applied of stats. To cut to the chase, I have a couple schools in mind, and I wanted to know if anyone had a sense of: (1) Whether this school list seems reasonable given my profile (or if there are any that are worthwhile adding) (2) If people had suggestions for professors doing relevant work (outside of those I listed) Thanks for taking the time to read this -- I appreciate any feedback! ______________________________ Schools List Stanford Statistics: Tibshirani, Hastie, Duchi, Ma UC Berkeley Statistics: Yu, Wainwright, Jordan, Steinhardt, Bartlett Biostatistics: Dudoit Harvard Statistics: Murphy Biostatistics: CMU Statistics: University of Washington (12/1) Statistics: Shojaie, Witten, Harchaoui, Kakade Biostatistics: University of Michigan Statistics: Nguyen, Regier, Tewari Biostatistics: Duke Statistics: Dunson, Rudin, Parr UPenn Statistics: ______________________________ Undergrad Institution: Princeton University Undergrad Major: Bachelor of Arts in Mathematics, Certificates in Applications of Computing (CS) and Statistics/Machine Learning GPA (Undergrad): 3.725 department, 3.65 overall Type of Student: Domestic Relevant Courses(Undergraduate): Honors Analysis (A), Honors Lin. Alg (A), Analysis II: Complex (A-), Topology (A-), Discrete Math (A), Theory of Algorithms (A), Fundamentals of Stats (A-), Optimal Learning (A), Neural Nets: Theory/Apps (A-), Analysis of Big Data (A), Junior Seminar: Analytic Number Theory (A-), Junior Paper (A), Senior Thesis + Oral defense (A, A), Real Analysis (B), Abstract Algebra (B) Relevant Courses(Graduate): Fairness in ML (A), Theoretical ML (B+), Machine Learning & Patter Recognition (B+) Relevant Research: Princeton requires doing an undergrad "junior paper" and "senior thesis", so I did mine in applied game theory and applied ML/data analysis respectively. I also did research in mathematical modelling with a professor from Columbia over freshman summer. And, although not really "research," I did some projects that extended papers in some of the grad classes I took (specifically (1) creating a probabilistic ML fairness checker for scikit-learn, (2) a policy gradient exploration for Tesla charging station locations, and (3) implementing an x86 neural branch predictor). Also definitely not relevant, but I did some work at the Princeton Plasma Physics Lab as a high school senior and was a Siemens Westinghouse Semifinalist. Recs: 3 strong rec letters (undergrad thesis advisor, Columbia professor, and current manager) GRE General: 166V/170Q/6.0A Programs Applying: Biostatistics PhD Current status: Working as a computer graphics/vision engineer at Facebook (2 years)

Thanks everyone for the replies -- this has been *super* useful!

Wow, thanks for all the replies! It seems like the vagueness in my original post makes this a difficult question to answer. So, to clarify on my interests (just a quick background), I was rather torn between applications and theory in my undergrad studies (hence the split between CS courses and the more theory focused courses) and why I have worked in industry for 1 1/2 years. But now, my interests are in fields like statistical learning theory and perhaps reinforcement learning (i.e. bandit problems, policy methods). I clearly still need to spend time refining this set of topics, but off the top of my head, Peter Bartlett's work at Berkeley (https://people.eecs.berkeley.edu/~bartlett/) looks super cool to me! So, any group that is doing similar work would be probably what I'm looking for. Also, as a somewhat more blunt question, what does the "top 10" refer to here. (I realize that rankings are quite arbitrary, but it's nice to get a sense of the places that other academics consider as having interesting work going on) There's the stats top 10 (from US News), but I wasn't sure if that really mapped to the ML subfield (as I narrowed it above). Does anyone have a sense of what the top schools (other than Berkeley, Stanford, MIT, and UW) would be? Thanks again!

Hey everyone! I wanted to get a better sense of my prospects for a PhD in Statistics, aimed at ML, mostly to get a better sense of what schools I should be applying for (have some specific questions on that at the bottom of the post). But anyway, here's an overview: Undergrad Institution: Princeton University Undergrad Major: Bachelor of Arts in Mathematics, Certificates in Applications of Computing (CS) and Statistics/Machine Learning GPA (Undergrad): 3.725 department, 3.65 overall Type of Student: Domestic Relevant Courses(Undergraduate): Honors Analysis (A), Honors Lin. Alg (A), Analysis II: Complex (A-), Topology (A-), Discrete Math (A), Theory of Algorithms (A), Fundamentals of Stats (A-), Optimal Learning (A), Neural Nets: Theory/Apps (A-), Analysis of Big Data (A), Junior Seminar: Analytic Number Theory (A-), Junior Paper (A), Senior Thesis + Oral defense (A, A), Real Analysis (B), Abstract Algebra (B) Relevant Courses(Graduate): Fairness in ML (A), Theoretical ML (B+), Machine Learning & Patter Recognition (B+) Relevant Research: Princeton requires doing an undergrad "junior paper" and "senior thesis", so I did mine in applied game theory and applied ML/data analysis respectively. I also did research in mathematical modelling with a professor from Columbia over freshman summer. And, although not really "research," I did some projects that extended papers in some of the grad classes I took (specifically (1) creating a probabilistic ML fairness checker for scikit-learn, (2) a policy gradient exploration for Tesla charging station locations, and (3) implementing an x86 neural branch predictor). Recs: 3 strong rec letters (undergrad thesis advisor, Columbia professor, and current manager) GRE General: V/Q/A: Haven't take yet, but assume ~165/167/5 or so GRE Math Subject: Haven't taken (considering taking, but logistics seem a little screwy with coronavirus) Programs Applying: Statistics PhD Current status: Working as a computer graphics/vision engineer at Facebook I'm mostly worried about my real analysis/abstract algebra course, and some of the grad classes I took in ML. Of course, there's nothing I can do about the grades now (I've studied it after graduating and understand it a lot better now and really love the material! Guess I was just missing something when I took the class, but eh) Anyway, with that out of the way, I would love to gauge the schools that seem reasonably well matched with these stats. This is largely to find a set of schools to which I should apply. Also, with that said, I'm still quite early in my search for labs -- I'm really interested in doing research in the theory side of ML (hence the application for a stats PhD vs. CS). Would anyone happen to know labs doing interesting work? Thanks anyone who read through this long post -- I really appreciate you taking the time and would love to hear anything you have to say!