Biostats PhD (Profile Evaluation + Recommendations)

[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!

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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: 

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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)