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PhD Eval Comp Sci/ML


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Student type: Domestic white male

Major: Mathematics BS, Computer Science BA at a top 20 statistics and top 10 biostatistics school

GRE: 169 V, 169 Q, 5.0 W, no subject tests

GPA: 3.5 Overall, 3.4 Math Major, 3.75 CS Major

I know it's not good but I do at least have an upward trend... GPA will be a 3.7+ all semesters in my Junior and Senior years, which includes a full load of summer classes in addition to normal semesters.

Research Experience:

One project that will be submitted to ICASSP 2020 and will be accepted/rejected sometime during the application process, so they may see it or may not. 9 months of work on deep learning for drug classification, state of the art result on a popular dataset in the subfield. Professor does not do machine learning, but is a moderately well known theorist. Also dept. chair.

One project that may be submitted to a conference around the time of applications but will not be accepted or rejected at that point. It's on using recurrent neural networks to forecast the cognitive abilities of Alzheimer's patients on their next clinical visit. 6 months of work. Professor is fairly famous, 10,000+ citations, well known ML researcher in our biostatistics department. Also dept. chair.

I will be first author of two on both these papers, I did most of the work and came up with the research questions.


I'm only gonna discuss the super relevant ones.

B+ in real analysis, A- linear algebra, A in intro graduate/advanced undergrad ML in the stats dept., A in intro graduate/advanced undergrad ML in the comp sci dept., and some grad courses. My university uses a weird scale and it converts to undergrad grades harshly... explained in my letters of rec. Pass/B in most programs/C here in intro to statistical theory, High Pass -/A- in most programs/B+ here in intro to statistical theory 2, high Pass -/A- in most programs/B+ here in numerical analysis. The statistical theory sequence is well known to be brutal and is considered the hardest pair of graduate courses in both of our statistics departments, so that'll be reflected in my letters.


2 from the guys I'm doing the research projects with, one from the prof I took intro to statistical theory 2 and the machine learning class in the stats dept. from. All should be quite strong, both people I am doing research with said they wanted to see me end up at Carnegie Mellon or MIT.

Master applied to:

Just UNC-Chapel Hill MS in statistics. I can transfer credits and do it in a year, but this is ONLY if I don't get into any PhD programs.

PhD Programs:

MILA, Carnegie Mellon (Specifically language technologies, robotics, and computational biology), Princeton, And then either Cal Tech or UCLA... torn between them honestly.

Also applying to stats and biostats but that's not listed here.

Edited by Danieldm
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