Danieldm

I basically narrowly care about three things. 1. ML for healthcare/precision medicine, 2. Architectures for statistical learning that mimic the human brain/operate more like real intelligence and 3. robotics. I'm also applying to 4 CS programs. May up biostatistics to 3 and add in Ann Arbor. But yeah... theory is by and large not my cup of tea. 2. is closest, but that's kinda more of a neuroscience/coding problem than a math problem.

Sounds like I oughta apply to more biostats then... Should probably knock off Penn for it. You're right that they admit basically nobody given the small size.

I basically got that same spiel from my profs. Actually, they encouraged me not to apply to places much worse, because they thought that the further down you go, the more heavily GPA is weighted. They both said I'd have a better chance to get into a program ranked 10/15 than a program ranked 30/40. I'm kind of a wonky applicant so I'm interested to see where I get in. I basically think this is super high variance, and the more people I talk to the more that seems to be the view. Thanks! I'll be sure to post an update on where I end up in a few months. Also you said you got into some programs similar to the lower end ones that I listed... may I ask where?

If I don't get in, I'll just go do the masters and reapply in a year. Also if it matters, I do have the upward trend going. GPA will be a 3.7+ for my junior and senior year, which includes a full summer load in between time.

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. Courses: 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. 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.

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. Courses: 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. 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: Statistics: Harvard, Carnegie Mellon, UM-Ann Arbor, UW-Seattle, University of Pennsylvania Biostatistics: UNC-Chapel Hill, Harvard Is this a reasonable list given my stats? I know my GPA is low but all else seems to be good. I was aiming for the 6-12 range for programs.