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Fall 2020 Biostatistics PhD Applicant Profile

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Undergrad Institution: Large private university ranked ~60s in USNWR
Major(s): Mathematics

GPA: 3.92
Type of Student: DWM

GRE General Test:
 164 (84%)
V: 163 (93%)
W: 4.0 (57%)

Grad Institution: Large private ranked in top 15 
Concentration: Applied Mathematics MS
GPA: 3.81
Programs Applying: Biostatistics PhD
Research Experience: Second author on publication in computer science, RA for 1.5 years with professor doing research in control theory. RA for another professor for two semesters, awarded a school grant to do computational neuroscience research that turned into a capstone-like project. Presented multiple years at undergraduate student research conference. Performed some research in Bayesian model selection methods in grad school, but no publications.
Awards/Honors/Recognitions: Full tuition scholarship throughout school. Research grant as mentioned above. Graduated cum laude.
Pertinent Activities or Jobs: Summer research internship at tech consulting firm developing statistical models for an agriculture company. TA for three quarters for calculus I and differential equations in grad school. Currently working in IT consulting building web apps using Python/C#/SQL.
Letters of Recommendation: The two professors that I RA'd for in undergrad, plus one in grad school who I took several classes from. The one I published with is quite well connected in CS, and the other are two (one in pure math, the other in applied math/biomath) are more junior, but I believe all should give me strong letters.
Math/Statistics Grades: 
Calc 2: A-
Calc 3: A
Intro to Proofs: A-
Linear Algebra: A
Diff Eq: A
Abstract Algebra: A
Analysis 1,2: A-,A
Complex Analysis: B+
Dynamical Systems: A
Geometry: A
Intro Stat: A
Intro Bayesian Stat: A-
Dynamic Optimization: A-
Feedback Control: A
Intro to CS, Data Structures: A,A
Diff Eq of Math Physics 1,2,3: B,A,A-
Asymptotic and Perturbation Methods 1,2: A,A
Nonlinear Analysis: B
Numerical Methods 1,2,3: A,A,A
Deep Learning, ML,Predictive Analytics: A,A,A
High Performance Computing: A
Computational biology: A

Any Miscellaneous Points that Might Help: I was originally in a PhD program in applied math that I mastered out of, to take some time figuring out what I wanted to do. After a year of working in industry and talking to several biostatisticians, who all seem to love their careers, I've decided to try my hand at applying to biostatistics PhD programs. I'm not sure if this will be viewed negatively or not by any schools.
Trying to decide which of these schools to apply to:
UC Berkeley
UC Davis
Colorado Denver
U of Louisville
U of Cincinnati
Medical College of South Carolina
Any thoughts on my competitiveness for these programs? I'm thinking of retaking the GRE to bump my quantitative score, and I'm taking an online course in calc-based probability as well. Thanks for the help!
Edited by hebpo
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I do think a 164 is a little low given the rest of your profile - it's not devastating, but if you think you could get it up to even a 166, I think that will look a lot better and would be worth $200 in the long run. I'm going to just assume for the rest of this that you take it again and get a 166.

I'd be surprised if you got into the top 3 programs (JHU/Harvard/UW) - I wouldn't spit out my coffee, but I think it's not super likely.  I don't think it's throwing money away, and you should always aim high to make sure you don't have regrets.

I wouldn't be surprised if you got into schools in Michigan-Emory range on your list.  I don't think they're give-me's, but I think if you apply to a lot of schools like this, you'll get in a few places.  I think you should be applying to a lot of programs in this range.

I do not think  someone with your math background should be applying to the last 5 schools on your list unless for personal reasons (location).  You will have a wider range of career options open to you from a school in the top 15.

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Also, I didn't notice the part about mastering out and how you're worried about how that will be viewed.  I wouldn't worry about it, too much.  You've switched fields, had some time to grow and realize what you want to do - the opinion of anyone who views this negatively isn't worth worrying about.  If you dropped out of a statistics PhD before (not at a defined endpoint like your master's), that could be a little risky for a program that you would leave again, but even that isn't a dealbreaker for most programs.  IMO, if anything, your experience makes you an attractive candidate because you've already seen things you like and don't like and are more informed than the average undergrad.

I bring up this point a lot in this forum, but grad committees are filled with human beings, who each have their own opinions, experiences, and prejudices.  Yes, there is someone on some admissions committee somewhere that will view something on your profile as a red flag.  It's not worth your time worrying about that stuff though, because there are more people who will be impressed by your thoughtfulness and what's led you to their program, and those are the people you want to go spend 4-6 years of your life with.

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If it eases your feelings a bit, I had a professor who mastered out of a top statistics school and then got admitted for a PhD at another top statistics school immediately thereafter.  You would have even less to worry about than they did since you changed fields, and clearly it wasn't a problem.  

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Thanks for the replies! That lines up with my suspicions about the top 3, after reading profiles here. I'll likely apply to one, maybe 2 of them, but spend the bulk of my applications in the rest of the top 15. That's good to know about the last 5 as well.


As a follow up question to my "mastering out" question - would schools want to see some sort of proof of commitment to a new graduate program? Personally, I know this is the right choice for me, and I can talk about what I think is great about biostat in my SOP - but will adcoms want to see more than that? For instance - taking on a research project as an RA, or something similar?

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I think it definitely makes sense to talk about research projects that got you interested and why you decided to switch to biostat. It doesn't have to be that deep.  Most people say they like math and want to help people - it's a pretty low bar. SOPs are not super important.

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It would be helpful to get a letter from someone in your former Applied Math essentially saying that you left the program in good standing (i.e., you didn't leave with a Masters because you failed the Ph.D. exam). And you will also want to talk in your SOP about why you're more "ready" for a Ph.D. now than you were then.

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