Undergrad Institution: Large private university ranked ~60s in USNWR Major(s): Mathematics
Minor(s): GPA: 3.92
Type of Student: DWM GRE General Test:
Q: 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:
Undergrad:
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
Grad:
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:
UW
JHU
Michigan
UNC
UC Berkeley
Minnesota
Wisconsin
UCLA
Emory
Duke
UC Davis
Vanderbilt
Colorado Denver
U of Louisville
U of Cincinnati
Rochester
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!