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Statistics PhD, Fall 2023 Profile Evaluation

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Hi everyone, I graduated this past May and am in the process of applying for Statistics PhD programs! I already posted my profile as a junior, but I will add an updated one. 

Through my undergraduate research experiences, I have become highly interested in the subject statistical machine learning (statistical properties of machine learning models). My advisors are recommending that I apply to "top programs," but I am worried about the competitiveness of the applicant pool. Does anyone have suggestions for safer schools I could apply to that also suit my research interests? I do not mean to brag, and I realize that I am very lucky to be in the position I am in. I am just worried about potentially striking out with my applications.

I appreciate any and all thoughts/advice.

My List

Statistics PhD: Berkeley, Stanford, Harvard, Carnegie Mellon, UWashington, Columbia, Penn, Cornell

Operations Research PhD: Princeton, MIT


Undergrad Institution: Top 5 USNWR
Majors: Statistics, Mathematics (double major)

GPA: 4.0/4.0 (major), 3.98 (cumulative)
GRE: 333 (169Q + 164V + 5.5)
Type of Student: Domestic White Male
Programs Applying: Statistics, Operations Research
Research Experience:  
  • Senior thesis: theory of neural network training, with a high-profile professor in my department
  • NSF REU: modeling PDEs via neural networks (have been working on this research for > 1 year, will result in a publication)
  • Applied Stats: did not publish, but produced some interesting results
Best Thesis in Statistics Department, Phi Beta Kappa, summa cum laude, other GPA-related things

Pertinent Activities or Jobs: 
  • Working as a research assistant at a well-known university during my year off between undergrad and grad school
  • Teaching assistant for three semesters in undergrad, both undergrad and grad courses
Coding Skills: 
Python, C, SQL,R
Relevant Classes, Grades:  
Math/Statistics: Calculus II & III, Ordinary Differential Equations, Intensive Linear Algebra, Probability Theory, Statistical Inference, Multivariate Statistics, Vector Analysis (i.e. Analysis of Manifolds), Real Analysis I & II (included general measure theory), Measure-theoretic Probability, Abstract Algebra (groups and rings), Linear Models, Mathematical Optimization I & II, Machine Learning, Causal Inference (all A's) (all proof-based except for calculus)
Computer Science: Introduction to Computer Science, Data Structures, Object-oriented Programming (all A's)
Edited by dirichletprior
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  • dirichletprior changed the title to Statistics PhD, Fall 2023 Profile Evaluation

Honestly, I would be very surprised if you did not get into any of the programs you listed. But if you're looking for more programs to apply to to be on the safe side, literally just go down the ranking list and see which ones you like. I think below the tier you're looking at right now, admissions should be pretty safe for you.

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