PhD in stats/biostats evaluation

[causalinf]

Undergraduate Institution: Top 10 by US News Ranking (Ivy League/MIT/Stanford/Duke/UChicago) with top 5 stats program

Major//Minor: Statistics major and CS minor

Cumulative GPA: 3.79

Major GPA: 3.82

Student Type: International Asian Male

GRE General Test:  Quant:170, Verbal: 160, AW: score hasn't been out yet 

GRE Math Subject: not planning to take it 

Classes: 

   Statistics: Bayesian Stats (A+), Machine Learning (A), Mathematical Stats (A), Regression Analysis (A-), Statistical Consulting (A), Decision Analysis (A-), Causal Inference (in progress)

   Mathematics: Linear Algebra & Differential Equations (A), Multivariable Calculus (A-), Probability (A), Basic Real Analysis (in progress) 

Research Experience: I worked on applied statistics research on the topics of missing data imputation and prediction model for my senior thesis in statistics (It is a year-long project). I had a good working relationship with my advisor, and really learnt a lot from this experience. I am currently working on methodology research in causal inference and ML with two other faculties in the statistics department. The research has been going well, and we are publishing the paper in the upcoming few weeks. I have also worked on interdisciplinary research in energy data for about a semester, where I got acknowledged contribution for the published paper.

Research Interests:  I am interested in both applied and methodology research in statistics. Specifically, based on my past research experience, I find causal inference a fascinating field, and can see myself working on problems related to this field in the future. In addition, I also enjoy building prediction model, and apply advanced statistical techniques and machine learning to problems related to social science/healthcare.  

Publications: 

   To be submitted: I am submitting my causal inference research paper in the next few weeks.

   Acknowledged Contribution: Scientific Data (accepted)

Teaching Experience: I was a TA for undergrad level mathematical statistics and graduate level bayesian statistics class.

Letters of Recommendation: I haven't asked my advisors for rec letters yet. If everything works out, I will have one strong letter from a senior faculty in stats department, where I worked with this faculty for a year for my senior thesis. Another letter from a junior faculty in stats, where I have working relationship with this faculty (research + TA) for around 5 months. Another letter from a senior faculty in stats/CS (also 5 months of research with this faculty).  

My Questions: Since I am applying for both statistics/biostatistics PhD, there are lots of options. I would like to narrow down my options to 15 - 20 schools. I am leaning more toward statistics program, so I will apply to more stats than biostats school. My target programs are Harvard, UW, U Chicago, and CMU. What are my reach school and safety school? Is it bad if I apply to the same school for both stats and biostats programs?

Edited October 13, 2018 by causalinf

[Stat Assistant Professor]

I think your mathematics background may be a little lighter than most of the applicants applying to Harvard and U Chicago (especially as an international applicant). UW and CMU have good faculty who work on social science applications, so I think these are both good programs to target as "reach" schools. Still, the programs that you've listed are very competitive, so I would make sure to apply to a much wider range of schools. In fact, the top 20 programs in the combined Stat/Biostat USNWR rankings are all quite competitive, so I would definitely send half of your applications to schools in probably the 21-50 USNWR combined Stat/Biostat range.

Your pedigree should help you, but I would recommend taking a few more math courses... numerical analysis, linear algebra with proofs, maybe a second semester of Analysis.

 

[insert_name_here]

You should be competitive at most schools. I'd consider targeting Berkeley as well, they're quite strong in ML and have hired a number of causal people in the past five years.

[speowi]

I would also suggest considering Berkeley, though it seems like more of a reach than a target.