Fall 2023 PhD Profile Evaluation

[randomwalker]

Hi everybody, I would appreciate some insight as to whether or not I am being realistic about my current list as well as some suggestions for additional schools that might be a good fit. I'm primarily interested in Applied Statistics with applications to the social sciences but not necessarily from an economic perspective (thus why I'm not applying to proper economics PhD programs but may pending the results of this app season), possibly causal inference or Bayesian flavored.

Undergraduate Institution: T5

Major: Math and Statistics, Economics

GPA: 3.75

Background: Domestic White Male

Related Coursework:

Undergraduate Level: Intro Stats (A), Proof-Based MV Calc and Linear Algebra (B-), Data Structures in Java (A-), Econometrics (A-)

Masters Level: Linear Regression (A), Statistical Computing (A), Elementary Stochastic Processes (B-), Machine Learning (A), Bayesian Statistics (A), Real Analysis I and II (B+), Statistical Inference (A), Probability Theory (A)

PhD Level: Statistical Inference (due to take in the Fall)

Research:

-Did an REU on spectral clustering and randomization techniques for directed graphs, presented to school undergraduate research conference

-REU led to a paper on differential privacy in graphs that is likely going to be submitted sometime this Fall

-Worked as a research assistant in economics department performing quantile and logistic regressions on outcomes in the labor force

-Tried to co-author a paper in health economics with the PhD student I RAed for that had data and funding issues and ended up falling through 

Work Experience:

-Worked as a grader for intro to stats (x3)

-Worked in the statistics helproom for undergrad and masters students

-Was a peer coach for a statistical inference and surveys class helping with R questions

-Was a TA in the Economics Department for a relatively well-known macroeconomist

-Data analytics summer internship for large bank

Letters of Rec: 

-Professor who supervised my REU and who I am working with on the upcoming paper

-Professor who taught my machine learning class and runs the statistics helproom

-Not sure who the third will be, maybe a professor I was a grader for?

Extracurriculars:

-Was a senior editor for two academic journals, one in social science research and one in economics

School List (all PhD): 

Reach: Duke (dream school), Carnegie Mellon (Stat and Public Policy, also a dream school), UNC, UCLA, UW, UChicago Booth (Econometrics), Columbia, UT Austin

Match: UC Irvine, Johns Hopkins (Applied Math and Stat), GW, UVA, Northwestern, Boston

[bob loblaw]

You have a great background!  I would cut down on your list of schools to 7 or 8. Maybe cut some reach schools?

Other notes:

• UCLA Biostats may be a better fit since you're more interested in applications. 
• If you're interested in Bayesian stuff, I'd recommend UCSC as a match school. You'd most likely get in. 
• This is completely based on "what I've heard" but UC Irvine is actually more difficult to get into than rankings suggest. 

[Ryuk]

I agree that your profile looks good. Just to provide a balancing opinion, I think someone with a strong application like yours should apply to as many programs as possible. I also had a good profile and applied to over 20 schools. It was expensive and time consuming, but not as much as you might think. Fee waivers are easy to get and a lot of the components are the same. It also doesn't make a difference to your letter writers.

I am currently attending a top institution that I definitely would not have applied to if I only chose 6 programs. I did not think I had a shot at Berkeley, Harvard, UPenn, Duke, etc., but I sent yolo applications anyway. This will likely be the biggest decision you make as a young adult, so now is not the time to cheap out. I'm not promising that you'll get into these places, but you'll never know if you don't apply.