Hi everyone, I'm a current third-year undergrad and just one semester away from submitting my PhD application. I would say since the second semester of my sophomore year, I've been fascinated by various topics in statistics and worked hard to prepare applying to grad school. However, due to the increasing competition in PhD application and the COVID situation, I find it hard to position myself in the applicant pool and match up with different schools. Any suggestion/comment is appreciated. Thx in advance!
Undergrad Institution: UC Berkeley
Majors: Statistics & Computer Science (B.A. not EECS B.S.)
GPA: 3.94 (Major GPAs 4.0 for both)
Type of Students: International
GRE: haven't taken any but I'm a good test taker, so I just assume I will do well on the general test. Probably would take the math test as well.
Completed Courses (Undergrad, all A/A+):
Stat: Probability Theory, Math Stat, Stochastic Processes, Bayesian Statistics, Linear Regression & Application, Programming in R
Math: Multivariate Calc, Linear Algebra and ODE (proof-based), Real Analysis I and II (measure theory included)
CS: Intro to AI, Machine Learning, Data Structure, Computer Graphics
(first/second year prerequisite courses like intro to statistics are omitted)
Grad-level Courses : Deep Reinforcement Learning, Analysis of Time Series, Theoretical Statistics (a PhD first year course that I will take next semester)
Research Experience:
1 semester biostatistics research with PhD student on dimensionality reduction.
1 year (ongoing) long self-driving car project in a Berkeley lab, focusing on the perception and planning modules. Might have a publication in reinforcement learning (not really relevant to statistics IMO) with a PhD candidate before application.
Honor thesis research & paper with an associate professor on a Bayesian statistics topic (tentative) starting in the summer.
Awards/Honors/Recognitions:
Dean's honor list (4 semesters)
CS honor society
Regional autonomous racing competition award
Work Experience:
Reader (grader) for 4 semesters (Stat, Math courses); Tutor for 2 semesters
Summer SWE Intern at a tech unicorn
Recommendation Letters:
One strong recommendation from an associate professor who taught me two courses (1 undergrad + 1 grad) and will be my honor thesis advisor. I think it would be strong since the professor will know both my academic & research capacities very well.
One "somewhat" strong recommendation from the self-driving lab executive director who knows my work. He is kinda famous in the field but weirdly not on a tenure track (idk how this would affect the letter's influence).
One average recommendation from an assistant professor who taught me two courses as well.
My Concerns/Questions:
It seems to me so freaking difficult to have a publication in statistics as an undergrad (except for those who take real analysis in freshman year I guess). Would top statistics programs accept candidates without any publication by the time they submit their materials
Does extending graduation for one extra semester (to apply a year later) seem to be a good choice if I want to reach a higher-tier program? Or do the programs place higher requirements for super-seniors?
So far my dream schools include Cal, Stanford (I really love the bay area), Duke, and Columbia. School suggestions are very appreciated as well.
