SparsityOnTopOfMyHead

Thank you so much for the advise! I am aware that all of these programs are extremely hard to get in, and I have a good chance to be rejected from all of them. One thing I forgot to mention is that my undergraduate's institute has a 5th year master's program and I am already admitted to that, so I still have somewhere to stay if I ended up rejected by all PhD programs. That said, I will definitely apply to more programs if I need to apply again next year. Thank you for pointing that out!!

Updates: 1. Letters from my two research mentors are most likely strong. One of my mentors is an assistant professor with a PhD from an incredibly renowned school in stats, and another is a postdoc fellow at my school. According to recent discussions, both of them seem to think highly of me, so I wish they may write me strong letters. 2. I eventually decided to apply to UWashington, UMich, Yale, CMU, UPenn, and perhaps UCLA. @Stat Assistant Professor @bayessays Could you please take a look at my profile? It will be much, much appreciated!!

Thank you so much for your suggestions! I agree with you that "at least positive" letters are definitely a drawback in PhD applications. I called them "at least positive" because I haven't discussed with these professors how highly they think of me, but the feedbacks I've heard from them are always pretty positive. I am still in close contact with my two research mentors and will definitely try to impress them more. If you may have time for any other suggestions, feel free to do so! I really appreciate them.

BTW, these ranks are both relative to all US universities, according to USNWR. So the second school is roughly 10 ranks higher than the first, among all colleges, no matter private or public.

Hi People!! Undergrad Institution(s): 1. Top 35 private US university; 2. Top 25 public US university with a stats dept. with good reputations. (Transferred after freshman yr) Major(s): Statistics, Mathematics Minor(s): None GPA: 4.00 in major, 3.98 overall Type of Student: International Asian Male GRE General Test: V160, Q170, AW4.5 GRE Subject: Have taken all relevant courses (for the math subject test), not sure if I should take the exam Coursework (completed): Institution 1: Math: Calc. III (A), Probability (A), Discrete Mathematics (A) Stats: Intro. to Categorical Data Analysis (A) CS: Intro. to Comp. Sci. (A; java) Institution 2 (Undergraduate level): Math (all proof-based): Linear Algebra (A), Advanced Calc. I (A+; mathematical analysis), Probability Theory (A+, *weakly* measure-theoretic), Abstract Algebra (A+) Stats: Theoretical (Mathematical) Statistics (A+), Statistical Learning (A+), Bayesian Statistics (A), Applied Regression Analysis (A+), Computational Statistics (A) CS: Data Structure (A; in C++) Institution 2 (Graduate level): Regression Analysis (A; PhD level), Statistical Learning (A; PhD level), Linear Programming (A; master's level) Coursework (future): Undergraduate level: ODE (fall), Topology (fall), Measure Theory (spring), Convex Optimization (spring) Graduate level: Masters' Statistical Inference (spring), PhD's Probability (fall; may or may not drop it depending on the amount of measure theory needed) Research experiences: A UROP-like project in comp. bio., where I worked out a C++ program (2000-3000 lines of codes) for some structure prediction problems in biology. No letters, and no connections with stats. Honors thesis in causal inference (using a very classical statistical method instead of graphs); derived the test statistic for a hypothesis test that will be used in my thesis. Data analysis are yet to be done, but a thesis or at least a draft will be expected by the time of application. Summer research in selective inference; switched project once during the summer, so not sure if I have sufficient time to come up with any meaningful result to present. The current progress doesn't seem to be too promising whatsoever. But the work will be continued before my graduation. Research interests: Selective inference, causal inference, statistical machine learning, and interpretable learning pipelines in general. Letters of recommendation: Mentors of the two statistics research projects (I have also taken a course with each of them), expected to be strong or at least positive. (Planned) A Do-Well-In-Class letter from a PhD course instructor. Working experiences: NA, is that a big deal? Skills Languages: Python, R, C++, MATLAB, Java Teaching experience No, but planning to do something this fall. Schools: Reach: Washington, Michigan, Duke(?), Toronto(?); *completely* uncertain about my chances Match: Cornell(?), TAMU(?), UCLA(?) and programs ranked 15+ or 20+? Safety: Undecided, all programs seemed hard enough to get in Notes: At this time of the application season, I am worried about so many things, including my target schools and my chances of getting in. Much of my anxiety should be traced back to the following questions, and I will very appreciate answers to them!! How likely am I to get into any of these schools mentioned above? Are there any advices regarding school choices? How much will my international background hurt my chances of getting into PhD programs during COVID? I heard some programs are having financial issues lately. Is my math background too weak for a stats phd applicant? I know I should take measure theory earlier, but in my college it is only offered in spring semesters, which are after the application deadlines. So are there better ways to shore up my math background besides the two courses currently on my schedule (ODE & Topology) this fall? Should I find a tutoring job (which I am very interested in), prepare for the GRE math test, or take one more course (that being either complex analysis, PhD level econometrics, or PhD level probability, with estimated difficulty in that order)? Thank you guy very, very much for any help and support!