fatcat2

Mainly concerned about my low? GPA and unsure of the strength of my rec letters. Not super familiar with statistics phd admissions either so I'm not sure where I stand as an applicant for these programs. Any advice would be appreciated. Will also be applying to ~10 EE/CS PhD programs, this post is just for stats. Undergrad Institution: UC Berkeley Double Major: Computer Science and Applied Math GPA: 3.65 (major gpa above 3.7) Type of Student: domestic GRE General Test: not planning on applying to programs that require it. Research Experience: Worked in deep learning research for a year, co-author of paper accepted at ICML. Industry research internship one summer in machine learning, and currently leading a ML adjacent research project with a bit more of a theory component. Broadly interested in statistical machine learning, high dimensional statistics, information theory, but I also find myself becoming more and more interested in other areas of statistics as I learn more about it like bayesian inference and causal inference. Letters of Recommendation: Strong letter from industry research internship - wrote the draft myself and pretty sure they are submitting it as is. Assistant CS Prof: I feel that I didn't contribute as heavily to the ICML project as I could have, and most of the efforts were led by a masters student. So I'm not sure how much the ICML name will really help my application. The letter will be at least decent/good but definitely not the outstanding kind that can get you into anywhere. Very well established EECS prof: For the project that I'm leading, I meet mostly with a grad student to discuss progress and don't have as many interactions with the professor. Hoping to turn it into a publication by the end of this year, aiming to have a preprint asap. Mostly focusing on this component of my app for the rest of the year. Hopeful that this can potentially be a strong letter. Relevant Classes Probability & Random Processes (A), Real Analysis (A-), Convex Optimization (A-), Linear Algebra&Differential Equations (A), Multivariable Calc (A-), Signals&Systems(A), Graduate Information Theory (A-), Machine Learning (B), Artificial Intelligence (A-), Data Science (A), Mathematical Statistics (currently taking), Numerical Analysis (currently taking), Abstract Algebra (to take this fall), Complex Analysis (to take this fall). A-/A in other CS courses with a few B+. Schools: Current list for stat phd. very interested in the first five but unsure of chances: Columbia, UW, UMich, Yale, U of Toronto, UNC Chapel Hill, NCSU, Wisconsin-Madison, UPitt, ASU MS/MA Statistics: Columbia, UW, U of Toronto Also will apply for around 10 EE/CS PhDs and also considering operations research PhDs