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Phew, that's a relief, thanks so much! Also, I have a geographical preference for the Bay Area (although obviously I'm open to any location). I know that Stanford and Berkeley are by far the toughest programs to get into, but is there anything more I can do to optimize my chances for those schools?

Hey there guys, I'm currently completing my masters and I'm strongly considering applying to Statistics PhD programs next year after I'm done. My research interests are in statistical machine learning and optimization, although I've become increasingly interested in biostatistics as well. Undergrad Institution: Berkeley Majors: B.A. Mathematics & Statistics (with Honors) GPA: 3.81/4.00 Type of Student: Domestic Male Masters Institution: Cambridge MASt in Mathematical Statistics I'll have my grades for my masters sometime next year towards the end of the course. Type of Student: Domestic Male GRE General Test: Q: 170 V: 169W: 5GRE Subject Test in Mathematics:M: 82% Programs Applying: Statistics Research Experience: 3 Research positions at Berkeley. All under EECS professors, one doing research in non-convex optimization, another in information theory and source codes, and one in statistical machine learning. I have two publications but none as a first author. I'll also soon begin conducting ML research under another notable CS professor here at Cambridge. Awards/Honors/Recognitions: N/A Pertinent Activities or Jobs: 1 software engineering internship at a startup Letters of Recommendation: I'll probably get them from professors I've done research with so they should be really good. Coding Skills: Python, Java, R Relevant Classes, Grades: Undergraduate Studies- Mathematics- Calculus I,II,II(A, B, A), Linear Algebra and Differential Equations (A-), Discrete Math (A), Advanced/Abstract Linear Algebra (A), Abstract Algebra (A), Real Analysis (A), Complex Analysis (A), Measure Theory and Topology (A), Functional Analysis (A), Advanced Numerical Methods for Matrix Computations (A) Statistics- Probability Theory (A), Statistics (A), Computing with Big Data (A), Stochastic Processes (A), Statistical Learning Theory (A), Time Series Analysis (A) EECS-Introduction to Programming (B+), Foundations of Data Science (A-), Optimization Models in Engineering (A), Machine Learning (A), Convex Optimization (A), Non-Convex Optimization and Approximation (A), Information Theory (A) Graduate Studies- Measure-Theoretic Probability, Advanced Financial Models, Stochastic Calculus and Applications, Modern Statistical Methods, Casual Inference , Topics in Statistical Theory, Computational Game Theory I'll have my grades for my masters sometime next year towards the end of the course. Although, I anticipate I'll get good marks in them. Right now I'm not sure if I want to go into academia or industry, so I guess the "name brand" of the school matters for me in industry. Programs I'm thinking of applying to right now are Stanford, Berkeley, Harvard, UW, CMU and UChicago. Here are some of the concerns that I have- 1. I obviously also need to add some safety and match schools as well so I was hoping I could get some feedback on that. 2. I have a few concerns regarding some of my poor grades in more basic classes like intro to programming, Calc II, Intro to Linear Algebra and Diff Eq's. I took these classes in the first three semesters of college when my mom was dying from terminal pancreatic cancer and as a result I never really got a chance to study, I spent all my time with her while I could. Should I explain these circumstances on my application? 3. Another concern I have is maybe my lack of dedicated programming classes. Although I learnt data structures and algorithms on my own time (and in practice through my software engineering internship), I never took a formal course in the topic or any other systems CS class. Could/Will this hurt me? Thank you for reading this super lengthy post. Any and all help is appreciated