rickck

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I am an MS in Computer Science planning on applying to Biostatistics (maybe statistics) PhDs, with interests in statistical machine learning, network science, precision medicine, and genomics. I graduated Math/CS undergraduate with 3.88 GPA. To offer some context on my school, it's honestly a pretty decent school with a strong statistics and biostatistics program, and strong CS program (although maybe now declining a bit in CS). Coursework: - Linear Algebra (more theoretical), Intro Probability, Advanced Calculus., Statistical Machine learning, + other maths. - Took graduate math (Although more CS related math - Combinatorics, Combinatorial Optimization, Mathematical Cryptography) as part of Budapest semesters in math (A+, A+, A) - Other CS courses - Will have: Graduate prob seminar, Functional analysis, Mathematical Statistics by the end of the MS among other CS courses. Research: - High risk screening tool for clinical outcomes. Decent likelihood that I will have a (I guess, small) publication from this. - Currently, I am getting involved in other bioinformatics/biostatistics related research, either some more vanilla biostat or others applying Machine learning to biomedical problems - TBD. Awards: - ACM ICPC - Top 25 regional - High GPA related honors Summer/Work experience: - Proof of concept model using bayesian networks for clinical prediction - wrote flask apis/code as well. - Research I stated as first point. - NLP related Data science internship with some coding/devops LORs: - First research advisor who is in school of medicine - Stat professor i took probability, machine learning, grad prob seminar with. - TBD GRE: (166 Quant, 162 Verbal, 4 (i think) ). Most likely will retake to perfect Quant. My aim is to attend a top school. In my opinion, strengthening my application with research/publications would be the most optimal move. A few questions: - Do you have any advice for strengthening my application, or input on where I stand applying to biostatistics/statistics phd programs? I feel like I am/will be in an ok place, but wondering if I should apply to an MS in statistics/biostat before applying to phd. - Related to q1, not sure what biostat schools someone like me should aim for? To be honest, I do not want to go to michigan/minnesota or non 'coastal' school. I was considering: (harvard, uc berkelye, columbia, brown, yale, upenn, udub, unc - ch, jhu, emory) but these definitely seem ambitious. Are (yale, brown, upenn, emory, columbia) considered top biostat schools? - How much do you think my MS grades matter? I imagine my math/stat grades definitely matter? but what about cs? - Finally, regarding research, at least without higher level math or not being a phd student, it seems most research will reduce to applying say standard statistical methods (survivial/p value analysis) on a dataset and reporting, or ML via a data science approach. Eg) just grinding out some analysis. Is that typically how research experience works for non/aspiring phds? - Do you think I should/in a good place to apply to statistics phd as well? Thanks.

I am an MS student in CS (and Math/CS undergrad) with the intention of applying to biostatistics phd programs. Background: I have taken the required background courses in mathematics for biostatistics phd programs (probability, linear, advanced calculus) among several other math courses (not related to probability/analysis/stat) coming out with good grades, and have some biostatistics research experience but no publications yet. Will be taking mathematical statistics and a graduate probability topics course. In preparing a strong application for biostatistics phd programs, what advice in general do you have? should I focus on research experience and publications? should I focus on taking more advanced mathematics courses (like functional analysis)? Since my MS is accelerated, taking the advanced math courses while dedicating a lot of time into research is not very feasible for me.