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Everything posted by potentialphd2

  1. Background: Graduated in 2019 with double major in Math and Computer Science and then worked in finance industry for a year. Returning this fall for a masters in the computer science department (at my school most statistics is under computer science) and will focus on some combination of machine learning / inference. My areas of interest in statistics are bayesian inference, causal inference, theoretical machine learning, and theoretical statistics. I am also interested in applications to healthcare. I would like to evaluate my prospects for applying to PhD programs in the fall in Statistics (or Biostatistics) and Machine Learning, as well as to hear feedback. Undergraduate Institution: Top 3 School. I will be starting a Masters in Computer Science focusing on inference/statistics this year and will be applying to grad schools in the fall. Majors: Mathematics, Computer Science Minor: Chinese GPA: 3.6/4 Major GPA: 3.76/4 Type of Student: Domestic White Male Courses taken: Math: Differential Equations (A), Linear Algebra (A), Real Analysis (no official grades that semester), Complex Analysis (B), Abstract Algebra (A), Intro to Stochastic Processes (A), Discrete Math Seminar (A), Logic Seminar (B), Intro to Probability (A) CS: Programming Fundamentals (A), Intro to Algorithms (A), Intro to Machine Learning (A), Algorithms II (B), Graduate Machine Learning (A), Graduate Seminar Machine Learning (A), Graduate Theoretical CompSci Seminar (A), Computer Systems (B), Software Construction (C) Stats: Graduate Mathematical Statistics (A), Graduate Inference (B), will also be taking 4 more courses in the grad statistics curriculum this year. Science: Biology I (No Official Grades that semester), E&M Physics (No Official grades that semester), Quantum Physics I (B), Quantum Physics II (C), Classical Mechanics (A), Intro Chemistry (C) GRE General Test: Not taken yet - seems like it might not be necessary? But happy to take if can improve my chances. Research Experience: Spring 2015-Fall 2016: Machine learning research in the physics department at a different university applied to theoretical physics data, published in a physics journal. Fall 2020: I will be starting a 1-year Masters this fall and will be doing statistics or ml-focused research. Working Experience: Fall 2017: Teaching Assistant for Abstract Algebra I Summer 2018, Sumer 2019-Summer 2020: Intern and quantitative trading analyst at bank Letters of Recommendation: Could get one from past research advisor, one from current research advisor, past work supervisor, or academic advisor Currently considering schools: PhD: What are my prospects at applying to top PhD programs (I.e. MIT, Stanford, Berkeley, UW, UChicago,...)?. Questions: Based on my background, am I better suited to apply to Statistics/BioStatistics or Machine Learning PhD Programs? Do you generally apply to do research under a specific professor, or is this decided after acceptance? Would it be more valuable to do masters research under: A well-known quantitative finance professor (would likely do relatively applied Ml/stats related work on healthcare data) A faculty in inference who is less well known (would probably do more theoretical work, the research group's interests are related to but not exactly bayesian/causal inference) How big of a problem is it that I have no experience in my areas of interest outside of my classes? Should I reevaluate? I would love feedback from you all and I greatly appreciate your help!! Please let me know if there's anything I can elaborate on! Thank you so much!
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