Hey everyone! Hoping to get an idea of the strength of my application and chances of getting into some of the schools below for a PhD. Left school to work at a management consulting firm which I believe is a bit untraditional. Would also appreciate any school recommendations/adjustments to my current list, any thoughts welcome! Thanks
Undergrad Institution: Top 10 University in Canada Major(s): Data Science GPA: 3.96 (Last 4 Semesters/2 Years), 3.96 (Major), 3.89 (Cumulative)
Type of Student: Black International (Canadian) Male GRE General Test: Taking summer 2022
Programs Applying: Computer Science (PhD), Statistics (PhD), Operation Research (PhD)
Research Experience:
2 first-author ACM conference papers (1 applied machine learning and has been cited, 1 data/text mining)
1 last-author conference paper (data/text mining; awarded Best Track Paper in Data Analytics and Big Data)
1 journal publication in geospatial statistics (submitted)
Other research in applied machine learning and finance (no publication)
Awards/Honors/Recognitions:
Gold Medal (graduated with the highest GPA in Major, top of the class)
Undergraduate Fulbright Fellowship
Letters of Recommendation:
One strong letter from an Associate Professor in Statistics & Management Science who I wrote the geospatial statistics paper with (not well-known in CS)
One strong letter from an Assistant Professor in Data Science & Business Analytics who I published two papers with in data/text mining (not well-known in CS)
One decent/strong letter from an Associate Professor in Economics & Statistics who I performed research with (~1yr together) but no publication
One strong letter from a Partner at my management consulting firm (relevant for Operations Research programs)
Computer Science Courses: Intro CS I & CS II, Data Structures & Algorithms, Software Tools & Systems, Discrete Structures/Discrete Math, Analysis of Algorithms, Databases I, Data Science I Math/Statistics Courses: Probability & Stats I & II, Statistical Programming, Calc I, Calc II, Intermediate Calc I, Intermediate Calc II, Linear Algebra I, Linear Algebra II, Numerical Analysis, ODEs, Modelling and Simulation, Regression, Generalized Linear Models, Advanced Statistical Computing, Statistical Learning
Any Miscellaneous Points that Might Help: Particularly interested in doing research within the field of Machine Learning (and potentially NLP), still figuring this out. Not particularly interested in CV or RL. Would have spent >1 year at a management consulting firm at the time of applying and >2 years by matriculation (a advantage/disadvantage?) Applying to Where: In no particular order here, CMU - PhD in Machine Learning University of Washington - PhD in Computer Science Columbia - PhD in Computer Science MIT - PhD in EECS Princeton - PhD in Computer Science Cornell - PhD in Computer Science NYU - PhD in Data Science UIUC - PhD in Computer Science UT Austin - PhD in Computer Science UPenn - PhD in Computer Science UC San Diego - PhD in Computer Science UCLA- PhD in Computer Science Georgia Tech - PhD in Machine Learning UMichigan - PhD in Computer Science
CMU - PhD in Statistics University of Washington - PhD in Statistics Stanford - PhD in Statistics* Cal Berkeley - PhD in Statistics UPenn - PhD in Statistics Yale - PhD in Statistics & Data Science Stanford - PhD in Statistics Cal Berkeley - PhD in Statistics UPenn - PhD in Statistics UMichigan - PhD in Statistics
CMU - PhD in Operations Research MIT - PhD in Operations Research Georgia Tech - PhD in Industrial and Systems Engineering UMichigan - PhD in Industrial and Operations Engineering
Thanks again!
