ihatemoney Posted August 12, 2020 Share Posted August 12, 2020 About me: White male US citizen, underrepresented minority affiliation Undergrad: Top 3 uni, BS Math, ~3.9. One summer of statistical computing research and another in an industry research lab. Linear algebra (Axler), calculus/ODEs, honors algebra+analysis, optimization, number theory, graph theory, full CS core + a good amount of CS theory. A-somethings everywhere but a B in analysis :'^( Masters: Top 3 program, MS Stats, ~3.95 Regression theory, data mining, sampling, PhD probability, statistical learning, stochastics, RL, bit of biostats. GRE, Recs: N/A (yet) Interests: ML theory, graph mining, p >> n, manifolds I've been working as a data scientist/applied scientist at a big famous Silicon Valley tech company since my MS (not being laid off lol), and after a couple years on the hamster wheel I'm wondering if I blew my chances at a great academic career. I wasn't aiming squarely at academia during undergrad/masters and didn't develop strong relationships with profs, do any great research or publish. I suppose I could ask stats PhD colleagues for letters of recommendation, but I'm not too confident I could get glowing letters from working academics considering both my unfocused past and that I've been out of the game for a while. With that said, my questions: Should I try to hype up my industry experience or downplay it? Any general guidance on the value of an industry recommendation vs. an academic one? Am I likely to be perceived as a flight risk? (Is that even a concern in grad admissions?) Does it make any sense to spend some time working on my research portfolio before applying for a PhD? How best to go about this if so? Any pointers on which programs are realistic? Thanks everyone. Link to comment Share on other sites More sharing options...
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