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Euler17

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Posts posted by Euler17

  1. 11 minutes ago, bayessays said:

    I was not aware the UPenn got their students out so quickly.  The GSTP at Michigan requires essentially an extra year of genetics courses so you will be busy with coursework for the first 3 years I think, in addition to basically starting out doing research right away.  Also the second year statistics theory courses at Michigan are pretty intense, and I think you also have to take PhD genetics courses which are probably difficult for someone who isn't a biologist, but I'm just hypothesizing here.  But yes, Michigan's degree takes longer in general and I would take that into account.  Even outside of genetics, 6 years seems to be more common than 5, and I think for genetics 6-7 is the norm.

    As for the first few replies in this thread, perhaps we worded the endorsement of Michigan too strongly.  Usually when we make recommendations like this, we are focused on optimizing for reputation/productivity of faculty, as these are the most objective measures and everything else is very personal.  I do think, objectively, that Michigan has way more faculty working in genetics and a lot of very good ones.  If you are looking for an academic career in genetics, the concentration of geneticists you'll interact with Michigan is a unique experience -- some of the professors like Zhou and Zollner are actually PhD geneticists who work on statistical stuff, which is rare to find - they take the genetics part very seriously.  Some alums in the past (not sure about recently) actually take jobs as genetics professors after graduation.

    The quals at Michigan are going to be *significantly* harder than the ones at Penn, as they cover 2 years of statistical theory, rather than just the masters-level material.  One or two students a year usually fail and have to leave the program.

    You're going to be getting a PhD from a top biostatistics program and in the worst-case scenario, you will have a lot of 6-figure industry jobs in a variety of fields open to you after graduation, so it may be worth it for you to choose Penn if these things about Michigan are scaring you away.  But if the question is only "who has the better genetics faculty," I think the recommendation still stands.

     

    OP should ask Michigan directly about the quals, rumor has it they may be changing. 

    I think the idea that Michigan stat genetics is facing a steep decline is a bit misguided. They have some strong junior faculty working in diverse areas.  Also, they have some nice recent hires, like Veera Baladandayuthapani, who doesn't strictly focus on genomics, but has done some really innovative work. 

  2. In relation to you not being proud of your recent papers, remember that you probably know the shortcomings/holes in your work better than anyone else. I have struggled with being completely unsatisfied with 2 of the papers I wrote this year because I felt like there were so many ways that they could be improved, but I trusted my professors that they were ideas worth writing about. And maybe I don't get around to improving upon them in later papers the way I envision, but once they are out there others have the opportunity to build on them if they'd like. I def agree with most of the issues you bring up, just wanted to give my two cents on that point. 

  3. This doesn't address everything in your post, but something to maybe keep in mind: I've found men to be much less forthcoming about their struggles in school, especially in a competitive grad school environment. Additionally, men, like myself, have been systematically "affirmed" by society of their ability to perform in STEM programs, which gives many a confidence that is often misplaced. This is just to say that when you are taking the "temperature" of your classmates, the observed states of men are probably less informative of the hidden states than they are for other students(excuse the hidden markov model terminology). 

     

     

    Edit: As an aside, covid-19 is definitely exacerbating inequity in academia. I have also found, like most others, I struggle much more in online math and statistics classes than in in-person ones. I hope you can stick it out until things return to a more "normal" situation. 

  4. I agree with Stat Assistant Prof, though your relationship with your math professor does not need to remain anonymous. Regularly attending office hours and asking good questions about the course material or about the professor's research can definitely help build a more meaningful relationship. It would also help if their office hours are not video calls with multiple people being able to attend at a time. Anyway, the physics prof is probably the way to go, especially if you have other letter writers or components of your application that attest to your mathematical ability. 

  5. Hello all, 

    I am currently using "Introduction to Linear Regression Analysis"  by Montgomery, Peck, and Vining and find myself dissatisfied with the presentation of the material. I am more accustomed to traditional mathematics textbooks and I think this text, because it has a broad audience, does not flesh out the mathematical details in a way that I would prefer. I think if I was given a more complete picture of derivations and such I would have a better intuition for the material. Does anyone have any suggestions for intro linear regression textbooks with a more mathematical slant? 

    Thanks in advance! 

     

     

  6. 7 hours ago, bayessays said:

    Definitely agree that there are theoreticians at JHU and Harvard, and even Michigan biostat (where a grad just got a job at a top 15 statistics department doing theoretical work).

    I also struggle with giving advice to prospective students on what it even means for work to be theoretical and whether this distinction is even useful except at the extremes.  Annals of Statistics papers are theoretical (and if you want to publish there, probably go to a statistics department besides a few professors at top biostat places).  Lower-ranked biostat programs, you can do very extremely applied health research and write a dissertation that probably doesn't have a whole lot of innovative stats stuff in it. But unless you absolutely know you want to do Annals-type research (which is rare), you'll probably find everything you would really want in between at a top 60 stats or top 8-10 biostat program.

    I also think that the "theoretical" label is often applied to only a very narrow set of what I would consider theoretical research - specifically things on high dimensional stats, asymptotics, measure theory, etc...  That is theoretical statistics according to the statistics community at large.  But when most people not already in the field say theoretical, I suspect they mean "not just applying an R package to a set of data", so the stuff done at any top biostat or stat department fits.  For instance, Tyler VanderWeele at Harvard is one of the biggest names in causal inference, a professor of epidemiology as well as biostats, and his research is described on wikipedia as "applications of causal inference", but I can't imagine reading this paper and thinking it's not a theoretical paper -- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166439/ it's about creating a framework to solve a class of statistical problems and how to think through them - if that's not theoretical, I don't want to be a theoretician.

    Research is usually divided into applied, methodological, and theoretical, which I think are pretty arbitrary, but I think prospective students should think a lot about what they mean when they want to do "theoretical" research.  Do they want to do something that focuses on conceptual ideas of how to solve statistics problems, rather than just analyzing a data set with a known method?  To me this is a more important distinction than "uses complicated enough math to go in Annals of Statistics."

    Could you share the web page for the Michigan grad -> Stat prof? 

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