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Found 5 results

  1. Heyo yall, I'm currently applying to some PhD programs in stats and I'm trying to gauge how competitive I will be. Undergraduate Institution: R1 state school, top 100ish US News, not particularly well-known Major: Mathematics and Economics Minor: Data Science GPA: 4.00 Type of Student: Domestic white male (I am not straight tho I have absolutely no idea if I should include this on my app) Research Interest: Econometrics, applied statistics in social sciences, ML GRE Score: 168Q/160V/4.5, no math subject test cause it was canceled Math classes: Calc III (A), Basic Concepts in Math, an intro to proofs class (A), Real Analysis I and II (A), Linear Algebra (A) Stat classes: Probability theory I (A), Probability Theory II (A), Mathematical Statistics (A), Data Analysis and Stats Computing (A), Intro to Machine Learning (in progress) Misc classes: Mathematical Economics, basically a lot of linear algebra and its applications (A), Econometrics I and II, at the graduate level (A), Python Programming (A), more programming and data science courses (in progress) Research Experience: Year-long research grant in econometrics, leading to a paper (not published) but presented at a conference Currently a research assistant, using Python to do natural language processing in the social sciences, currently drafting a paper on Machine Learning techniques that coincide with this project Work Experience: Just some work as a data manager Sophomore year to pay some bills Awards: Dean's list all semesters 2nd prize for Econ paper in my department Letters: Currently deciding on three of the following, in order by how strong I think they'd be: Sociology professor who I am working with right now, publishes applied sociology papers Econ professor who I worked with on my econometrics paper, publishes applied econ papers Math professor in all of my proofs class including real analysis where I was one of the best students, does not publish anymore Math professor in prob theory II where I succeeded after going to office hours a lot, publishes stats papers Schools: University of North Carolina, University of Chicago, University of Washington, UPenn, Wisconsin-Madison, UCLA, University of Toronto, University of Michigan, Colombia, Carnegie Mellon, Penn State, Iowa State Any other school suggestions/letter suggestions or any other suggestions would be greatly appreciated! Thanks yall
  2. Undergrad institution: NYU Major: Honors Mathematics GPA: ~3.7 Domestic/International: domestic. Research Interests: Probabilistic time series analysis, bayesian stats, reinforcement learning, deep learning. Humanitarian slant. Undergrad Math Courses: Honors cal III (B+), honors LinAl (A-), Indep Study in classical diffgeo (A), ODEs (A-), honors analysis I (A-), honors algebra I (A-), independent study in representation theory (A), honors analysis II (functional analysis and measure theory, A), honors algebra II (B+) independent study in wavelet analysis (A). Grad Courses: Measure theoretic probability (A-), topology II (A). Current Courses: math stats, math modeling, [PhD/MA] probabilistic time series analysis, [PhD] probability 1, [PhD/MA] (mathematical) foundations of machine learning. Research: Current work in multigrid methods and machine learning. Unlikely to be published. Two undergraduate research grants from department and dean. Letters of Rec: 1 very large name in probability (ex Bourbaki), two other TBD but decent to very good professors. Programming languages: Python, R, Matlab, some C++. GRE: not taken due to COVID. Work: internship at bulge bracket bank starting October. Schools: Generally top programs, especially with a focus on humanitarian statistics. Notably: Columbia Stanford Harvard UChicago I dislike safety schools, if I'm reaching too high would rather spend a year in industry then apply again. Some recommendations of schools would be appreciated though. My biggest concern is that my transcript seems distinctly A-. Research was self guided and with overseas professor who could rarely communicate due to covid, as well as a topic my research partner and I fell out of love with quickly. Also, my honors analysis and algebra II courses were way harder than usual ones, with the analysis class being very similar to Courant's PhD real analysis course. Also had a particular... disdain for algebra professor that I'd rather not write on my SoP which is a large part of the B+ in that class (genuinely, not just whining).
  3. I have been accepted to PhD programs for the upcoming Fall at the University of Florida (UF) and the Iowa State University (ISU). -> UF has a small program with younger faculty--most of them are recent graduates from ivy schools--while ISU has one of the largest Statistics department in the US. ->UF is ranked #40 this year and ISU is tied at #20 -> Both places have research that interests me -> UF has a better reputation as a research university in general Please help me decide on picking the school for myself. What factors should I consider before making a decision? Are there any current graduate students or alum from these schools who can give some pros and cons on the schools?
  4. Hi everyone, I just received the PhD offer from CMU stats department, and I am so happy with that. However, now I am facing a difficult decision between choosing to stay at University of Toronto for my PhD (where I did my undergrad) or go to CMU for a new adventure. My current research interest is more about: 1. Applied Bayesian inferential methods. 2. Statistical Computations. 3. Machine learning and Data Science. And my future plan is to find a faculty position in statistics. The pros for UofT: 1. I am an international student, so staying at uoft makes it more possible for me to get the PR card in Canada, while the PR card in US is a bit impossible... 2. I did my undergraduate study at here, so I know the faculties at this university very well. And I have been matched with my preferred supervisors whom I have already worked with and felt good working with, while CMU currently did not match me with any supervisor yet. 3. There will be five years of full funding package which includes full-tuition plus 20k stipend per year for UofT, while CMU's offer only describe my full funding package for one year (full tuition + 3.1k stipend per month), and based on their student handbook it seems like the funding from CMU usually only lasts for four years... The pros for CMU: 1. Higher ranking in Statistics, especially in Machine learning and Data Science. 2. Based on the suggestions from my professors, if I want to continue my career in academia, it seems like it would be better for me to not go to the same place for both undergraduate and PhD... But I am not sure how important that factor is 3. Probably the winter at Petersburg will be more approachable than in Toronto... Could anyone give me some suggestion on how to make this choice? Any suggestions is appreciated! Thanks so much!
  5. I just finished my junior year of college, and I want to apply to statistics phd programs this upcoming fall. I am wondering how my math background is for phd admissions. I want to apply to top 20 programs. The math/ stat classes I've taken so far: calculus 3, linear algebra, ODE, advanced calculus, probability theory, theory of statistics, discrete math, linear models, basic analysis in function spaces, topics in analysis (undergrad real analysis), data analysis, intro programming, intro data analysis, multivariate statistics, data mining/ machine learning I have A's in all except a B+ in discrete math. I will be taking complex analysis in the fall. Is it okay that I won't have taken classes like topology, abstract algebra, measure theory, stochastic processes before applying to phd programs in statistics? And do I seem competitive (math/ stat background - wise) for top 20? What about top 10? Also should I take the math gre? If this info is relevant, my college is a top ivy school Sorry for the numerous questions asked. I appreciate comments addressed to any of them.
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