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Found 18 results

  1. Undergraduate Institution:Top 3 in math/stat in India Program:Bachelor of Science Major:Math Percentage:74%(Major)(Known for grade deflation) Type :International Asian Male Math/Stat courses: Analysis 1(100),2(71),3(75),Probability 1,2,Algebra 1,2 ,linear algebra ,topology,optimization,differential geometry,differential equation,complex analysis,stat 1,2,3 Masters Institution:Same above Major:Math Percentage:73.6%(At time of application) Math courses:Measure theory,several variable calculus,algebraic topology,functional analysis... GRE(general):will appear,expecting good score in quantitative section GRE(Math)-Cancelled due to covid Research Project-None lor :Haven't asked,expecting average Programs applying: Phd Statistics(Would like to work on interdisciplinary fields with emphasis on probability) School:Please recommend the range of school I should look for with enough flexibility to do intra departmental work.Any suggestion is welcome.
  2. Undergrad Institution: University of Washington Type of Student: asian male, International (undergrad in US) Major: Data Science and Statistics Minor: Computational Finance GPA: 3.8 (Major is 3.7) GRE General Test: Q: 166, V: 157, writing 4.5 Research: Some research on COVID-19, no publication. Working on honors thesis. Teaching: TAing an undergrad course Experience: Statistics related volunteering, data science blogger Awards: 1st place in a Datathon (~60 participants). A programming certification by Google Letters of Recommendation: 1 from research supervisor (professor). I will ask thesis advisor (professor) and TA supervisor for the other 2. Relevant Coursework: All undergraduate level. linear algebra, differential equations, probability, machine learning, statistical learning, discrete & continuous modeling, database Schools Applying (PhD in Statistics): University of Washington Carnegie Mellon UC Irvine UC Davis UC Berkeley UCSB UCLA Columbia NYU University of Toronto UBC Any school recommendations can help, thanks!
  3. Type of Student: International south asian Undergrad and grad Institution: Top public university in Bangladesh Major: Applied Statistics GPA: 3.97 , class topper (BSc) GPA: 4.00 , class topper (MSc) GRE General Test: 152 V/ 163 Q/ 4.0 AWA IELTS: 8.0 overall Programs Applying: Statistics/ Biostatistics PhD Research Experience: 1. One project on structural equation modeling with application in econometrics ( published) 2. Thesis on bias reduction with penalized likelihood approach for small or rare event data in AFT models (under review) Teaching experience: Lecturer at a university Awards/Honors/Recognitions: Dean's award, CASc (ISCB41), CFDC (ISCB40), other departmental awards Letters of Recommendation: Two from my project and thesis supervisors, one from one of my professors Math/Statistics Grades: Calculus (A+), Real Analysis (A+), Mathematical Analysis (A+), Basic and Linear Algebra (A+), Statistics (A+), Probability (A+), Statistical Inference I (A+), Statistical Inference II (A+), Regression (A+), Multivariate analysis (A+), Design of Experiments (A+), Econometrics (A+), Generalised Linear models (A+), Bayesian Statistics (A+), Survival Analysis (A+), Longitudinal data analysis (A+), Machine Learning (A+) Programming/ Software skills: R, STATA, MATLAB, Python (basic) Research Interest: Mixed effect models, Survival analysis, Statistical Learning, High dimensional data analysis Note: Weak points: GRE scores (though not planning to retake), limited research experience Schools: I am confused about the competitiveness of my profile for stat/biostat PhD as an international student. Any suggestion about the range of schools I might have a good shot at would be very helpful. Here are some schools I am planning to apply, 1. JHU (Biostat) 2. Upitt (Biostat) 3. Uflorida (Stat) 4. Purdue (Stat) 5. TAMU (Stat) 6. UCI (Stat) 7. UCSB (Stat) 8. UMass amherst (Stat)
  4. Undergrad Institution: Big state school (CSU) Major: Economics Minor: Math GPA: 3.835 (Magna Cum Laude, 4.0 if looking at just last 60 units) Type of Student: Domestic White Male GRE General Test: Taking it in a few weeks, have done very well on practice tests so I believe I can get 165+ Q and 160+ V at the very least - AWA probably 4.0+. Also willing to retake it if necessary. Programs Applying: Statistics PhD (but not necessarily opposed to biostatistics) Research Experience: Nothing unless you count an econometrics capstone project. Only decided to go beyond undergrad within the last semester or so. Awards/Honors/Recognitions: Very likely that I will be awarded as the top/outstanding Economics student, however that award seems to not be happening/has been delayed due to the covid situation. Letters of Recommendation: Very strong letter from the calc-based probability and real-analysis professor. Strong (or better) letters from calc-based mathematical stats, proof-based linear algebra, and econometrics professors. I can go into more detail about these professors on request. Math/Statistics Grades: Calc I (B+), Calc II (A+), Calc III/Multivariable (A+), Real Analysis (A+), Linear Algebra (fairly proof-based) (A+), Proofs Class (been told it's akin to a slightly more advanced discrete math class) (A+), Calc-Based Probability (A+), Calc Based Mathematical Statistics (A+), Econometrics (A+), Basic Stats Class (A+) Schools: Any recommendations here can help, but my main goal is to attend a decent UC and UC Irvine specifically is my current target (are programs in the top 30-60 range a reach for me? Should I consider a Master's instead?).
  5. Undergrad Institution: Ivy Major: Math + Joint masters in Statistics GPA: 3.85 Undergrad, 3.8 grad Type of Student: Domestic Asian Female GRE General Test: Have not taken yet Programs Applying: Statistics PhD or Math PhD Research Experience: One year with an assistant professor in Biostatistics studying evolutionary processes, One year with a full professor in Biostatistics. 4 months in a government lab of applied research with computer vision. No papers. Letters of Recommendation: The two professors that I've done research which should be strong. Also one from my undergrad advisor which isn't as strong. All are undergrad level unless otherwise indicated. Math Courses: Multivariable Calculus, Linear Algebra, Applied Linear Algebra, Differential Equations, Intro to Analysis, Analysis I, Analysis II, Functional Analysis, Measure Theoretic Stochastic Processes, Measure Theoretic Probability (PhD Level), PhD Level Analysis (will be in progress when I apply). Statistics Courses (Master's Level): Introduction to Stochastic Processes I, Introduction to Stochastic Processes II, Mathematical Statistics, Independent Study, Linear Models and ANOVA. Statistics Courses (PhD Level): Statistical Learning I, Statistical Learning II, Linear Models I, Linear Models II, Linear Models III, Information Theory, Bayesian Stats I, Bayesian Stats II. Mathematical Statistics (will be in progress when I apply) I'm pretty clueless about the whole process. I've looked at the websites for a few programs thus far and it appears as if I meet the basic requirements for most of them, but I'm not sure which I should apply to given my profile (nor am I sure about the distinction between the biostats and stats programs at some schools. Can/should you apply to both?) Thanks for your help!
  6. Type of Student: International (Canadian) Undergrad Institution: Top 5 Canadian University Majors: Honours program in Comp Sci. + Stats Minor: Physics GPA: 3.75 GRE: 160V/164Q/4.5 Research Experience: 3 REU's, two of which resulted in honours theses (one CS thesis and one Stats thesis). Although I have no publications, one of the REUs resulted in a paper that is currently being worked on and will probably be submitted post-application, if that matters. Also, I wrote a "project paper" that uses tools from stochastic PDE's, analysis, and topology to prove the behaviour of stochastic algorithms which impressed a prof. I was thinking of creating a personal website to showcase these theses, projects, etc. Graduate Courses: I will be graduating with 8 graduate courses (2 of which are PhD courses): Time Series(A), Generalized Linear Models(A), Mathematical Topics in Machine Learning(A), Applied Machine Learning(A), Regression and ANOVA(A), Reinforcement Learning,(*) Computational Statistics(*), Probabilistic Algorithms(*). * denotes currently enrolled Relevant Undergrad Courses: Honours analysis I (A), Honours Analysis II (B), Honours Linear Algebra (A-) Honours Stats (B+) Abstract Algebra I (A), Other: Awarded a fellowship to conduct research over the summer. Elected into student council twice and volunteer teaching music lessons to elementary kids. Letter of Recommendations: I currently have 4 professors who said they are very happy to write letters for me. Two of which said they will write "excellent" letters for me. All the profs are well-known in their respective fields. Notes: Hey guys! I'm mainly applying to PhD programs in statistics and concentrating on the following programs (they may be a reach but fingers-crossed): 1. CMU 2. UCLA 3. University of Washington 4. Yale 5. University of Toronto In short, I think my weak points are my GPA and GRE. I'm hoping my letters and the fact that I performed well in advanced courses can compensate for them. I essentially completed a masters degree during my undergrad so I feel like I wouldn't get much out of applying to a masters program. If anyone could give their opinion on schools I am competitive for and what other programs I should look into, that would be greatly appreciated! I'll take any information on biostatistic program stoo! My backup is a masters at my current institution which I am confident I can get into. Thanks and good luck to everyone else applying!
  7. Undergrad Institution:- A State University in India ( Country Rank:- 151-200) Program:- Bachelor of Science (Hons.) [ 3 year ] Major:- Statistics Minor:- Mathematics, Economics GPA:- 10/10 (Major), 9.6/10(Overall) [ Best Science Undergrad Award ] Type of Student:- International Asian Male Math & Stat Courses:- Classical Algebra+Trigonometry(9/10), Coordinate Geometry+Vectors(9/10), Calculus(10/10), Mathematical Analysis (10/10), Diff. Equations (9/10), Abstract Algebra(10/10), Linear Algebra+Mechanics(9/10), Numerical Analysis (10/10), Probability-I, II, III, IV (10/10 in all), Stat. Methods- I, II (10/10), Stat. Inference-I, II (10/10), Applied Stat I (10/10), Stochastic Process+Biostat+Applied Stat (10/10), Sample Survey(10/10), ANOVA+Design of Expt. (10/10), Multivariate Analysis+Operations Research (10/10), C Programming (10/10) Research/Internship:- One summer internship in a reputed institute (learnt some advanced math), Bachelor Dissertation on Skew-Normal Distribution (literature review+one new direction discovered by me) Graduate Institution:- Indian Statistical Institute Programs attended:- Master of Statistics [ 2 year ] Percentage:- 71/100 Courses:- Analysis-I (51/100), Linear Algebra+ Linear Models (83/100), Prob. Theory (75/100), Measure Theoretic Prob. (66/100), Martingale Theory (67/100), Large Sample Theory (69/100), Stat. Inference-I (58/100), Stat. Inference II (70/100), Robust Statistics (72/100), Non-parametric+Sequential Analysis (74/100), High Dimensional Inference (73/100), Regression (71/100), Multivariate Analysis (75/100), Sample Survey+Design of Expt. (64/100), Intro Computer Programming (75/100), Time Series (76/100), Stat. Computing-I (75/100), Stat. Computing-II (73/100), Pattern Recognition+ML(70/100), Resampling Techniques(70/100) GRE(general):- 158 (Verbal), 169 (Quantitative), 4 (AWA) GRE(Mathematics):- 77 percentile Research/Project:- Many projects as a part of course. One 6 month project paper on High-Dimensional Inference LORs:- Good Programs applying:- PhD Statistics(main preference) + PhD Bio-statistics(those where I can find work related to my graduate project) Schools:- Any recommendation for both the programs will surely help me. For Statistics, I am aiming for some universities like University of Texas, Austin, University of Connecticut, University of Iowa, Iowa State, Ohio State type of universities.
  8. Hi everyone, I finished undergrad in Spring 2018, have been working in consulting for 2 years and am looking to enroll in either a masters or PhD program in Biostatistics. I would prefer the PhD route upfront in the interest of funding. I've been preparing for the last year or so, and I would appreciate some more guidance on setting expectations and moving forward. My math courses are a little light, but I did have exposure to some statistics applications in finance/econ courses. I studied chemistry my first two years of undergrad and have some background in medicine / public health from those days, which I've addressed in my SOP when relevant. Type of Student: Domestic male Undergrad Institution: top 50 university (USNWR) Major: Finance GPA: 3.88 GRE General Test: Q: 166 V: 167 W: 4.0 Research Experience: None Letters of Recommendation: Two from finance professors, should be solid. One from a director at my company with whom I've worked closely and performed well. Math/Related Grades: Calc II (A), Predictive Modeling and Optimization (A), Financial Modeling (B+), Econometrics (A), Calc III (B), Linear Algebra (A), taking Diff Eq this fall. Programming: I have background in R from taking econometrics. Planned Applications: UCLA (MS), UC Berkeley (MS), University of Wisconsin-Madison (PhD), Boston University (PhD), Emory University (PhD), Columbia University (PhD) I realize a course in real analysis and/or probability would help a good bit. Unfortunately, one or two classes a semester is the most I can manage with my current job. If I'm not admitted, I am fine to re-apply for Fall 2022 admission and use the year to further strengthen my app. Any recommendations on programs or other advice is very helpful. Thank you!
  9. Student Type: Domestic Asian Male Undergrad: Top 5 U.S Public School Major: Statistics GPA: 3.63 (Major GPA is 3.8ish) Math & stat classes: Linear Algebra (A), CALC III (A-), Differential Equations (A), Data Science (A), Optimization (A), Probability (A), Advanced Linear Models (this fall), Machine Learning (this fall), Stochastic Modeling (this fall). Intro to Programming (C+) I goofed in my Intro to Programming course (took it Freshman year), but I've done well in all my higher level stats courses that have used R so hopefully that makes up for it! GRE: 164Q, 160 V, 4.0W Research: None. Was accepted into SIBS this summer but got cancelled due to COVID. Letters of Recommendation: All three from my Stat professors. They should be decent. Hi everyone, I have been reading posts on this forum for a while and am looking for feedback. I'm looking to apply for BIOS graduate programs this fall but I'm not sure if I should be applying to PhD or MS programs. I would ideally like to apply for PhD programs (don't really want to pay for Grad school) but I don't think my application will be strong enough due to low GPA and lack of research experience. Applications: Currently looking to apply to Emory (MS), UNC (MS), UCLA (MS), UC-Denver (PhD), Houston Medical Center (Phd), Vanderbuilt (MS) Judging my profile, do you think I'm overrating or underrating myself? Should I only be applying to MS programs even at lower ranked schools (Denver, Houston)? Or do I have a shot at PhD programs? Other program recommendations would be greatly appreciated!
  10. Hey everyone, I just graduated college, and due to COVID, my plans have changed. Originally, I was going to work in a sports analytics role for a professional baseball team this summer/winter with the chance of being hired full-time if I performed well. Then, after a year or two of work, I was going to apply to grad school. But, my job got canceled, and so now I've been home coaching pickleball (no lie, it's been a fun experience) and have decided to apply to grad school this winter. Thanks for any help you can offer! Undergrad Institution: UC Berkeley Major: Statistics GPA: 3.743 Type of Student: Domestic White Male GRE General Test: Taking it in a few weeks, obviously hoping for 165+ Quant Programs Applying: Master's Statistics Research Experience: I had a negative research experience with a professor, and I ended up not passing the "research apprenticeship." This is one of the main dents in my transcript I feel, so I will probably address it in my applications. While I don't think the professor did his fair share in leading the apprenticeship and he admitted this, I definitely could've done things better too. Fortunately, I grew a lot from the experience, and he and I met a semester later and reconciled, leaving everything on good terms. Work Experience: Does this matter? Just thought I'd mention--I was a data analyst intern for a baseball team after my sophomore year (used lots of R and SQL). After my junior year when I went abroad, I was a business analyst for a pro soccer team (mostly used Excel). This position was not a great experience, and I actually quit the role to go back to school early over the summer to prepare for senior year. I used this time to study machine learning on my own to be ready to apply to jobs, and I ended up getting the job I mentioned above with another pro baseball team. To this day, I'm really happy with my decision to leave the soccer job and do something that would be beneficial for my career. Awards/Honors/Recognitions: I was selected as a TA for Berkeley's 1,000 student Intro to Data Science course, which involved me teaching weekly lab and discussion sections of 25 students and participating in weekly meetings about content and logistics. I taught topics like python, SQL, pandas, linear regression, and PCA. I did this my last semester. Letters of Recommendation: Going to request a letter from my Game Theory professor who knew me personally and definitely liked me. Got an A in her class. Also will request a letter from one of the professors I TA'ed for (mentioned above). The third letter will probably be from an employer, though I'm a bit torn here. I can ask the person from the baseball team who hired me and who can speak to the models I built throughout the hiring process and my performance in interviews, or I can ask my current employer (the pickleball director) who I'm sure would have good things to say. Math/Statistics Grades (freshman-senior year in order) Intro to Linear Algebra and Differential Equations (B), Calc III/Multivariable (A), Calc-Based Probability (B+), Computing with Data (basically an R class) (A+), Discrete Mathematics (intro to proof-writing as well) (A), Theoretical Linear Algebra I took one summer at UCLA (A), Intro to Real Analysis (B), Calc-Based Mathematical Statistics (B), Fundamentals of Data Science (the class I TA'ed for) (A-), Intro to Time Series (A), Linear Models - Theory and Applications (A), Game Theory (A), Intro to Machine Learning (very theoretical) (A). Regarding my grades, I definitely didn't do as well as I would've liked in some of the fundamental stats courses (probability, mathematical statistics). But, my senior year I got all As in my stats courses which I really worked hard to do, and I hope this shines through. There are some weak spots in my math grades too, like my Linear Algebra and Diff EQs class, but that was my first semester and I like to think my A in the summer Linear Algebra class makes up for it. Real Analysis was really tough and I took it during a difficult semester personally, and it basically showed me I don't want to do a PhD. Schools: I'm very interested in applying to a few schools in Europe... please let me know if you know anything about this. And, regarding schools in general, I'm not quite sure which tier of schools I should attempt to target. From what I've read, Master's in Stats aren't too terribly competitive, so I'd like to think I can get into a top 20 program. Thanks for any guidance here. - ETH Zurich - EPFL (in Lausanne, Switzerland) - Oxford/Cambridge? (They seem pretty expensive for European programs) - Stanford - Berkeley - Harvard Data Science - Washington - NC State - UNC Chapel Hill - Minnesota - UCLA - Yale
  11. I graduated this June, currently working as a software developer at a top 5 bank. I'm interested in doing research in bayesian statistics / nonparametric statistics. Something bad happened in my first year so I got only ~3.8 in first two years of uni. Undergrad Institution: Canada top 3 Major(s): Statistics Minor(s): Computer Science GPA: 3.89(last two years 3.97) Type of Student: Asian Female GRE General Test: Q:165 V: 161 W: 4.0 Applying to: Statistics PhD Research Experience: One year at a relatively large hospital, worked mainly with logistic regression, co-authored an abstract. Other than that I do not really have much research experience. I have several bayesian course based projects. Letters of Recommendation: Probably a decent one from summer reading on math. One from AP teaching ML(got 99). I am hesitating between a prof who Ive taken course with, or my hospital research prof. Neither of them knows me well, but the former is a COPSS winner while the latter is from the only research I've done. Math/Statistics Grades: 1st year: Real Analysis(B+), Linear Algebra I(A+), Linear Algebra II(A) 2nd Year: Advanced Cal(A-), and probability I(A+), Prob II (A-), 3rd Year: Stochastic Process(A+), Spatial Stat(A), ML I(A), Applied Stat(A+) 4th Year: Stat Computation(A+), Multivar Data(A), Mathematical Stat I(A), Mathematical Stat II(A+), ML II(A+), And a bunch of CS courses: Neural Network, Database, Algorithm, etc, at least A Planning on Applying to: Duke, UCLA, UW, UPENN, UNC Chapel Hill What are my chances of getting into some of these schools, and what are some other schools I should look at? Thanks for your help!
  12. Hi, I hope everyone is doing well and staying safe! I plan to apply to graduate statistics programs soon. I'm primarily interested in masters programs, as I don’t feel competitive/competent enough for a PhD. I would love if someone could help me understand where I stand and if my feeling is correct. I also am very nervous about the kinds of schools that are a viable, given my background. I‘d greatly appreciate any advice! Type of student: Rising 4th year domestic mixed male Undergraduate Institution: Large public, no grade inflation (USN top 20 statistics department) Undergraduate Major: Applied Math - Stat concentration, CS minor, honors (not sure if that means anything) GPA: Cumulative: 3.90, Major: 3.95 GRE: Haven't taken yet; estimate: ~169Q, ~163V, ~4.5W Relevant Courses: (all undergrad level unless specified; all A's, except B's in ODE's and databases) Math: calc 1-3, real analysis, linear algebra, ODE's, mathematical probability, numerical methods, combinatorics, dynamical systems Stat: intro stats, linear models, data mining (grad level), sample survey/experiment design, intro operations research, topological data analysis, statistical computing, databases (grad level) Computer Science: intro programming, intro DS&A, analysis of algorithms, computer organization, programming languages Letters: average, but maybe below average - didn’t form very strong connections with professors Research Experience: 1 year of statistical modelling with biomedical science application; 1 year of work with a CS professor on a project with statistical focus in deep learning; no publications Internships: (not sure if this is relevant) one SWE/AB testing internship at local startup; one ML/NLP internship at a large public, but also relatively unknown, IT company Misc Experience: one TA position for a senior level math class; a lot of eclectic self imposed machine learning projects For masters I plan on applying to: (in order of descending perceived chances?) Texas A&M University, UNC, UWisc, Columbia, UMich, Duke, CMU, UW, UChicago, Harvard, Berkeley, Stanford. I have no idea if I am competitive enough to apply to these school though. They all seem super competitive, so I will likely apply to more schools. A big worry I have is that many of the masters programs in statistics are targeted toward industry/data science, rather than preparing for research/PhD - so idk if it is even worth applying to them. I am also unsure whether I have enough rigorous statistics courses to apply to PhD programs. Would love any advice! Thank you! p.s. sorry if you saw this on reddit, I thought it would be valuable to get specialized advice from here
  13. I'm about to start my junior year and would like to gain some insight into how I can be a more competitive applicant for statistics and biostatistics PhD programs during my final two years of college. I plan on applying for Fall 2022 but am considering taking a gap year to gain more research experience. My profile: Undergrad: Top 150 large state school Major: Mathematics GPA: 3.96 GRE: aiming for 167 for quant and 162 for verbal Type of Student: Domestic Asian Female (US) Math Classes: Calc I (A+), Calc II (A), Calc III (A), Linear Algebra (A), Differential Equations (A), Intro to Proofs (A), Intro to Statistical Computing (A+) Research: 1 year of experience in a biochemistry lab, no publications. Currently cold-emailing professors doing statistics research and hoping to start sometime this upcoming semester. I also plan on applying to stat/biostat REUs for summer 2021. Activities/Jobs: TA for multivariable calculus. Looking for fall and spring internships in data analysis/data science. Tentative Schools UC Berkeley - Stat PhD UCLA - Stat PhD Columbia - Stat PhD Carnegie Mellon - Stat PhD UPenn - Stat PhD Texas A&M - Stat PhD UT Austin - Stat PhD Rice - Stat PhD UCSB - Stat PhD UCI - Stat PhD University of Washington - Biostat PhD Emory - Biostat PhD Harvard - Biostat PhD JHU - Biostat PhD Most of these schools are reaches and very competitive, but these are a general idea of where I'd like to attend. Questions I've switched majors a few times and recently decided on going to grad school, which is part of the reason why I don't have any relevant research experience. Will this hold me back significantly? Would it be better to take a gap year (doing research or working in industry) or apply for masters programs to strengthen my PhD application? I come from an unknown undergrad. How will this affect my chances for a top 20 program? What can I do to strengthen my application? How important is undergraduate publishing for PhD applications? Thank you for reading through! Any advice would be appreciated!
  14. 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.
  15. Student Type: Domestic Asian Male Undergrad + Masters: Top 3 University with (a lot of) Grade Inflation Major: Mathematics (Masters in Statistics) GPA: 3.85 (3.93 Masters) Relevant Courses: Math: Linear Algebra, Multivariable Calculus, Ordinary Differential Equations, Applied Linear Algebra , Complex Analysis, Analysis I , Groups and Rings, Galois Theory, Graduate Analysis I, Analysis on Manifolds, Graduate Probability I, Functional Analysis, Analysis II (measure theory) Stats: Mathematical Statistics, Stochastic Processes I and II, Regression Models and ANOVA, Time Series Analysis, PhD Level Statistical Learning I and II, Applied Linear Models I, II, and III (PhD Level, taking this year). GRE: 169 Q, 166 V, 5 W Research: 2 years in a research lab working with a lesser known professor on evolutionary genomics. 4 months during the summer working on applied statistics research at a gov. lab. Honors thesis in Math in probability theory Various expository final project papers for classes I've taken Letters of Recommendation: 2 letters that are hopefully strong from my thesis advisor and professor who I worked with for 2 years. 1 letter from my undergrad advisor who taught an upper division undergrad course that I've taken. Considering that my university has a lot of grade inflation, I was a bit concerned in the GPA department. I was also a little shaky about the research experience that I've had; I don't know how much is "enough", especially since I've had no real publications that have come out of it! I wanted to get some suggestions on which programs it would be feasible to apply to with my background. Thanks!
  16. Hi all, I am considering applying to statistics, IEOR, and biostat PhD programs. My interests are more in the methodological side of statistics, so OR and biostat departments may be more appealing to me (leaning towards biostat), but I'm not opposed to more theoretical stats. That said, I wanted to have more insight on what should be my reach, target, and safeties for PhD applications. I would also like to point out some problems in my transcript, and see what you guys may think about it. A bit about my profile: Undergrad: Ivy League (not HYP) Major: BA in Economics (Major GPA ~3.1, Overall GPA ~3.35) A few C's and several B's in Microeconomic-related courses, but A level in Econometrics, and Macroeconomics Minor: Mathematics and Statistics (STEM GPA: 3.75) GRE: (plan to retake) Q: 164 V: 159 A: 4.0 Graduate: Mid-tier state school Major: MS in Statistics (GPA: 4.0) Courses Taken (Undergrad Level): Multivariate Calculus (A), Diff Eq (A+), Linear Algebra (A), Probability (B+), Statistical Inference (B+), Mathematical Statistics (A-), Econometrics (B+), Advanced Econometrics (A), Data Mining (A), Statistical Computing (A), Time Series (A), Real Analysis I (A-), several CS related courses at the A/A- level. Courses Taken (Graduate Level): Probability (A), Statistical Inference (A), Survival Analysis (A), Linear Regression Analysis (A), Modern and Applied Statistical Modelling and Computing I and II (A), Time Series Analysis (A), Design of Experiments (A), Data Mining (A), Multivariate Analysis (A) Research Experience: Had 2 research assistant positions in undergrad, doing applied statistics with business faculty. One independent study project in graduate school with a statistics professor (still in the works, trying to get published). Problem courses (all undergrad classes in sophomore and junior year): Health Economics (C), International Finance (C), Literature in the 1900s (C), Accounting (C), Several B-level grades in gen ed courses related to social sciences and economics I am worried about these problem courses since their grades are low. How will these seemingly unrelated courses affect my application in the stats, biostat, and IEOR fields? What schools should I be targeting? What schools should be safety? I am not even sure about the US News rankings, since there are mixed emotions about their ranking scheme. I plan on retaking the GRE to score a higher quant score of 165+. Any advice and suggestions would be helpful. Thanks a lot.
  17. My goal was to apply to Stats and CS departments and emphasize my strong desire to collaborate interdisciplinary on applying statistics/ML to problems in the physical and life sciences. I really loved that kind of work over the years of undergrad research and even now as I work post-grad on research on biomedical imagery. I graduated in 2020 and right now am in a gap year doing research with my old school. Student Type: Domestic Asian Male Undergrad: Top 30 University (USNews National Rankings) Major: Physical Chemistry and Statistical/Data Science double major GPA: 3.55/4.00 Relevant Courses: I don't know if this is a worthy not, but I also took a slew of upper division and graduate classes in Chemistry, Physics, and Biology, including and not limited to: Statistical Mechanics, Quantum Chemistry, Advanced Organic Chemistry, Mechanics, Molecular and Developmental Biology, Biochemistry, Medicinal Chemistry, and Inorganic/metallochemistry Math: Linear Algebra, Multivariable Calculus, Ordinary Differential Equations, Discreet Math Stats: Probability theory, Regression Models, ANOVA, Time Series Analysis, Statistical Machine Learning, Bayesian Data Analysis, Statistical Experiment Design CS: Programming principles in Python, Introduction to algorithms in C++, Introduction to data structures in C++, Python for Machine Learning GRE: In-Progress, haven't gotten my score yet Research: 3 years in a research lab working with the chair of the chemistry department, did polymer/materials science research, two second-author publications in two different journals with impact factor of about 4-4.5. Also helped develop a simulation program in Mathematica. Currently doing post-grad research with the MechE department making a tool that assists in the classification of biomedical images (I don't expect a publication soon, Grad student said he'd put my name down maybe 3rd author when the paper on the project is finally published). I am also trying to put my chem lab skills to good "humanitarian" use and took a part time job processing COVID19 tests (A friend reached out and said that the genomics lab he works for needed extra bodies to process samples) Selected for and presented as keynote project in school data science fair Letters of Recommendation: Chair of the chemistry department (Extremely Strong) - For 3 years he directly oversaw the research I was doing and saw my writing/data show up in the final manuscripts that were submitted and accepted. Collaborator (PI) on the chemistry project from a different university (Less strong than #1) - they saw me do the legwork for the project, collecting data and presenting it over 2-3 years. MechE Professor (Strong) - I'm doing work with him postgrad and he said openly in group meeting that he loved the work I was doing and was amazed at the progress I had made in a few short months. He said he would love to write a strong rec. Statements/Essays: I'm trying to craft a story of someone who has tried really hard to discover that I finally loved the application of statistics/computing to physical and life sciences. My UGrad career was a wild ride of intense chem, physics, bio, math and stat classes and while trying to discover what I really wanted, I realized that I wanted to research something that mixes all of them. I wanted to get some suggestions on which programs to apply to with my background (low tier, target, reach). Is my GPA too low to get into top programs? How well would I have to perform on the GRE to be considered at top-tiers? Edit: also I see a lot of people took real analysis classes, is this super mandatory to be considered a top applicant?
  18. Hi, all! New member here. I'm currently in the first year of an interdisciplinary masters program focused on trauma and genocide. I'm building a list of potential doctoral programs to apply to in English (bachelors is in psychology with a minor + significant work in English). My research interests are trauma and memory studies, critical race theory, and children's literature. More specifically, I'm interested in representations of the violent/traumatic past and cultural memory within children's historical fiction. So far, Sara Schwebel at the University of South Carolina is the researcher I've found that most closely aligns with my research interests on the children's lit side, not sure about the theory side. Along with South Carolina, I'm interested in University of Pittsburgh, University of Florida, Ohio State, University of Southern Mississippi, and University of Connecticut. If anyone knows any programs/scholars strong in these areas, I'd be extremely grateful if you'd pass their names along to me! Thanks so much!
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