Jump to content

Search the Community

Showing results for tags 'statistics phd'.



More search options

  • Search By Tags

    Type tags separated by commas.
  • Search By Author

Content Type


Forums

  • Comment Card
    • Announcements
  • The Cafe
    • City Guide
    • IHOG: International House of Grads
    • The Lobby
  • Applying to Graduate School
    • The April 15th is this week! Freak-out forum.
    • Applications
    • Questions and Answers
    • Waiting it Out
    • Decisions, Decisions
    • The Bank
  • Grad School Life
    • Meet and Greet
    • Officially Grads
    • Coursework, Advising, and Exams
    • Research
    • Teaching
    • Writing, Presenting and Publishing
    • Jobs
  • The Menu
    • Applied Sciences & Mathematics
    • Arts
    • Humanities
    • Interdisciplinary Studies
    • Life Sciences
    • Physical Sciences
    • Professional Programs
    • Social Sciences

Blogs

  • An Optimist's PhD Blog
  • coyabean's Blog
  • Saved for a Rainy Day
  • To infinity and beyond
  • captiv8ed's Blog
  • Pea-Jay's Educational Journey
  • Procrastinating
  • alexis' Blog
  • grassroots and bamboo shoots.
  • Ridgey's blog
  • ScreamingHairyArmadillo's Blog
  • amyeray's Blog
  • Blemo Girl's Guide to Grad School
  • Psychdork's Blog
  • missesENG's Blog
  • bgk's Blog
  • Tall Chai Latte's blog
  • PhD is for Chumps
  • bloggin'
  • NY or KY
  • Deadlines Blog Ferment
  • Going All In
  • In Itinere ad Eruditus
  • Adventures in Grad School-ing
  • inafuturelife
  • The Alchemist's Path
  • The Rocking Blog
  • And Here We Go!
  • Presbygeek's Blog
  • zennin' it
  • Magical Mystery Tour
  • A Beggar's Blog
  • A Senseless Game
  • Jumping into the Fray
  • Asian Studies Masters
  • Around the Block Again
  • A complicated affair
  • Click My Heels Three Times and Get In
  • dimanche0829's Blog
  • Computer Science Crossed Fingers
  • To the Lighthouse
  • Blog of Abnormally Aberrant
  • MissMoneyJenny's Blog
  • Two Masters, an Archive and Tea
  • 20/20 Hindsight
  • Right Now I'm A-Roaming
  • A Future Historian's Journey to PhD
  • St Andrews Lynx's Blog
  • Amerz's Blog
  • Musings of a Biotech Babe
  • TheFez's Blog
  • PhD, Please!
  • Blooming Ecologist
  • Brittle Ductile Transitions
  • Pleiotropic Notions
  • EdTech Enthusiast
  • The Many Flavors of Rhetoric
  • Expanding Horizons
  • Yes, and...
  • Flailing Upward
  • Traumatized, Exhausted, and Still Going
  • Straight Outta Undergrad!
  • A Hitchhikers Guide to Transferring PhD Programs
  • Conquering College Admissions
  • Reflections of an Older Student.

Find results in...

Find results that contain...


Date Created

  • Start

    End


Last Updated

  • Start

    End


Filter by number of...

Joined

  • Start

    End


Group


AIM


MSN


Website URL


ICQ


Yahoo


Jabber


Skype


Pronouns


Location


Interests


Program

Found 11 results

  1. Dear All, I need a little helping hand from you. I suppose some of the users in this forum have a good idea regarding which universities have expertise on which field. So could it be possible for you to tell me which of the following universities' Statistics departments can be considered as a good option for a student who would like to end up doing PhD in the broad area of Computational Statistics. More specifically in the following areas:- Statistical aspects of Machine Learning (Statistical Learning), High Dimensional Inference & Data Analysis, Bayesian Computation, non-parametric methods, Topological+Functional Data Analysis, model-selection (say LASSO, penalized optimization related works). I would not expect but if anyone can provide a ranking (based on say the amount of people associated in these areas, reputation of the department considering these topics, Or the possibility of hosting a great research community in these areas in near future), it will be highly appreciated. I beg your pardon in advance as the list is not small and it may not be possible for someone to know about every nook and corner of each department. So, replies from several from users will be grateful who can at least tell what the department is good at. I hope the answers would surely help lots of people who are basically aiming for good (not the very top ones) universities in the above areas. The list follows. (1) University of Minnesota-Twin Cities, (2) The Ohio State University, (3) Iowa State University, (4) Pennsylvania State University, (5) University of Georgia, (6) UC Davis, (7) UCLA, (8) UC Irvine, (9) UC Riverside, (10) Rutgers University, (11) University of Connecticut, (12) University of Iowa, (13) University of Colorado, (14) Colorado State University, (15) Oregon State University, (16) Temple University, (17) Virginia Tech, (18) George Washington University, (19) University of South Carolina, (20) University of Texas-Austin, (21) Northwestern University, (22) University of Pittsburgh, (23) Rice University, (24) Florida State University, (25) University of Florida, (26) University of Illinois-Urbana-Champaign
  2. I am interested in pursuing a PhD in Statistics to specifically research Spatio-Temporal Data or working in industry working with spatial data (finishing my first year at a Masters in Statistics at Top 5 Stat program & interning at top GIS company this Summer). I am trying to see if anyone can share their experiences in this field (Spatial Temporal Modeling) about going the PhD route or straight to industry (government, tech, or other)? Also, after searching the web, I came up with these prof's/ uni's as a starting point for reading literature: Chris Wikle (U. of Missouri), Debashis Mondal (Oregon State), and Mevin Hooten (Colorado State). Any others to mention as I begin reading papers this Summer to better compare/ contrast the two routes? Thanks! Any and all advice is appreciated!
  3. Hi! I am trying to decide between the current offers for Stats PhD I have from Purdue, Minnesota, Ohio State and Univ of Toronto (Math Finance track in Statistics dept). I am interested in the areas of Machine Learning and high dimensional statistics, although I am open to explore new areas and then decide. Other than UoT where supervisors are already assigned, I have the flexibility to choose my supervisor in the other 3 places. In UoT, I have the chance to work at the interface of machine learning & finance which I find appealing. My questions are: 1. Which would be a better choice if I want a career in industry and which would be more suited for academia? 2. Among the US universities, is there any significant difference in the reputation of the 3 places? How close does the best of the 3 come to UoT in terms of research and future prospects? It would be really helpful if someone could suggest well-reputed faculty members or someone doing good research in the areas of ML and high dimensional statistics at Purdue, Minnesota & OSU. Note: Due to the COVID-19 issues, I am considering deferment to next fall. While the US universities have given me the option to do so, there has been no such assurance from UoT so far.
  4. Realized I had preemptively posted in the April 15th week section. I am currently weighting offers from UNC and Rice for a PhD in Statistics, and waiting for a response from TAMU (my top choice). Background: I am in my last semester of my Masters of Statistics at NC State. Started research with one of my professors there last semester and really enjoyed it; however, the MS program at state doesn't go as far in depth in either applied or theoretical statistics as I would like. I prefer Texas (e.g., Houston/proximity to Austin) for long-term location, but Rice appears to be ranked much lower than UNC and I don't want to be taking a step back academically. Rice also offered a much more financially lucrative package and has the connections with MD Anderson. UNC covers the theoretical shortcomings of my statistical education so far, but they are completely theoretically focused. I am also concerned about the current gender ratio and I didn't 'click' as well with the faculty at UNC as I did at Rice. Would I be throwing away a great opportunity if I chose Rice over UNC? What are the objective pros/cons in this situation - I am really struggling to step back and look at the big picture.
  5. I am still not sure whether to pursue a Phd degree in the future but I want to work in industry as my final career goal. Can anyone give me some advice on those two programs? If I would like to work as a data scientist/data analyst /data engineer/machine learning engineer/ after my graduation, which one is better? If I would like to continue to get a Phd Degree,which is better?
  6. Hi all, I wish that I had done this sooner! I never thought to ask the GradCafe, and everyone is so helpful here. I don't have much idea of which programs I should be targeting, so I'd like some advice based on my background. I graduated in 2018, so I spent a gap year after graduating without applying. I mention some of the things I did in here. Undergraduate Institution: UC San Diego (mid-tier university of california) Degree granted 2018: BS in Mathematics and Physics (double major), with department honors in Mathematics GPA: 3.59/4.0 (cumulative) Type of Student: Domestic Asian Male Relevant Upper Div Courses (quarter system): Math and Stats, Undergrad level Real analysis (lower level) (A, A-, A) Abstract Algebra (A-, A, Pass) Complex Analysis (A) Number Theory (A+) Mathematical Statistics (A-, A) Nonparametric Statistics (A-) Probability and Stochastic Processes (A, A-, A) Math and Stats, Graduate level Real analysis (A) Algebraic Topology (A-, A-) Probability (A-) Mathematical Statistics (A-, A) High Dimensional Statistics (A-) Physics, undergrad level: Classical Mechanics (B, A) Electromagnetism (B+, A-, A) Statistical Physics (A+) Quantum Physics (A-, B ) Mathematical Methods (A, A) Electronics (A-) Laboratory Projects (B) Physics, Graduate level: Special topics---Quantitative Physics (A-) Relevant Lower Div Courses (quarter system): Lower Division Physics (C, B+, B+, B+, A) Lower Division Physics Lab (B-, A-) Lower Division Honors Calculus/Linear Algebra (B+, B-, B+) GRE General: Verbal 167 (98%) / Quant 166 (89%) / Writing 5.0 (92%) GRE Math Subject: 760 (71%) Research Experience: Two summer REUs. I spent one summer working on improving facial recognition with neural nets. I spent the next summer on a theoretical math paper, basically using combinatorics. The revision is almost done, but we're not ready to submit it yet! My honors thesis was mostly expository, but I proved and explored one new thing, buried under all the surrounding context. Just that small result wasn't worth a publication though, I think. I spent the last year working part-time in a marine biology laboratory. I was mostly doing data engineering, organizing, and cleaning using bash, Python, R. I did train a neural net to recognize fish for the lab, and worked with a government biologist to fit some survival analysis models. That paper is also being written up, and is almost done, but we're not ready to submit yet! Work Experience: I was a TA for the Math department for nearly three years. Letters of Recommendation: Undergraduate honors thesis advisor (who also taught my grad mathematical statistics) High dimensional statistics prof Prof facial recognition REU Other: I read the Elements of Statistical Learning cover to cover this year very closely, and I have some detailed notes and problem solutions that I want to expand and post online but haven't done yet. Schools I'm Considering, Ranking from US News Stanford (1) Berkeley (2) UChicago (6) CMU (8) Duke (12) Columbia (16) Penn State (20) UCLA (27) UC Davis (31) UCI (50) UCSB (67) I've also applied for the NSF GRFP, if that matters. My main questions are: Am I targeting the right schools? Which ones of these are reach, match, safety? Should I try to cram in a retake of the general GRE? And should I submit my math subject GRE? I also have some other concerns... if anyone has time to lend an ear and donate a few cents. About my rec letters: I might have time to switch some of these around. The post-doc who led the combinatorics REU could also write a letter instead, or he could get the supervising professor to ultimately write it. Is that a better idea? And what about the government biologist? Honestly, we met somewhat infrequently, and it was mostly him asking me for advice on how to do a technique and generating plots for him. His training and credibility isn't in statistics. About my in-progress papers: Is there a good way to talk about these when they haven't been submitted yet and I'm not getting letters from anyone regarding them? It might be very important to get one of those letters, but I do think my high dimensional stats prof thinks very highly of me already, and I've known her longer... About my lower div grades: Those were all done in my freshman year, which has the lowest GPA of all my years. Is it worth explaining something about this? I think it was carryover from high school, where I also did not do very well, with plenty of C's and a few D's even, but I also had some family issues. About my Pass in Algebra: I accidentally signed up for pass no pass and didn't realize until the middle. The professor who taught that course acknowledges he would have assigned me an A, and he can let one of my letter writers know that. Is that appropriate? Should I mention it in my personal statement as well? Thanks so much for your input! You guys are such an awesome resource.
  7. Hey everyone, I will be applying to Statistics PhD programs for fall of 2020. I am mostly interested in probability theory and general statistical theory. Any advice is greatly appreciated! Undergraduate: Small public university. Relatively small math department. Major: Mathematics GPA: 4.0 Type of Student: White Male Relevant Courses: Calculus I, II, and III, Ordinary Differential Equations, Linear Algebra, Abstract Algebra, Modern Algebra, Discrete Mathematics, Advanced Calculus I (Real analysis), Numerical Analysis I, Financial Mathematics, Life Contingencies, Intro to Statistical Methods, Applied Reg/Time Series, Nonparametric statistics, Statistical Process Control, Mathematical Statistics I and II, Foundations of Computer Science, Fundamentals of Programming, Object Oriented Programming, Intro to Algorithms and Data Structures (A's in all courses) GRE General Test: Q: 168 V: 160 W: (waiting on score) Research Experience: Statistical consultant on a medical paper currently in peer review, not expected to be officially published before application. Additionally, I worked with a professor in mathematical research. I was primarily in charge of the computer programming to simulate our enumeration problem; research stopped due to professors family crisis. Awards/Honors/Recognitions: Valedictorian of the College of Science, Outstanding Math Student Award, Dean's list each semester. Letters of Recommendation: Professor (Department Chair) I worked with closely as a TA and took courses from, Professor I took classes from, Assistant Professor I took classes from. Additional Experience: Experience working in R, SQL, Python, C++, C#, and LaTex. I have taken three actuary exams and pass all three. I have a year's experience working as an actuary. A few internships during the summers of my undergraduate career in Cyber and Actuarial Science. I have ample experience in math and statistics TAing and tutoring. Applying to: Texas A&M, Colorado State University, University of Iowa, UC Davis, Virginia Tech, (Other schools suggested?) Comments/Questions: I'm curious to know if I'm aiming for the right caliber of schools. I am concerned about not having published, how will this effect my application?
  8. Hi all, I wanted to see if anyone had a little more info on UNH’s Statistics Ph.D. I looked on their website, and was able to look up a few professor’s profiles, and research interests, but there was not too much information, otherwise. I also sent an email out to the graduate admissions chair (as I have a LOR writer who got his MS there before going elsewhere for his PhD) just to hear a little more about their program. While I am waiting back, I wanted to hear a little more about their program, and if anyone has any experience with them, or the types of applicants they attract/admit. Thanks! B
  9. Undergrad Institution: Top 5 in U.S (private) Major(s): Mathematics (Intensive), Statistics and Data Science GPA: 3.95 Type of Student: domestic white male GRE General Test: Taking it in two weeks. Very confident about the Quant section, but not sure I will have enough time to prepare for the writing and verbal GRE Subject Test in Mathematics: I took it yesterday. I felt tired and unfocused for the last third of the exam and don't think I did well. Research Experience: 1st Summer: worked in a computational medical research lab 2nd Summer: worked in a computational atmospheric science lab 3rd Summer: worked on a project in a climate science lab that was heavy on statistics and time series analysis Awards/Honors/Recognitions: Phi Beta Kappa Pertinent Activities or Jobs: Currently TA'ing introductory Bayesian Statistics Graduate level courses: Functional Analysis (A), Bayesian Statistics (A), Statistical Inference (currently taking) Undergrad level courses: Math: Real Analysis(A), Abstract Algebra (A), Complex Analysis (A), Proof based Vector Calculus/Linear Algebra I and II (A and A) , Discrete Mathematics (A-), Ordinary Differential Equations (A) Statistics/ Data Science: Theory of Statistics(A) Introductory Bayesian Statistics (A), Optimization Techniques (currently taking), Data Mining and Machine Learning(currently taking) Computer Science: Algorithms(A), Data Structures (A) Physics (I don't know how much Stat programs would care, but some of these were challenging courses): Thermodynamics and Statistical Mechanics (A-), Advanced Classical Mechanics(A), Quantum Mechanics(A), Intensive Introductory Physics I (with lab) and II (A and A) Letters of Recommendation: -A computational medical researcher with whom I worked with for 3 summers (including the summer after freshman, and two in high school). He writes very strong and generous letters, but I am not sure if this carries much weight being so long ago, especially when the work was more coding and didn't have much statistics. - Stats teacher who knows me fairly well, I am a TA in his class now and took 2 courses statistics courses with him ( I am worried I did a mediocre job on his research final project in one class, but he knows me from enough settings to highlight my positive work) -Math teacher I didn't get to know all that well, but I took two math courses with him, (one being a grad level course), and aced all 4 of his exams. I found his classes and exams very difficult, but I am worried he won't perceive his classes as all that hard Statistics PhD Programs applying to: Top Schools: Berkeley, Stanford, UW, Harvard, Chicago, Wharton, CMU, MIT (for operations research instead of stats) Other schools: To be determined, but I know I should apply to at least 3 or 4 other schools that are easier to get into. Advice on this would be welcomed Concerns: -Not doing as well as I could have on the Math subject GRE, but I guess it is too late to retake, and I don't necessarily have to send the scores to most at least -Lack of any of my letters being able to highlight the ability strength as a statistical or mathematical researcher (rather than just ability to do well on tests, TA, or code at a high level relative to the med students and undergrads in the medical lab). Also maybe it is a red flag that I am not asking more recent research supervisors for letters, but maybe that's justified because I only worked with them for 2 months each. -No research publications -TIME: This is really where I need advice. I only have a month and a half left to take the regular GRE, write a statement of purpose, pick schools to apply to, communicate all the relevant information to my recommenders etc. I am unsure what to prioritize, the regular GRE? communication with letter writers? SOP? should I shrink my list or grow it? Also curious if I have a reasonable shot at top 10 schools and if it is worth the time applying to all of them or if I should focus on applying to schools where I have a better shot.
  10. What are my chances would be of getting into a statistics phd program? Undergrad Institution: top 50 private Major(s): Physics, Math Minor(s): GPA: 3.88 Type of Student: male, White, Domestic GRE General Test: Not taken yet, took a practice and got 86 and 91st percentiles for reading/math respectively, not planning on taking subject test Programs Applying: PhD in Statistics Research Experience: 2 summers of research at home university in physics, one paper in review, maybe another one or two to follow (not first author) Awards/Honors/Recognitions: Deans List, Pi Mu Epsilon Honors Society,Honors College Pertinent Activities,Jobs: TA, Tutoring Letters of Recommendation: I'm assuming they will be pretty good Computing Skills: R,Python, Latex, SQL, limited linnux, Applying to Where: NC State University of Connecticut Columbia Northwestern Boston University UNC UCLA Duke Is this a good list? Any advice would be really helpful. Thank you
  11. 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.
×
×
  • Create New...

Important Information

By using this site, you agree to our Terms of Use and Privacy Policy.