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Found 123 results

  1. I'm an international student who graduated from the UK in 2016. I'm applying for PhD programs in Biostatistics/Statistics, starting Fall 2019. Please leave some comments on my profile, especially the range of schools to which I'm applying. BSc Degrees: Mathematics with specialization in Statistics from a top-10 university worldwide GPA: Top 10/~250 Master Degree: Finance from a top-10 university worldwide GPA: Top 10/~40 Type of student: Asian male Program desired: PhD in Statistics/Biostatistics Research Experience: coauthor of one published paper on an international journal in econometrics. I have also done 2 research projects (~ 3 months each) that were outsourced by private companies. Both were related to Statistics/Data Analysis. During undergraduate studies, I had 1 individual poster project, in which I made a poster on statistical classification using LaTeX and presented to faculty members, 1 group project on Support Vector Machine and Kernel Smoothing, which also involved writing a report and presented our work to professors, and 1 individual project on Pattern Recognition, which heavily involved R programming. Teaching: 2 semesters of TA in Probability & Statistics and Time Series. I will TA another 2 semesters this year LOR: 1 from my professor in the UK, who was my personal tutor. 1 from a professor, who is currently the director of the research institution I'm working at. Both should be strong. I can get the last letter from another professor, but whether it's strong or not is a question mark. Programming: Proficient in R, MATLAB. Competent in C++, Visual Basic. Proficient in LaTeX GRE: Verbal 162, Quantitative 170, Writing 5.0 GRE Subject Math: 820. Not sure if I should retake Coursework: most were A+, some were A and 1 B in Computational Maths. My first 2 years focused on developing a rigorous mathematical background while my final year consisted of graduate level courses, mainly in advanced Statistics. First year: Foundation of Analysis, Mathematical Methods I, Mathematical Methods II, Computational Maths, Mechanics, Probability and Statistics I, Geometry and Linear Algebra, Algebra I, Real Analysis Second year: Probability and Statistics II, Algebra II, Introduction to Numerical Analysis, Analysis I, Complex Analysis, Statistical Modelling, Differential Equations, Multivariate Calculus Third year: Statistical Pattern Recognition, Applied Statistics, Stochastic Simulation, Credit Scoring I, Scientific Computing in C++, Quantitative Finance, Survival Analysis, Games Risk & Decisions Research Interests: I'm interested in high-dimensional statistics and machine learning, with applications to chronic diseases and cancer research in particular Applying to: Biostatistics: Harvard UNC Wisconsin-Madison (biostatistics track) Minnesota - Twin Cities Rochester Statistics: CMU (joint statistics and machine learning) Yale Cornell North Carolina State Michigan - Ann Abor Ohio State Penn State Note: I'm currently working for a research institution, which is part of the national university in my country. I expect some more projects in this academic year, though I'm not sure if these result in publications. It's unlikely that these will come before the application deadlines anyways. The things that concern me most is the questionable 3rd LOR and the lack of published papers (only 1 for me). I've also heard that chances are much slimmer for international students.
  2. Hi, I am planning to apply to Statistics and Biostatistics PhD programs for Fall 2019. I'm an international student. I completed my undergraduate degree in Statistics and Math in a top 15 university. I'm currently doing a MS in Biostatistics at a top 15 university. I have a undergraduate major GPA of 3.5, I had some mental health issues that screwed up my life pretty badly during my junior and senior year, which caused a major decline in my grades in a few statistics and math classes. I've been continuously involved in one research project after another since summer of sophomore year. After I began my MS program, I tried a lot harder to put my life back on tracks, I also did my best to take graduate versions of the courses I did badly in college to try to make up for them. My graduate GPA is 3.85. As of now, I've had 4 research projects, one poster at a Statistics conference, and no publication, although I am working towards publication, so I may have one or two papers in submission by the time I apply. I'm also taking the GRE math subject test later this Fall. I'm still assembling my list of schools, but right now I have Columbia Stat, UPenn Stat, Harvard Biostat, CMU Stat, Yale Stat and Data Science, and JHU Biostat. I would appreciate any honest assessment of my profile, suggestions to programs that would suit me, and any suggestions to improve my application. Many thanks in advance!!!
  3. I know this probably isn't quite a usual post, but since a couple peers have suggested that the answer to the given rhetorical question might be "yes", I figure I should run it by the experts: I'm applying this upcoming year, and I anticipate graduating with 4 majors -- pretty much, I was double-majoring in Econ and Psych, and then added Math and Stat later on. Will admissions committees see my CV/transcript and think that, say, I have a hard time being decisive, or won't be committed to statistics long-term? Should I at least spend a portion of my personal statement explaining (in more words than above) how I came to have 4 majors, so as to help explain my situation and prevent any presumptions of waffling? While I'm at it, is there any chance admissions committees would think this is a positive? Most people here appear to be double-majors at least, so I'm assuming not, but if schools really look at GRE scores in any detail like this post (https://forum.thegradcafe.com/topic/99147-what-im-looking-at-when-i-review-applications/) would suggest, then maybe they would look at strange numbers of majors too? Well, have fun skewering me, hopefully at least I've made y'all's weekends more enjoyable by laughing at this topic.
  4. Undergrad/grad Institution: Vanderbilt Major: Molecular and Cellular Biology Minors: Scientific Computing, Chemistry, (maybe math, if I decide to take diff eq) GPA: 3.8 Type of Student: Domestic Asian Male Relevant Courses: Single variable calc 1 and 2 (5 on AP test), multivariable calc (A-), intro to stats and probability (A-), stats lab (A), biostats (A), genome science (A), thermodynamics (A-), calc-based physics lecture and lab (A), Data Science Methods for Smart Cities (A) Courses in progress: Foundations in bioinformatics (grad course), linear alg, real analysis GRE: Taking in a week, but I got 180 quantitative on all practice tests, 155-160 on verbal Career goals: Leaning towards industry. Interested in bioinformatics, and I want to get a solid education in the statistical theory behind it Programs Applying: Biostatistics PhD (only applying to MS programs if PhD apps don't work out) Research Experience: Two years worth of research at Vanderbilt (one project on RNAseq analysis, new lab focused on machine learning for genetics) One summer internship in industry focused on machine learning and medical imaging Just got back from 3 week Summer Institute in Statistical Genetics at UW. Mostly just attended lectures, but I luckily got to talk to the UW professors a little Recommendation Letters: One from biostats prof, the other two from PIs from two diff labs, one of whom is a big name in evolutionary genomics Coding Experience: R, Python, Matlab Applying to: University of Washington University of Michigan University of Minnesota UNC Yale UPenn MD Anderson Duke University of Pittsburgh Boston University University of Rochester I know my math background is lacking, but I only recently became interested in biostats, and I'm trying to make it up by taking more math courses senior year. Any tips? Or schools you would add/remove from my list?
  5. Greetings everyone! I am planning to apply for Biostatistics Ph.D./statistics master programs this fall. I am humbly and sincerely asking for your gracious advice! Below is my background: Institution: Top UC's (Statistics program ranking: 31) Major: Applied statistics (concentrating field: mathematics) I know it is weird... My intention is to learn more math since I switch my major from applied mathematics. GPA: 3.5 Type of Student: International Asian Male GRE General Test: Not taking yet, will do soon Programs Applying: Ph.D. in Biostatistics/Statistics Research Experience: 1 year of RA in biostatistical research experience and currently working on Large-scale biostatistical data mining. Teaching Experience: One-quarter of Learning assistant for calculus Letters of Recommendation: One from my research advisors, two from my statistics professors Relevant Courser Work: (P means Pass, some are optional) Math and Stat Courses: Cal 1-4: All (A's) | Linear Algebra: (B) | Elementary Statistics: (A) | Intro to Abstract Math: (C+) (with 70% of the class below C- range) | Differential equations for: (P) | Real analysis_1: Pass | Real analysis_2: C | Regression analysis: B | Statistical Data Science: B+ (right after I switched to statistics from Applied math major, without any R experience beforehand, worked really hard) | Probability theory: (P) | Time series: (C-) (was taking 5 upper divs at one quarter, trying to allay from my tuition burden, trying to finish in 4 years, therefore admitted sacrifice) | Nonparametric statistics: (C) (medical condition) | Analysis of categorical data: (C+) (medical condition) | Analysis of Variance: (B+) (mental medical condition)| Mathematical statistics: (A-) (medical condition) | Euclidean Geometry: (B+) (still in love with proof writing, want to prove myself) | Applied statistics: (B+) | Advanced statistics undergraduate independent study: (P) | Programming and problem solving: (B) (C language) Computing Skills: R (Proficient, but still learning and exploring), Matlab Applying to: UC Berkeley-biostatistics (master) Stanford - statistics (master) UCR - statistics UCSC - statistics SMU - biostatistics Duke - Biostatistics Columbia - Biostatistics University of Michigan - Biostatistics UIUC - statistics Notre Dame - Statistics University of Minnesota - Biostatistics CMU - statistics in Public policy University of Pittsburg - biostatistics NCSU - statistics Uccon - biostatistics ISU - Statistics USC - Biostatistics UCLA - biostatistics Yale - Biostatistics I am pretty sure that I am not competitive enough since both my math/statistics class rarely curved (Statistics department is known for the epitome GPA deflation ) I am preparing for the worst to come. Please enlight me and guide me through this, thanks! Please be critical for my school selection!
  6. jjj02027

    PhD applicants: Fall 2018

    Hi, Thought I'd start this topic for 2018 fall applicants specifically for Public Health. I realise there is a general one for all applicants, but thought it might be useful to have one for just a Public Health field (in the most broad sense). I apologise if this post is redundant. I just wanted comments on whether my choice of universities were too strong. Although these were the only programmes which matched my interests. Feel free to comment/share your experience so far. Undergrad Institution: Imperial College London, UK (Top for science in UK)Major(s): Biomedical SciencesMinor(s): n/aGPA in Major: no GPA system in UK (Upper 2nd class)Overall GPA: n/aPosition in Class: Type of Student: International female Postgrad: MSc in Public Health (GPA 3.88) at London School of Hygiene and Tropical MedicineGRE Scores (revised/old version):Q: 160 (76%)V: 167 (98%)W: 4.5 (82%)B:TOEFL Total: n/aResearch Experience: 2 years in health services researchAwards/Honors/Recognitions:Pertinent Activities or Jobs: some teaching assistanceAny Miscellaneous Accomplishments that Might Help:Special Bonus Points:Any Other Info That Shows Up On Your App and Might Matter:Applying to Where:Harvard, John Hopkins, UNC, UCLA, UCSF, Northwestern, Brown, Ohio State, Iowa, Standford
  7. I would like to ask for your opinion regarding my application for PhD programs in Biostatistics 2019. All programs seem to require strong background in mathematics, demonstrated by several semesters in Linear Algebra, Advanced Calculus, ideally Real Analysis and Numerical Analysis as well. Some top programs seem to favor students with previous experience in programming languages such as Python, R or MATLAB. However, I am not sure if PhD programs in Biostatistics also require previous exposure to Biology and/or Genetics of any sorts. I graduated with a bachelor and a master degree with heavy mathematical/statistical components from a top UK institution (my bachelor degree was in fact mathematics with specialization in statistics). But I am not sure if the lack of formal training in biology will be a big disadvantage to me when applying for top Biostatistics programs. Apart from PhD programs in Biostatistics, I am also applying to Statistics programs, where there are faculty members with interests in biomedical sciences. Thanks very much for your opinions.
  8. I've heard of a lot of people who applied to University of Michigan's Biostatistics PhD program and got offered admission to their MS to PhD fast track. My question is should I apply for just the MS, just the PhD, or both programs if I want to get that fast track offer? I'm a molecular and cellular biology major, with a little higher than a 3.7 math GPA (only took "multivariable calc" and "intro to statistics and probability and the associated lab", and I'm taking linear algebra this fall, maybe another math class spring semester). I also saw on the UM website's tableau graph that around 70% of applicants get into their MS program. Am I just imagining that?
  9. Hi everyone, It's near April 15 now. And I still can't decide on this two programs. --- UNC pros: Higher rank in biostatistics, and better location (triangle area). I do believe UNC is more than well-regarded in this field. Does it mean their student will have better opportunities in finding academics and industry jobs? UNC cons: As an international student, I still haven't received any funding. And it looks like it has always been a problem for UNC. To me, it kills the creativity when you have to worry about money in PhD life. --- UW Madison pros: It's full-funded. The curriculum of UW Madison seems legit to me. They provide 3 course sequences in Biostatistics Theory and Methods, Computer Science, and some Specialization Topics. I really like the CS sequences, because I'm major in Statistics now, and didn't get much CS training, which I think will be of huge importance in graduate study & research. UW Madison cons: As opposed to the UNC pros, it's not as highly ranked. The location and weather are not as good I guess? And it is a new program. I literally can't find any insider's experience. There are too many uncertainties. --- Any thoughts on this two programs? Please help me, any comments or thoughts will be much appreciated. Thanks!!!
  10. I am trying to decide between two MS Biostatistics programs and am wondering if anyone can offer insight. 1. Boston University MS Applied Biostatistics. This is a 12 month (full time) program is in a School of Public Health, but incorporates a research component (500 hours total). 2. Northwestern University MS Biostatistics. This is also a 12 month (full time) program in the School of Medicine. My goal is to work as a data analyst in a hospital or university, with specific interests in clinical epidemiology. Although I’m still open to pursuing a PhD (most likely in Epidemiology), I also want a robust quantitative background that would allow me to work after the Masters. Both are similar in that they are more applied biostatistics programs that are flexible if I choose to later on work full or part time during the program. I feel as though Northwestern has a better name/prestige but BU’s main draw is the location in Boston which has so much opportunity in the biomedical sector. However I’m worried that even if I were to choose BU that I would be overshadowed by those with more prestigious degrees or schools in the Boston area. If you have any advice, please help me in my decision. Edit: In terms of program “fit”, I’m attracted to BU because I’ll be closer to my long-term partner who was also accepted to another school in Boston. In terms of research interests, Northwestern has an edge (and less competition for opportunities due to the smaller program).
  11. Hey everyone! I am admitted to MS biostatistics programs at UNC and Michigan. Both schools have an internal process and a preference of admitting their own master’s students to their own PhD program, especially UNC. I was wondering would it be bad if if apply for an internal transfer as well as trying to apply for other TOP biostatistics programs like Harvard or JHU in the meantime? Would it be a bad impression for the faculties? Thank you!
  12. Hello all, So my name is John Thomas, rising senior at Ursinus College. I am doing undergraduate research at both Ursinus and Temple, and doing an internship at a national taste testing facility known as RDTeam. I created my own major in Statistics here and am the first of my kind. Starting out as Bio, I had grades in the mid 2's , speaking GPA. I have climbed up to a 3.12 cumulative and will be taking probability in the fall. This is also when I will be applying but my fear is that I will not be considered since I will not have taken 3 courses in my major (Differential Equations, Mathematical Statistics, and Linear Regressions) at the time of my application. I would ideally like to get into a PhD program right out of college but I fear without these courses on my transcript I will not be able to. What are some ways I can overshadow this, or should I just put all my marbles into starting out as a Masters student? Also, if anyone has any east coast theoretical statistics programs, please list! Thank you all -John
  13. Hello! I've been accepted into Columbia and UNC's Biostatistics MS programs and I'm having trouble deciding between the two! I'm leaning towards Columbia but it worries me that their Stats department is apparently not well regarded. Does this reputation extend to their Biostats department, even though their department is still in the top 10? I know that UNC is a better program overall and has more renowned professors, but if my end goal is to pursue a PhD, does it matter where I get my MS? Cost is not a strong factor because I consider living in NYC to be a once in a lifetime opportunity. I am also more interested in eventually working in industry if that helps. (I've heard that Columbia has more ties in industry and UNC is more theoretical with difficult quals) Thanks so much for any replies!
  14. So I received a PhD offer from UNC Biostats last week, yet the offer letter did not mention funding details and says 'funding will be determined separately'. I looked through several threads about UNC biostats from last year, and found some applicant admitted to UNC biostats PhD program seems to be unfunded (at least until last March, the date that thread was posted). I am an international student and I feel it might be more difficult for me get funded, so I am a bit worried, and wanted to ask if anyone knows about the funding opportunity at UNC. Thanks a lot!
  15. So, those are the only two schools I got into. University of Maryland SPH, and Milken Institute SPH (GWU). Milken is significantly more expensive (about double UMD) but doable w/ loans if I work part time. I'm fine with both of the actual programs themselves in terms of courses offered, but is either one held in significantly higher regard than the other? Will the school's reputation matter much for an MPH in Biostatistics anyway? I talked to a couple of advisors but didn't get any definite answers.
  16. I was wondering how much weight does having a decent GRE Math Subject Test has on an application. If so, what should the target range be for domestic/international students?
  17. I recently received admission offers from U of Michigan's MS in Biostatistics program and Purdue's Statistics program, and I'm having trouble deciding between the two! I plan to pursue a Ph.D. after graduation. Both programs have pros and cons, and it is tough to reach a decision. For example, the U of Michigan's Ph.D. in Biostatistics highly prefer their own MS students and has a higher ranking. While Purdue allows MS students, who wish to transfer to the Ph.D. program take the qualifying exams. I don't have a clear research interest yet. Will an MS or Ph.D. in Biostatistics narrow my choice in the future? I'm leaning towards Purdue since I am more interested in working in IT industry. Thanks in advance for any suggestions!
  18. I'm trying to decide between M.S. Biostatistics at Columbia and M.S. Applied Biostatistics at Boston U. The appealing thing about Boston U is that the program is only one year, as opposed to two at Columbia. Anyone familiar with the program at Boston U and if it would be better to tough it out for the extra year at Columbia for the benefit in the long run? Thanks!
  19. Touch Chicken

    UTHealth at Houston 2018

    Hi all, I am going to UTHealth for PhD in biostatistics this fall. Is anyone going for the admitted student day on April 6? Never been to Houston before, hopefully, it will be a fun trip.
  20. Does anyone know about biostatistic programs in Canada and how they are viewed in the academic/research field? Specifically McGill/uoft/waterloo. From what I have found McGill - some good professors (highly cited/good journals). Students are competitive in top PhD programs like Harvard/UNC/UW/JH . DLSPH- The school itself is at top public health school in the world. The statistics department at u of t is highly regarded as well. I do not know a lot about the biostatistics program, however, seems very applied. Waterloo - Heard this program is the best in terms of training (though not necessarily global reputation). People who work in the field have told me that Waterloo graduates are highly skilled.
  21. I received offers from the Dalla Lana School of Public Health at U of T, Waterloo and Mcgill for their MSc Biostatistics programs. There does not appear to be much information out there on biostatistic programs in Canada. Does anyone have any insight on the pros and cons of these three programs? How are they viewed in the biostatistics field? I am not completely decided on a a PhD yet.
  22. I'm fortunate to have been accepted to both programs. Based solely on research faculty and rankings, which is stronger? I can't find much information regarding the biostats program. Is it clumped together with the bio, med, or stats dept? In addition, if I'm looking for industry work which would look stronger? If you have any info regarding internal PhD conversion rates or on whether Duke's program has been on the up and up please let me know!!
  23. There are a few other current PhD students who frequent this forum. I've visited it on and off over the years, but I have not seen many posts from current PhD students about their experiences. I thought this may be of interest to potential applicants, so I decided to write about what I have learned (I am about to graduate, finishing my final defense and thesis in May). I am happy to report that my PhD experience was largely positive. 1) A PhD program is fundamentally a research degree, and research is nothing like taking classes. I think some Stat/Biostatistics programs do a great job of involving students in research early on through rotations with different professors or through reading courses to familiarize students with statistical literature. But there are a lot of programs where students do not start research until the end of their second year. And I have seen many PhD students who were very, very bright (acing all their classes, 4.0 GPA, etc.) but who really struggled with transitioning from being a student to becoming a researcher. I definitely think you should work hard in your classes so you can pass your written qualifying exams and so you can developed a solid foundational understanding, but once you get to the research stage of the program, you really do have to teach yourself a whole new area. Moreover, research is about discovering something new and pushing the boundary of your field. There is just no way of knowing if some "open problems" can be solved or not! It's not like solving a problem on a homework set where there is generally one correct solution/approach. If you do a theoretical topic for your dissertation, you need to prove new theorems that have never been established before, not just "show” something that already has a known solution. And even if you start working on a problem, you may get stuck for long periods of time (or need to cut your losses and give up), or you may end up somewhere completely different from where you started. Unlike problem sets and exams, there are no concrete solutions. For example, for the first paper that I wrote, I was stuck on a proof for my main theorem for three whole months. Nothing I tried seemed to work! But my PhD advisor pushed me to keep trying, and eventually I found the technique that worked. Phew! 2) A lot of the learning in grad school happens outside the classroom, and you need to ask questions. This comes from talking with your peers, meeting with your advisor, attending departmental seminars, and reading papers. Here is the thing: when most people start research, they do not yet have the skills to really excel at it. A small number of people are able to excel right from the get-go, but for most people, it takes a bit of adjustment, and that's okay! It is important to reach out for help if you need it. If I didn't understand an author's proof or a new concept that I had never encountered before, I would ask my advisor to help me. I didn't have much experience with high-performance computing or running simulations on multiprocessing systems, so I asked my more experienced classmates to help show me how to navigate it. 3) Everybody thinks about quitting at some point. This is perfectly normal. A PhD can be a very demoralizing, frustrating experience. Plus, things can happen in your personal life that can derail you. It's just part of life. When I felt like quitting, I just took some time off... maybe 2-4 days of not doing any work to recuperate and assess why I was putting myself through the PhD. After some time off (not too much time off), I could reason to myself why I wanted to get a PhD, and I got right back to work. So if this happens to you, accept your feelings, take a breather, and then really question your own motivations for pursuing a PhD. If you can answer this question to yourself, "Why do I want a PhD? Am I willing to 'tough' it out when I'm feeling frustrated?", then you will be able to pick up right where you left off. 4) Just about EVERYBODY gets their papers rejected, even Distinguished Professors and Nobel Prize winners. My PhD advisor has co-authored over 250 papers and is quite smart, and he still has papers rejected. Professors at all levels get their papers rejected, some multiple times before they are finally published. It’s part of the process. It also happened to me for the first paper I ever submitted. Rejection always stings, but I say if it happens, take a deep breath and cool off a bit. Once you’ve acknowledged the disappointment and cooled off, read the referee reports and comments from the Associate Editor very carefully. Peer review is inherently a subjective process, but for the most part, paper referees take their jobs very seriously, and there will be valid concerns and comments for improving your manuscript (even if some might not be the most diplomatic when letting you know the faults they find with it!). It may be that the journal you submitted to just might not be the most appropriate venue for your work. Or there may be more substantive changes that are needed to make your manuscript more acceptable for publication. After my first paper was rejected, I spent a lot of time with my advisor revising it. We eventually re-worked the whole paper (e.g. cutting down the length of the literature review to the most essential points), we proved a new lemma and a new theorem that showed our new estimator’s improvement over previous estimators, and we performed several new simulation studies that showed quite interesting results. We just resubmitted this paper, making appropriate changes suggested by the peer reviewers who had rejected the manuscript, and I have to say my paper was way better than before. The paper was better off in the long-run. 5) The choice of PhD advisor is critical. It's very important that your PhD advisor is someone whom you can have a great working relationship with, whose research is interesting to you personally, and who is actively publishing in respectable journals. I think the last two are more important than anything else, especially for academic jobs. You basically need to have quality papers and excellent recommendation letters if you want to get a good postdoc or faculty position. Some PhD students are hesitant to work with Assistant Professors and are "star-struck" but there's really no point working with a world-renowned professor if their mentorship style and their research does not align with your personal working style/interests. Plus, an Assistant Professor who is actively publishing their work in top journals can still help you develop your career. Some people need a bit more guidance and an advisor who gently “pushes” them, while others can operate fairly independently and do not need to meet their advisor very frequently. The working style of you and your advisor should mesh well if you hope to be productive. 6) The fields of statistics and biostatistics change very rapidly, so it's more important that you do research that "comes from the heart" than try to keep up with a "hot area." I would not recommend researching a topic that is so archaic and obscure that only a tiny number of people in the world are still working on it. But I also think that you should prioritize your personal interests above what's currently "hot." It can be very difficult to predict what will be "hot" years from now. For example, Dirichlet processes were not very popular when the concept was first introduced, but decades later, Bayesian nonparametrics have exploded in the field of machine learning. It used to be that SVMs were very popular and neural networks lost some of their popularity, but currently, it is the opposite. There is an explosion of interest in neural networks/deep learning and not as much in SVM. The fields of statistics and biostatistics are constantly evolving and changing, so trying to "time" your thesis to a "hot area" can be tricky. But most importantly, a PhD is a very time-consuming commitment (at least 2 years of research). So you do not want to be miserable the whole time you are doing it. So make sure to pick a thesis topic that you find interesting. You probably won’t be able to do that yourself at first, but to that end, your advisor will help you hone in on some interesting open problems to work on. Do not do a topic that you have no personal interest in! Sure, some people might be more impressed if you do (what they perceive to be) a more "difficult" topic, but at the end of the day, you're the one who has to live with yourself and your career choices. And if your heart just isn't into it, it will make finishing the PhD much more excruciating. 7) Do not assume that your PhD thesis topic is the only thing you will work on for the rest of your career. To tie in with my previous point, you can always change gears and switch to a “hot” research area after you are done with your PhD. Finishing the PhD is the start of your career and certainly not where you want to peak. A PhD dissertation is usually on a specific, narrow topic or set of topics. Some people are lucky and can milk their research area for the rest of their career, but many people aren't that lucky. Even if you want to go into industry, an employer of PhD graduates is going to expect that you can teach yourself new things (new software, new models, etc.) on the fly, even if you've never seen/used these things before. In fact, it is this creativity and ability to learn new things quickly that makes hiring a PhD graduate more appealing than hiring someone with juts a Masters. Likewise in academia, professors are teaching themselves new things and moving into new areas all the time. My own PhD advisor began his career doing frequentist nonparametric statistics, but now he has research in a variety of areas of Bayesian statistics. The postdocs I am currently considering are in entirely new areas that I haven't learned before. By the end of the PhD, you should ideally have enough maturity and initiative to teach yourself different areas of statistics.
  24. Hello! I am very stuck between Michigan and Emory right now. I know Michigan has a better biostatistics program than Emory. But in terms of location, I like Atlanta way better than Ann Arbor. In addition, I think the coursework (and the Qual) at Michigan might also be much harder than Emory. My plan after graduation is to find a job (Pharma/Tech/Consulting), so will there be a huge difference between these two programs? I know PhD students at Emory can find an outside intern job during the summer. I'm not sure if I could do the same at Michigan. Any comments are very welcomed, like the pros and cons for each of them? Thanks! I appreciate that very much.
  25. Has anyone received offers from Canadian MSc programs in Biostatistics?
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