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cyberwulf last won the day on December 7 2017
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About cyberwulf

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Biostatistics (faculty)
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welkin reacted to a post in a topic: Before you start agonizing over your personal/research statement for stat or biostat, read this.

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Fall 2018 Statistics Applicant Thread
cyberwulf replied to Bayesian1701's topic in Mathematics and Statistics
Yes, that's how I interpret it. 
2018 Applicant Profiles and Admission Results for Statistics/Biostatistics
cyberwulf replied to Bayesian1701's topic in Mathematics and Statistics
Wait, what? You have a 4.0 in math/stat from one of the top schools in the country and great GRE scores, but you didn't get into Michigan and you're saying this was "better than expected"? Something doesn't add up here. 
cyberwulf reacted to a post in a topic: Best computer to get?

cyberwolf reacted to a post in a topic: What I'm looking at when I review applications

cyberwolf reacted to a post in a topic: What I'm looking at when I review applications

cyberwulf reacted to a post in a topic: 2018 USNWRRankings (Statistics/Biostatistics)

2018 USNWRRankings (Statistics/Biostatistics)
cyberwulf replied to GoPackGo89's topic in Mathematics and Statistics
Well, so much for my theory! 
2018 USNWRRankings (Statistics/Biostatistics)
cyberwulf replied to GoPackGo89's topic in Mathematics and Statistics
It's awfully strange. I note that Pitt *stat* is ranked (fairly decently) all of a sudden, whereas it wasn't even listed in 2014. My best guess is that they screwed up and ascribed scores for Pitt biostat to Pitt stat. 
cyberwulf reacted to a post in a topic: Practical Statistics/Biostatistics PhD survival guide from someone who is about to graduate

rosebud123 reacted to a post in a topic: Harvard biostatistics

Yes. Of course, within each subarea of biostatistics there is a range of mathematical sophistication. For example, within causal inference there is plenty of heavy mathematical lifting in the theory of semiparametric efficiency for doubly robust estimators.

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What I'm looking at when I review applications
cyberwulf replied to cyberwulf's topic in Mathematics and Statistics
It really depends on the quality of the publication(s), and if there is strong evidence (from recommendation letters) that the student did much of the work in developing and writing the paper. The problem with undergraduate publications is that they are sometimes more a function of "luck" (being in the right research group at the right time) than ability; also, faculty can have very different standards for the contribution required to be first or coauthor on a paper. 
cyberwulf reacted to a post in a topic: Top 3 Biostatistics vs top 10 Statistics Ph.D.

cyberwulf reacted to a post in a topic: Fall 2018 Statistics Applicant Thread

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What I'm looking at when I review applications
cyberwulf replied to cyberwulf's topic in Mathematics and Statistics
This is a pretty unusual situation, but coming with your own funding changes the admissions calculus somewhat. Provided the program has the capacity, they may be more willing to admit you provided they think you can successfully complete the degree. 
Statistics Grad School Application Guidance
cyberwulf replied to Taheel's topic in Mathematics and Statistics
Combined with courses in probability and mathematical statistics, yes. 
What I'm looking at when I review applications
cyberwulf replied to cyberwulf's topic in Mathematics and Statistics
It's hard to give numbers to these, since the importance is very contextdependent. For example, if someone has taken (and done well in) a number of advanced mathematics courses, then a B (say) in Calc 3 or Linear Algebra isn't a big deal. On the other hand, if that's the most advanced math on your transcript, then it's much more of a concern. Some general rules, though:  The real analysis grade is very important, particularly if it's your most advanced class. It's not uncommon to see students with high grades in Calc and Linear Algebra get a low grade in RA, so if you do well that will help you.  Other pure/advanced math courses play a similar role to analysis; so, for example, getting A's in Abstract Algebra and Topology might help you overcome a lower RA grade.  Statistics courses outside of probability and math stat don't carry much weight, whether they're taken at the undergraduate or graduate level. The one exception is for students who are doing a Masters (or taking Masterslevel courses) at a highlyranked program.  Nonmath quantitative courses can help bolster your application if you're light on math; otherwise, they don't carry much weight.  Electives courses generally don't matter much unless there is something very concerning there; for instance, you got low grades in all the classes that involved writing. 
What I'm looking at when I review applications
cyberwulf replied to cyberwulf's topic in Mathematics and Statistics
It's hard to elaborate more on that, since as noted in my post the evaluation involves balancing a number of factors. 
What I'm looking at when I review applications
cyberwulf replied to cyberwulf's topic in Mathematics and Statistics
Yep, that does happen from time to time. 
What I'm looking at when I review applications
cyberwulf posted a topic in Mathematics and Statistics
Well, the first round of application deadlines has come and gone, and soon your applications will be in the hands of admissions committees at programs around the country. From the outside, the process likely seems pretty mysterious, so I thought I would give an overview of how I review PhD applications. DISCLAIMER #1: My approach does not necessarily reflect how other admissions committee members perform their reviews. DISCLAIMER #2: This description applies to PhD applications, where the goal is to identify and rank the most promising applicants; the process is different for Masters admissions, where the goal is to figure out whether applicants meet a given standard.  The process begins when we receive a list of applicants whose applications are ready to be reviewed (i.e., they are sufficiently "complete"). For each applicant, we typically have access to individual documents (transcript, letters, research statement, etc.) along with a combined PDF file that has all the relevant information.  First, I get a feel for what type of applicant this is. There are five common types: domestic students coming from undergrad, domestic students attending Masters programs, international students attending US undergrads, international students attending US masters programs, and international students attending undergrad in their home country. I'll also note the institution(s) attend(ed). This sets the expectation for what I will be looking for in the application.  Next, I'm likely to notice standardized test scores. Both are going to help me start forming my impression of your application. Basically, I'm looking for anything concerning (e.g., a low GRE quant score) or particularly impressive (a high verbal and/or analytical writing score); if they're in the "solid" range, I don't pay much attention to specific numbers or percentiles.  One of the things I pay closest attention to is the transcript. I'll start by doing a quick scan to get a rough sense of overall performance; then I'll look more carefully at the courses. I'll start by looking at how many math courses were taken, and how well the applicant did in them. If there are some lower grades on the transcript, I'm interested to see whether they're mostly in "heavier" courses (such as organic chemistry) or "lighter" ones. In evaluating the transcript, I very much keep in mind the institution attended; if I've never heard of a school (and I've heard of a lot of schools, through my experience in admissions), anything less than a nearperfect GPA is likely going to be an issue, and conversely, if an institution is known for grade deflation, a lower GPA might not be fatal.  At this point, if there is anything unusual in the transcript or the rest of the application that seems to beg for an explanation, I'll take a look at the personal statement. Otherwise, I'm unlikely to give it much more than a quick glance.  Last come the letters of recommendation. The vast, vast majority of them are quite positive, so I am looking both for subtleties in tone ("this student was great!" vs. "this student was AMAZING!") and for specific distinguishing details ("this student received the highest grade in my class, by a mile" or "within 3 months of starting to work with me, this student was operating at the level of a PhD student") that add information beyond what I already got from the transcript and test scores. I pay some attention to the academic rank and seniority of the letter writer (the statement "this is the best student I've ever worked with" means more coming from a senior full professor than a secondyear assistant prof), but don't recognize most of the names so am not often "impressed" by the stature of letter writers.  Now, it comes time to score the application. At our institution, we use a categorical scoring system with options ranging from "I strongly object to admitting this applicant" to "I strongly support admitting this applicant". In assigning the score, I keep in mind the total number of people we are likely to admit (which is determined by projected available funding, and discussed before admissions decisions are made), and I try to give "supportive" scores to about this number of applicants. I keep a mental note of applicants that I'd like to discuss with the full admissions committee, particularly if I suspect my score is likely to be substantially higher than my colleagues'.  The last step involves the admissions committee discussing scores and ranking applicants. Our initial ranking is based on the average score assigned by committee members, and from this we can usually identify some "obvious" admits and rejects. Then, we discuss the remaining applicants and determine our final ordering. 
If anything, I think you might be aiming a little low for Masters admission.

I think you'd be a solid candidate for most Masters programs in the field.

Drop names or don't, it probably doesn't matter. I guess I would lean slightly against doing it, since you do run the risk of looking uninformed if the given professor isn't really active, etc. The basic idea is that if you're not confident talking about the research that individual faculty members are doing, you probably shouldn't be spending a lot of space on it in the SOP.