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AnnieM

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About AnnieM

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    Woman
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    she/her
  • Location
    Burlingame, CA
  • Interests
    HIV research, yoga, running, baking, math, statistics

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  1. I got this too... I'm not sure if it definitively means we are not getting an interview, but I suppose it could.
  2. Has anyone heard from the Biomedical Informatics (BMI) home program?
  3. I applied and I haven't heard from this program yet.
  4. I wouldn't sell yourself too short, you never know, it's such a crap shoot and why not aim high while keeping a decent safety net? I was a Biostat Masters grad from UC Berkeley, and spent a lot of my time working in the Berkeley Stat department - and what I learned from their grad students as well as their policies is that they value high grades above all else. The masters students in the Stat department were automatically not offered spots in the PhD program after their graduation if they earned even one B+ in their masters coursework. The grades had to be all A's. So, it may be tough for you there, but you never know. It's worth finding out what each department values. I honestly think you seem very well-prepared for a machine learning PhD and certainly a great program exists that will be an awesome fit. Stanford, Berkeley, Harvard, Washington - those are the top-of-the-top schools, basically a long shot for nearly everyone. Doesn't mean you shouldn't apply. I visited UCLA (for Biostat, not Stat), and I loved the vibe there. I also knew several grad students in the UC Berkeley Statistics dept that came from UC Davis Stats dept. Have you considered NC State and/or UNC? Duke? What about Michigan, Wisconsin-Madison, or Minnesota-Twin Cities? All have great programs from what I hear. Best of luck
  5. Hi all, I'm applying to PhD programs for entrance Fall 2020. I'm in the process of narrowing down which programs best suit my research interests, but even after viewing faculty research profiles and general descriptions, I'm having trouble parsing the finer details of each choice from this readily available info, such as, what's it like to be a student there? How strong is their research in the areas I'm interested in? Etc. My research goal has been the same for the past 20 years: to find a cure for HIV infection... or a vaccine... eventually, both. I had the idea as an undergraduate when I studied Immunology and also took several Calculus courses. I thought, why not approach a cure or vaccine from a mathematical standpoint? What if the biological problem of random mutation (and other issues making HIV hard to eradicate in the body) could be modeled mathematically, then computational methods (data science) and new engineering techniques (such as CRISP-R) could be applied to come up with a solution, or at least a more detailed description of the problem? Giving this info as background to help clarify what I'm looking for, and what programs may be a good fit. Also, in the event that a cure and vaccine are developed, I am still very interested in applying the same principle (mathematical modeling of biological systems and pathology to aid in development of disease treatments) to other areas of medicine. I have a B.S. in Molecular Biology from Vanderbilt ('01), and an M.A. in Biostatistics from UC Berkeley ('12). I'm a white female, 40 years old - kindly asking please no disparaging comments on my age, I don't care so much that I'm ancient compared to many applicants! I won't go into the finer details of my background, because in this post I am interested in which program would truly be a good research fit, not my chances of getting in (I'll cross that bridge when I come it it - suffice to say, I have the basic prerequisites to apply to all of these programs). I have good research experience, wrote a paper on it for publication (not published... long story), have worked in the health sciences and data science & analytics since I graduated from Berkeley, did outreach work for people living with HIV/AIDS in college, am generally a humanitarian type with a curious, scientific mind, and I LOVE math. Did one year of med school before dropping out due to a serious illness in my early 20's. Since I'm more research-focused, I'd like to get a Ph.D. or an MD/PhD (but, I'd really like to avoid taking the MCAT again, TBH). The programs I'm curious about are: 1) Harvard-MIT MEMP (this seems a great fit to me, very interested in Arup Chakraborty's work as well as Bruce Walker's) 2) Stanford Biomedical Informatics - seems to be the right program at Stanford for what I'm after, but I also looked at Bioengineering and Immunology... anyone's opinion on these and their fit with my research goals is most welcome! 3) Harvard BIG or BBS - they both look awesome, I can't tell which is a better fit, but possibly BIG... though I like the sound of the interdisciplinary nature of BBS and how much students seem to love that program. That said, the research fit is really the crucial piece. These are the ones I've looked at but have been unable to really tell which is the best fit. I mention Harvard-MIT MEMP because it seems so ideal for my goals, but I just learned about the program this past year, so I'm curious if anyone has been more involved in it and/or knows what it's like to be a grad student there. Thank you whoever read this whole thing for your help, I truly appreciate it!! If anyone is a current or past student in any of these programs, I would love to get your perspective. Also, if anyone is a student in a program that would be a great fit based on my research interests, I'd also love to hear about it.
  6. I graduated from the Masters Biostatistics program at UC Berkeley in 2012... based on your credentials, you are excellently set up for success for any Masters Biostats program IMO. You have way more going for you in your resume than what I had when I applied & got in! That said, I think it's true that if you wait a year to apply, and ace those math classes, you will be a top candidate at any program. They really do care how you do in hard quantitative courses. And I can tell you, it's good that they do, because if you can't handle a difficult math course, you will likely be in misery if not total failure in a typical Biostats Masters program. We took a certain grad-level probability course the first semester that was... not for the faint of heart. People with strong math background did well, but those of use with less math credentials had to put a lot of work in. It's doable, you can do it, but it's better to come in with strong knowledge of Calculus especially, as well as at least one class in Statistics. I entered the program having only taken ONE Statistics course, the lowest-level Intro to Stats undergraduate course, but it really saved me, because I knew the concepts they were talking about in my classes, even if at a much more introductory level. I loved Calculus so it was a good fit, but I had to brush up for sure. Linear Algebra is also involved, but I found that to be mostly matrix operations, not too challenging compared to the Linear Algebra course I took to satisfy prereqs. Also, you won't be alone in not having a pure math background. Many people had less math credentials than those who go into pure Statistics programs, and they still got in and did fine. If you do apply this year, a great support would be an excellent GRE quantitative score. I was told by a student who sat in on the admissions committee at Berkeley Biostats that the committee members think the GRE quant score is a good indicator of how well a student will do in the program. That was several years ago, but still... worth considering. For a Masters program, I think you have more than enough research experience. In the interest of strengthening your application, if I were you, I'd move on to satisfying all those math prereqs and doing well in them. Grades are important, but I got in with a B+ in lower-division Linear Algebra and after a challenging first year, I ended up doing well in the program. I took upper-division Linear Algebra and Real Analysis while in my grad program at Berkeley, and those helped... *particularly* the Real Analysis. I think that class should be a prerequisite for any Stats program at all, but I know that would limit those who can apply, as it's an upper-div math class. It helped tremendously, I cannot emphasize that enough, and my classmate who also took Real Analysis while we were in our Biostats grad program felt the exact same way. Also, they seemed to care how interested you are in attending their specific program. There are many tactful ways of indicating interest in your first-choice program, such as attending applicant info sessions, contacting current students, etc. It's maybe considered risky according to some, but I think *very brief* emails to professors whose research interests you can go a long way (speaking from experience). Best of luck!!!! You got this!
  7. Hi all, I'm applying to PhD programs for entrance Fall 2020. I'm in the process of narrowing down which programs best suit my research interests, but even after viewing faculty research profiles and general descriptions, I'm having trouble parsing the finer details of each choice from this readily available info, such as, what's it like to be a student there? How strong is their research in the areas I'm interested in? Etc. My research goal has been the same for the past 20 years: to find a cure for HIV infection... or a vaccine... eventually, both. I had the idea as an undergraduate when I studied Immunology and also took several Calculus courses. I thought, why not approach a cure or vaccine from a mathematical standpoint? What if the biological problem of random mutation (and other issues making HIV hard to eradicate in the body) could be modeled mathematically, then computational methods (data science) and new engineering techniques (such as CRISP-R) could be applied to come up with a solution, or at least a more detailed description of the problem? Giving this info as background to help clarify what I'm looking for, and what programs may be a good fit. Also, in the event that a cure and vaccine are developed, I am still very interested in applying the same principle (mathematical modeling of biological systems and pathology to aid in development of disease treatments) to other areas of medicine. I have a B.S. in Molecular Biology from Vanderbilt ('01), and an M.A. in Biostatistics from UC Berkeley ('12). I'm a white female, 40 years old - kindly asking please no disparaging comments on my age, I don't care so much that I'm ancient compared to many applicants! I won't go into the finer details of my background, because in this post I am interested in which program would truly be a good research fit, not my chances of getting in (I'll cross that bridge when I come it it - suffice to say, I have the basic prerequisites to apply to all of these programs). I have good research experience, wrote a paper on it for publication (not published... long story), have worked in the health sciences and data science & analytics since I graduated from Berkeley, did outreach work for people living with HIV/AIDS in college, am generally a humanitarian type with a curious, scientific mind, and I LOVE math. Did one year of med school before dropping out due to a serious illness in my early 20's. Since I'm more research-focused, I'd like to get a Ph.D. or an MD/PhD (but, I'd really like to avoid taking the MCAT again, TBH). The programs I'm curious about are: 1) Harvard-MIT MEMP (this seems a great fit to me, very interested in Arup Chakraborty's work as well as Bruce Walker's) 2) Stanford Biomedical Informatics - seems to be the right program at Stanford for what I'm after, but I also looked at Bioengineering and Immunology... anyone's opinion on these and their fit with my research goals is most welcome! 3) Harvard BIG or BBS - they both look awesome, I can't tell which is a better fit, but possibly BIG... though I like the sound of the interdisciplinary nature of BBS and how much students seem to love that program. That said, the research fit is really the crucial piece. These are the ones I've looked at but have been unable to really tell which is the best fit. I mention Harvard-MIT MEMP because it seems so ideal for my goals, but I just learned about the program this past year, so I'm curious if anyone has been more involved in it and/or knows what it's like to be a grad student there. Thank you whoever read this whole thing for your help, I truly appreciate it!! If anyone is a current or past student in any of these programs, I would love to get your perspective. Also, if anyone is a student in a program that would be a great fit based on my research interests, I'd also love to hear about it.
  8. That makes total sense, the logistics and expenses are a huge factor No prob, wish you the best!
  9. Hi there, you're welcome, happy to help... yeah I think the average GPAs are a bit scary to look at - there will be some people with 3.9-4.0 GPAs, but that brings the average way up, so there are likely many below that. Plus, they do look at the overall application. UCB MPH is maybe less competitive, but when I was there, it seemed they didn't take a lot of classes with us. There was some overlap, but that program was much less math-heavy. Also, the benefit of the Biostatistics program is the close relationship with Berkeley's Statistics department. It's easy to get teaching jobs in the Stats department, which gives you funding, and they are very nice. I loved that aspect of Berkeley Biostats. That is just my experience and preference - it really depends on how quantitative you want your classes and research to be. Not sure what your situation is, but if you could take a year to finish some of those prerequisites and apply next year, I would do that. It's just another year, goes by quickly, then you've opened up so many options. I don't know how online courses are viewed, so you'd have to ask the admissions people in each department, but I took a course in C programming online so I could satisfy a prerequisite. Either way, I really think taking a long shot and applying to some top-notch places even if you think the chances are slim, is a really good idea. You never know.
  10. Hi there, I graduated from UC Berkeley's masters program in Biostatistics in 2012. From what you have posted, you seem to me to be more than fine to apply. I had a very similar GPA - my overall GPA was 3.51, and I think it was 3.7 or 3.75 in my math classes. I don't think it's a problem at all that you had a low GPA your first semester in college. I got a C in a prerequisite math class, that I retook and still only ended up with a B+, and I got in. I know that Berkeley looks a lot at the GRE score (told to me by a student when I applied) - they said it's a good indicator of success. I had a 780/800 in the quant section in the old version of the test. Looking at a conversion scale, I guess that's a 163 in the current version, though I know they decided to make it harder because a ton of people were getting 800's on the quantitative section in the old version. They have an info session for applicants in the fall, and I went to it, and was told later it helped my application. It seemed that they want to accept people who want to go there. I had the same experience with UCLA - I wouldn't have stuck out from the pile of applicants, but I emailed someone there and asked if I could visit to find out about the program, they said sure, and it went very well and I got in there, too. Tactfully showing interest goes a long way in my experience. That said, I did the same thing with Harvard (asked to visit, they said sure, I went and met with a few folks), and the admissions clerk flat-out told me I wouldn't be considered because I didn't have an undergrad Math major. My GRE scores, though higher than their average for accepted applicants, did not matter one bit. She was very polite about it, I was not offended. I still applied but I did not get into Harvard Biostatistics. The fact that you have an undergraduate degree in Math will really help you with admissions committees (see comments on Harvard above - have you considered applying there? They have a great program). From the stats you posted, I think you have more than cleared the baseline to be a strong applicant. The process is very arbitrary and very good candidates like you still do not get in due to that, but there is no reason not to apply based on your background. One other thing a student mentioned is to make sure you get letters of rec from people who will write you a strong letter (you can even ask "do you feel you would be able to write me a strong letter of recommendation?"), and make sure not to get someone who just says "this person got an A in my class". It seems like everyone has something on their application they think isn't perfect and will prevent them from getting into a program. I think they are more flexible than we think. I also think that a "perfect" resume will not guarantee admission anywhere. I wish you best of luck!!!
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