<p>It is my dream to someday get into graduate school and have a career in academia. I originally planned to major in biochemistry/biophysics and enter graduate school immediately after earning my undergraduate. However, I have recently discovered a program that my school offers, in computational mathematical sciences, which I think may be a better option for me. It is an interdisciplinary program focusing on computing, mathematics, and science. Specifically it is designed to teach the discipline of using computing and simulation to solve applied problems in practically any field of study. Programming, theory and use of algorithms, a full sequence of calculus and other advanced mathematics, and a science sequence are all included. It also teaches scientific computing and other applied computational methods. I am considering this because it would allow me to develop strong analytical and reasoning skills, as well as give me a larger skill set and more options. If I go this route I may work for a bit after earning my bachelors, in a mathematics/computing/science oriented field, before going back to school to pursue a career in academia. The only thing I am concerned about is this major affecting my ability to be admitted to and succeed in graduate school. If I, say, worked for some odd years in computing, actuarial science, finance, ect. and then decided to apply to graduate school to research something such as neurology, biochemistry, physics, or another highly theoretical science discipline, would I be less likely to be admitted because I wasn't a straight science major and only earned about 20 science sequence credits? Do graduate schools care much about what prospective students earned their undergrad in? And furthermore, would I be at a significant disadvantage to those who majored in a straight science in their undergrad? It's a double-edged sword because this major would give me strong analytical/reasoning skills and allow me to apply the principles of scientific computing, which I could use to my advantage in grad school and in research, however I would have a limited background in the sciences, as opposed to those who majored in a straight science. Would this be a good option for what I am trying to accomplish, or would I be better off with a straight science major?</p>
<p>There is a lot of computation in science research, however, if your focus is on the mathematics and computation, and not on a specific science, then you would have a significant number of remedial courses to take in a science graduate program. The reason, I assume is that your 20 science credits would not be concentrated in one specific science but across the sciences and you probably would be missing key courses in any one science discipline. You would probably be in a position to step right into a graduate program in applied mathematics though… Frankly, if you are thinking about physics as a graduate program, you might consider taking the degree in physics with a concentration on computation. This is not unreasonable as many physics programs already have a significant computational component.</p>
<p>My main areas of interest are physics, biology, computation, and mathematics. The only concern I have with going for physics with a concentration in computation is that I would not be well enough prepared to go into grad school for biological studies (neuroscience, biochemistry, biophysics, ect.). What would be the be the best course of action to tackle all four of these areas in my undergrad? How about a dual major in biophysics and computational mathematical sciences? Or would something else be better/easier to manage?</p>
<p>Standard caveat: These are not my fields. But my take on it is that you need to decide what you want to do.</p>
<p>Interdisciplinary programs realize that it’s difficult for students to adequately prepare for graduate study in two disparate fields. So, generally speaking, they take students with diverse backgrounds and help them get the preparation that they’re lacking. Biophysics programs are a good example: a biology major with a strong quantitative background could get into some biophysics programs and get the physics and computational training they needed with a tailored curriculum. Or a physics major with limited biological/chemical coursework could get into a biophysics program and pick up the biology. I get the sense that some exposure to the secondary field is valued (at least 1-3 classes) but such is the nature of interdisciplinary programs. For example, check out what Harvard has to say about applicants to their biophysics PhD program:</p>
<p>Applicants for graduate training should have sound preliminary training in a physical or quantitative science; especially chemistry, physics, computer science, or mathematics.</p>
<p>From UC Berkeley’s biophysics PhD program:</p>
<p>We encourage those with distinguished undergraduate records or research experiences in the biological, chemical, or physical sciences to apply to the Biophysics Group.</p>
<p>From Ohio State’s:</p>
<p>The program enrolls students with undergraduate degrees in the physical sciences, life sciences, engineering and mathematics, and provides individualized course work based on each student’s background and field of interest.</p>
<p>But if you were interested in a straight biochemistry program, my sense is that you might be at a disadvantage to students who majored in biochemistry or biology or chemistry. Although theoretically this is a program that blends two disciplines, in reality most colleges and universities have undergraduate biochemistry majors these days - and even if they don’t, you could always double major in biology and chemistry or major in one or minor in the other.</p>
<p>Neuroscience is kind of in the middle. Computational neuroscience is very much a thing, and neuroscience programs appreciate people with backgrounds in scientific computing and applied mathematics. However, I also don’t think you would be as competitive if your major is only in applied math and you have no biological science background. Someone interested in computational neuroscience should probably at least minor in biology (or biological psychology).</p>
<p>I think the dual major is a good idea. The other option, if the dual major is too much/would take an extra year, is to major in biophysics as you planned and just design your own concentration in scientific computation/applied mathematics. I’m assuming that you can take the classes without being a major - you could earn a minor in the field or you can just take the classes in the same sequence as the students in the major do without the actual restrictions of being a major. Nobody cares what your diploma says; they care about the actual coursework that you’ve done and the skills and knowledge you have (and that’s both grad programs and employers). You may even be able to count some of those courses towards your biophysics major.</p>
<p>First, no expert here, but to my understanding this is a very hot field right now. There is enormous interest in people with these skills, they seem to be beating the bushes for them. Or it is just so much media hype-- but not entirely. You seem to be completely discounting continuing grad studies in that and closely related areas. I wonder why? There are research topics there and universities are forming initiatives and institutes around it. CS itself has highly scientific and theoretical areas.</p>
<p>I should think that working in actuarial science or finance might not be the best preparation for PhD in theoretical physics, or biochem eh? You don’t see that? Not saying it is impossible but that sounds more like an unplanned detour rather than something you’d want to do on purpose. I would say if you want to research one thing, then it doesn’t make a whole lot of sense to prepare by studying something different, does it?</p>
<p>However, there are companies that are looking for application of scientific methods to working with data, all kinds of data. There are research positions in companies like IBM and Microsoft. I’ve heard of many startups in a wide array of fields like global security, medical, corporate intelligence, fraud detection, social media, consumer insight of course, and finance using such methods. This is beyond analyst and enters the realm of investigator.</p>
<p>You might look at what people in academia are doing in that area, and of course consult your university advisors. Read about Kirk Borne professor of astrophysics and computational science at George Mason University, Sean Murphy, data scientist at Johns Hopkins University Applied Physics Laboratory. The new Columbia Institute of Data Science and Engineering in partnership with the City of NY will focus on five key sectors: smart cities, new media, health analytics, financial analytics, and cybersecurity. This is just a taste.</p>
<p>I wonder where you are attending? A question occurs to me if a more generalist degree like you mention is best for undergraduate study. Maybe no one knows as such programs are pretty new, I think. Will there be research available for you to get involved with at your college? One thing you will want if you are going to be trying for a PhD program, not matter what area, is to have undergraduate research experiences. </p>
<p>Finally do not neglect to investigate the state of the academic job market. Don’t have on rose colored glasses.</p>
<p>I am also interested in computational math. I have a BA in math with a minor in physics and a BS in computer engineering. I took courses like scientific computing, numerical algorithms, and parallel programming. Now, after having working for what should be about a year by the time I start grad school next fall, I will be getting my masters in computational science. </p>
<p>I am a bit different than you though, I don’t really have so much of a plan as to what I would like to do after, and I’m not even sure what specific application area I would like to focus on. I’m considering something in physics, maybe computational fluid dynamics, or sensor networks. It seems to me though like this doesn’t matter much because computational science grad programs give you some freedom to explore application areas. I guess they assume that if you have a strong quantitative background you should be able to pick up an area to apply it to. </p>
<p>I would think the same would be true for the opposite situation. If you have a strong quantitative background as an undergrad by studying something like computational science then you should be able to get into grad school for a more specific field. You will want a bit of exposure by getting a minor in biology or physics or whatever.</p>
<p>Also, I wouldn’t worry so much about what exactly you major in. Pick one or two things, and then if you can’t get a minor in whatever is left over, just take a bunch of classes in when you can and make sure you communicate your plan well when you apply to grad school so that it doesn’t look like you had a difficult time picking something. </p>
<p>You might also want to try to do research or a project with a professor interested in like biophysics or something and example to them that you are interested in applications of computational math and physics to biology. </p>