3D Printing at Rutgers

Since I am going to be using 3D printing as part of my research, I’ve been on the lookout for places to print at Rutgers for quite some time. If you’re also interested to do some 3D printing for your research, or you just want to 3D print something for fun, then I have come across a number of options that might be useful for you. I’m sure there might be even more locations available. So, if you happen to know of any other locations that allow for open use of printers, please let me know.

  1. Douglass Library, Fordham Commons area Fablab, Douglass Campus: on the ground level of the library are two MakerBot Replicator 2’s and computers with design software. You can schedule an appointment to print your project and to get pricing estimates.
  2. Rutgers Makerspace, 35 Berrue Circle, Livingston Campus: MakerBot Replicators and other fun items, like a pool table, are available here. The Makerspace normally has regular drop in hours for printing or just hanging out. The space is run by Rick Andersen who has lots of experience in computers and electronics including web design, Arduino and soldering.
  3. Rutgers Mechanical Engineering Dept., Busch Campus: the department has a few options available for Rutgers affiliates to use, including a Stratasys Objet350 Connex and Stratasys uPrint SE. The contact person for setting up an appointment to get your projects printed and for pricing is John Petrowski (petrows@rci.rutgers.edu).
  4. FUBAR Labs, Highland Park, NJ: Fair Use Building and Research (FUBAR) Labs is a nonprofit that provides a local spot for people with common interests, usually in science and technology, to meet and collaborate. It’s an open community offering classes, workshops, study groups, and long term project collaboration. You can join as a member for 24/7 use of the space, or you can drop by for one of their events to check them out.

Communicating science: the elevator speech

In a previous post, I described my experience at a workshop (organized by the Rutgers Graduate School-New Brunswick) on communicating science.  I described the importance of preparing descriptions of your work for a spectrum of likely audiences – having at least some idea of what aspects of your work to emphasize to different audiences and what language or ideas to use are critical.  However, in addition to these more customized versions, having a more generic but highly-polished description of your research that you can recite from memory at any time is probably worth having.  This is often known as the “elevator speech,” since it’s supposed to be something simple and short enough that you can say it during the time you’d spend with a stranger in an elevator.

I’ve had a murky version of this for a while, but it was largely a vague set of examples and analogies I liked to use when describing my research to a friend or family member rather than a well-crafted summary.  But the workshop motivated me to finally develop a better version, so here is my latest attempt:

Every cell in your body contains thousands of different kinds of molecules, stuffed into a very small space and interacting with each other in complex ways.  How does this mess of molecules ultimately do all things that cells do, such as making new cells, extracting energy from food, and transporting nutrients?  And how did the precise interactions of all these molecules develop over millions of years of evolution?  This knowledge is important both for treating human diseases in which these cellular functions go wrong (e.g., runaway cancer cell growth), as well as engineering microorganisms to perform useful jobs, such as synthesizing biofuels with bacteria or making better beer with yeast.  My research uses mathematical models and computational techniques to understand how natural selection changes these molecules and their interactions over time.  We want to use this both to understand how organisms naturally evolved in the past and to predict how they might evolve in the future.

Research: Estrogens and the body

Let’s first start off by stating that I am a student in the Endocrinology and Animal Biosciences Program at Rutgers. The program I am in is very diverse and we study multiple aspects of endocrinology–or the study of hormones. There are professors here that study anything from cancer biology to the reproductive system to obesity. The lab that I am in focuses on estrogens. Specifically, I am interested in how estrogens control the body–what changes it can make to energy balance (consuming more or less than our body uses), thermoregulation (temperature control), and reproduction.

In addition, our lab is interested in how estrogen acts. Estrogen is considered to be a steroid hormone, which means that it is able to go into cells and bind to a receptor to become activated. However, there is now a new way that it can function–it does not have to go into the cell, but responds to receptors on the outside cell membrane. Furthermore, I am interested in something called KNDy neurons, which are neurons that produce three different genes: Kisspeptin, Neurokinin B, and Dynorphin. These neurons are located in the brain and are important for many body functions including those that respond to estrogen.

Research in Mathematics

Working in mathematics, I’ve found myself often asked the question “What do you do?” Sometimes the expected response is my “elevator pitch” (the short blurb about my area of expertise). But sometimes the question is more basic: “What is it you do, though? Do you just sit all day and think?”

Thinking [cc]

Now, to a large extent, many people in research spend all day thinking. However, mathematics is not simply the art of staring at a problem until the solution materializes in one’s head. (It’s worth a try, but often solutions do not come from epiphany alone.) I would like to discuss a few of the ways in which research is conducted in mathematics, with emphasis on the parallels and similarities that may exist between mathematics and other fields, perhaps to somewhat debunk that notion there may not be any such similarities.

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Mathematics research revolves around proving new theorems — mathematical statements that can be deduced from the fundamental axioms of mathematics and from preexisting theorems. Generally, though, the procedure is not to make a big pile of the existing statements and to try to string them together randomly until one forms a coherent deduction that results in something meaningful. That would be pretty rough sailing! Mathematics relies on conjectures, put forth as believed to be true and hopefully proven by someone at some later time. While there are conjectures (e.g. Goldbach’s conjecture) which remain unsolved for long periods of time (sometimes resulting in notoriety), most theorems start out as rough ideas or propositions that are developed with increasing structure and refinement until they are proven. In addition to proving new theorems, other steps forward in research include constructing examples of mathematical structures and verifying theorems by re-proving them in new ways. Computational work is also done to improve theorems in the case that a theorem is quantitative (or sometimes, to prove that a quantitative result is best-possible).

El. J. Comb. logo

Know the literature: As in most fields, the mathematical literature is vast, and perhaps especially in mathematics, it is easily accessible. Increasingly, mathematics journals are available online — not just through the library system, but free for instant download on the Web. Having less concern for the preservation of intellectual property, many editorial boards have shifted to such open/free publication. (Indeed, I myself have a publication in The Electronic Journal of Combinatorics, which is precisely such a journal.) There is also the arXiv (the X is pronounced sort of like χ, the Greek chi), which hosts preprints of papers and other works of mathematics (and many other fields).

Being familiar with the body of literature, both seminal papers and other older works as well as the current cutting-edge work (as it appears first, usually on the arXiv), is an important part of conducting research in mathematics. Jacob Fox came to Rutgers in 2009, when he was at Princeton, to speak at a seminar. He noted during his talk the importance of being familiar with the literature, mentioning in particular how his knowledge of a certain publication helped him and his coauthors solve a problem.

Mathematics [cc]

Crafting and Proving Good Conjectures: One of the more important questions is where to start — if we’re going to prove a statement is true, what is that statement? Generating good conjectures is not a matter of guesswork or divine inspiration, at least not entirely (although the former may have helped from time to time, and the latter is open to some debate at least). Increasingly, experimentation is a common way to generate conjectures. It is also often useful to test conjectures in small, typical, or special cases (where “small,” “typical,” and “special” depend on the problem at hand). Usually a conjecture applies to too many cases to test them all (sometimes, infinitely many cases), so this methodology is often used to verify that the conjecture is sometimes true, but not to verify the conjecture exhaustively. (Conversely, experimentation may lead to a disproof of a conjecture by identifying, constructing, or otherwise elucidating a counterexample.) Experimentation may also help unearth components of the proof of the conjecture at hand.

It is also crucial to have a firm understanding of the big picture in the field where these questions are being asked. There is a substantial amount of context and content that guides someone to the right kinds of conjectures and the proofs of those conjectures. Mathematics is a field in which the objects of study are highly structured, and knowing these structures helps eliminate some of the technical clutter that can obfuscate the underlying truths that one wishes to prove and the bits and pieces that go into proving them. Many proof techniques can be adapted to different situations, so in some sense theorems may be proved by matching a generalized proof to a statement you would like to prove specifically.

Proof [cc]

Building Theories and Solving Problems: Tim Gowers is famously credited for roughly dividing mathematicians into the two categories problem-solvers and theory-builders (or rather, he is credited for noting this division in his oft-quoted The Two Cultures of Mathematics). I won’t discuss this dichotomy, but these two activities characterize much of the research done in mathematics. Proving single, unrelated theorems one-by-one is not usually how research goes. Rather, the enterprise involves longer strands of investigation — a dozen theorems sometimes collapses into a single stronger and better statement after enough exploration and refinement. Meanwhile, single ideas branch into many avenues of investigation. But generally, the aim is not to knock down one theorem, then turn around π radians and start over, but to work on larger-scale investigations. I could make a metaphor about bowling pins or dominoes, but I think the idea is clear. An important aspect here is also collaboration, which is a major element of research for many mathematicians. Working on papers is one part of collaboration, but other important activities include seminars & conferences (as participants and as organizers), expository writing, editorial work, and many other collaborative activities.

Structure [cc]

So the venture is to find good lines of research and establish some clear, path along that line. There are the two approaches. The first is to identify important problems and build up theory to solve them. One famous example is Fermat’s last theorem, which conjectured centuries ago and recently proved by Andrew Wiles. During those centuries, large swaths of mathematics were developed in large part as attempts to prove this conjecture (including Schur’s theorem, one of my personal favorites). This “problem solver” work weaves what might be the leading strands of the theory, loose and rough but pushing outwards farther than neighboring strands. Such work often moves mathematics in innovative or interdisciplinary directions, building bridges between fields of mathematics, and may also connect with work in applied mathematics. The “theory builders” weave strength and cohesion into the fabric (to extend the metaphor). To this end, they focus their research on developing and enriching the theory. They may work to classify all types of a particular structure, for example. Such work includes that of several Rutgers faculty in classifying the finite simple groups. This theory-building reinforces others’ work as they develop and solve conjectures, as it makes the underlying theory more robust.

Images used in this entry are used under fair-use and/or under licensing guidelines set forth by the copyright holder that allow use in this blog, as presented for educational or critical commentary. Images are copyright their respective holders and credit or source is indicated in each caption or in the text of this entry, as applicable. Thanks to Yusra Naqvi for her helpful comments and suggestions.

Identity, Goals, and Diversity in Interdisciplinary Research

While I was an undergraduate physics major, my interests and research experiences were quite clearly of the pure physics variety: particle physics, cosmology, astrophysics.  There was never any question about my scientific identity or goals — I was unambiguously a “physicist,” and with that label implicitly came values about what I was supposed to study and how.

When I began graduate school, however, I found a new interest: biophysics, an interdisciplinary science if there ever was one.  While Rutgers has many physics Ph.D. students and faculty studying problems in biophysics and quantitative biology, I couldn’t help but suffer a bit of an identity crisis, albeit one more professional than adolescent in nature (not so much “Who am I?” but rather “What kind of job will I be able to get?”).  This seemed exacerbated by my specific research, which focuses on evolution; while physical analogies abound within the mathematical models, the phenomenon itself is plainly biological.  So when describing my work to others, I had to wonder: am I still a physicist?  Or am I a biologist?  Am I some type of hybrid, i.e., a biophysicist, and if so, what does that really mean?

Over time, though, I’ve come to believe what defines our identities as scientists is not so much what we study but how we study it.  More precisely, it is not the questions we ask but the kinds of answers we seek that are important in defining this identity.  Many different types of scientists (biologists, chemists, physicists, etc.) in a field like biophysics are basically studying the same problems — gene regulation, biochemical kinetics, protein folding, etc. — but their actual work may look completely different from each other’s on paper.  A good example is given by protein folding, the famous problem of understanding how a chain of amino acid molecules making up a protein folds relatively quickly into a unique 3D conformation (Ref. 1).  To a structural biologist or a bioinformatician, so-called homology-based methods provide an adequate solution.  These methods predict unknown structures of proteins using large databases of known structures and statistical algorithms.  To a physicist, however, this is not really a solution at all — it is a practical tool to make predictions, but it offers no insight into the fundamental physical principles underlying how the folding process occurs.

This issue has real consequences for a discipline, beyond just a little angst for students.  Despite all the good intentions of funding agencies, journals, and institutions toward cultivating interdisciplinary research, they run into problems when geneticists are evaluating physicists’ proposals by genetics standards or when mathematicians are evaluating biologists by mathematics standards.  As demonstrated by the example of protein folding, scientists can have genuine disagreements about whether a problem is even solved.  An interdisciplinary field must be aware of these different values and should openly discuss how to make different scientists’ goals and styles complementary for the sake of scientific progress.  Indeed, interdisciplinary research has tremendous power to meet the daunting challenges of the 21st century, but only when effective communication and collaboration exist to take advantage of it.

[1]  Dill KA, et al.  (2007)  “The protein folding problem: when will it be solved?”  Curr. Opin. Struct. Biol. 17:342-346.

Media Mouthfeel

Who am I? I thought I dispensed with such philosophical wormholes after the teenage angst years. My first year as a doctoral student at Rutgers has proved me wrong. Although the angst has mellowed now in my late 30s, I still dread the ubiquitous wine and cheese filled inquisitions about my research interests. I will confess to a degree of envy when my colleagues in other disciplines succinctly explain what they study. Math, in particular, tends to shut people up. The study of media on the other hand sparks loads of questions. Everyone has opinions about it. For those who embrace the postmodern world (I myself am suspicious of the adjective), everything is a text filled with gaps available to be read, even the cheese cube in your hand. I actually like this everyday quality of media, yet it encourages my “shiny object syndrome”—my habit of knowing a little about a lot, a kind of epistemology of distraction. This mindset seems antithetical to the scholar, the learned one able to pontificate on the political economy of toothpicks.

So I must focus at least in time for my qualifying exam circa the start of my third year. I have roughly 6 courses left of my traditional student life. Scarcity breeds abject terror. As a 16th grader, the paranoia of making them count looms large. I also have the awesome opportunity to take two courses outside of Rutgers through the research consortium. I need a plan, yesterday.

Of course, I have talked to my advisors. They are ever so patient with me. I tend to ask enormous questions such as “What makes media more or less democratic?” I get cranky thinking about techno-evangelists, those who have Internet technology saving the planet, as if cyberspace were free of race, class and gender. Would we even want such a place if it were possible? This is why I like science fiction novels. Maybe I should have chosen English. Disciplines are mere fabrications, artifacts of university politics. Do you see why my professors are so patient with me?

If you pass me the pinot noir, I will tell you that I want my research in media studies to convey hope—hope for our global liberty expressed locally. I told a colleague that this was a corny idea. She disagreed, suggesting that theory leads to anger (at the disconnects?), which eventually gives way to a longing for hope. This has a truthy ring to it, one that harmonizes with my conviction that culture is what we make it; culture is essential to freedom. Media are cultural channels, spaces carved out with rich striations and sediments for study–cultural terroirs. I tend to focus on who made the channel, when, where, why and how. Media production is my bailiwick, and I am developing a fondness for ethnography—minus the transcription part. My kind of work is open to interpretation. I wouldn’t have it any other way.