Gas is under $3/gallon! My heart sings.
Ahem…
Recently I went to an event aimed at very young female undergrads who planned to major in sciences or engineering. The girls were great, fun, and smart. The overwhelming majority of them had plans to go into biomedical fields.
There were several female professors there and each of us shared what we did for research; I was the only one whose work did not involve working with biological systems.
If the student cohort is interested in biomedical fields, it makes sense to bring faculty from those fields. But a few young women approached me afterwards and I learned they had interest in fields with a heavy emphasis on math, physics, or computing; they shared that they were starved for meeting people in their fields of interest.
I am in a field where it is quite common for me to teach to a classroom with no women. Zero. Not all fields are like that. And the few girls who do venture into those physical science fields apparently feel somewhat alienated from other young women with an interest in science, if this event is any indication, because their interests are not mainstream even among STEM-focused female students.
I don’t want to make a separation between fields, we are all scientists. But, I would say there is a pretty significant difference in the types of interests between the people who enjoy hands-on work in a lab or field and the people who enjoy largely mathematical and computational work. For example, I never had much interest in biology; while I had interest in chemistry, it was nowhere near as strong as my interest in math and physics, especially building mathematical models and later implementing physical reality on a computer. I see websites of people doing field work or bench work and I understand intellectually that there are many great problems to explore there, they just don’t rock my boat. I don’t get awfully excited about experimental physical sciences labs, either.
I wish we could do a better job of reaching out to young women who are interested in the math-heavy or computing-heavy portions of the physical sciences. It feels like they really are the outliers all around, among both the men in their classes and among other women in science. I know there are online resources, but it feels they don’t really reach the young college women demographics. Perhaps the best thing to do is exactly this, getting out there to meet them and trying to help them one at a time; ironically, this may work precisely because they are so few and far between.
xykademiqz 
I have seen this & totally agree. I feel bad when I go to those kinds of events and am interacting with women interested in math & physics. I’ve got nothing for them, as those academic worlds are so different from mine. I really feel for them. It is easier to find women in the life sciences.
Agreed. I’m a woman in STEM but it’s a whole different world for women and much more male-dominated field like physics and math.
Interestingly, I do mostly computation and modeling but I got into that from a love of the natural world (biology & fieldwork).
Sounds like you are being a good mentor & role model. Our professional society has grants to bring diverse undergrads to our annual meeting & provides mentoring while they are there.
http://www.nescent.org/eog/undergraddiversity
The SEEDS program also has a lot of activities geared towards diverse undergrad students. No idea if something similar is available for math and her physical science students. http://esa.org/seeds/
There are a lot of women in math… but they end up in teaching positions (lecturers, adjuncts, or high school) or, if they’re lucky, industry. I think there’s a lot more support for math-oriented women in more applied areas of higher education like Operations Research or even engineering (which, admittedly, is still not perfect) or finance where they’re less likely to be treated as badly. Biosci still isn’t any nirvana for women either, of course. *sigh*
As an undergrad, I did a lot of physics demonstrations in primary and secondary schools. I noticed a rather unusual phenomenon. In my elementary schools, all of the students (male and female) were equally engaged in the science. Around late elementary/early middle school I began to see more of the girls lose interest, and more of the boys dominate the questions. I wonder why this split occurs. Puberty is associated with unusual sociology, but I wonder what it is about the middle school sociology that leads to fewer girls being interested in the physics.
@soggybomb, I think there is some research that points to peer pressure having a role in this. I don’t have that research at hand, though. Maybe check that NPR Planet Money episode on where all the women in computer science went- I think they had some links to research in the show notes.
I don’t know the answer to the “lack of visible role models” problem. For awhile, I could have been a role model for more math/computer heavy work (albeit in a biological field), but over the course of my career I somehow found myself doing more management and “interface” work, and then of course, I just chucked even that in. Right now, I feel a bit like an anti-role model for girls interested in science and computers!
My former university offers specific “girls days”, where middle or high school girls can visit a department for a day, do some experiments and/or modelling exercise, meet a bunch of PhD students and professors and have a chance to ask all the questions they have. Some of them came back to do their high school work experience thingy in the department. Not being in a biological STEM field we usually had about 20 girls visiting and from each year about 2-5 started after high school graduation.
Yo, the heaviest math is IN bio fields. Biomedical engineering is still engineering, and a lot of its subdisciplines are heavily math and programming based. I think I had the same silly bias when I was in high school. Try to model what your blood does as it flows through your blood vessels… It’s a whole lot more complex than what happens when any liquid flows through a pipe. And a million similar examples.
Yo, the heaviest math is IN bio fields.
Of course, biological systems are very complex. But, there are many other very interesting complex systems in all areas of science (e.g. modeling weather, various problems in astronomy, the stock market), it’s not like it’s either biological systems or fluid in pipes (btw, many people will tell you that simulating realistic viscous fluids is very complicated; a number of applied math people focus on fluid mechanics). Even something you’d think is trivial, like a pendulum, isn’t really: e.g. the double pendulum has very interesting dynamics.
So to each their own. We don’t have to all like biology.
And I am pretty sure the heaviest math is actually in math. 🙂
I think what “a” was trying to say is that there are some real needs for more heavy-duty mathematics in bio fields—there are incredibly hard problems (the blood system consists of a viscous fluid in a fractal network of elastic-walled pipes driven by a pulsating pump—much messier than what most people in fluid dynamics are willing to model, particularly since what needs to be modeled for the blood system usually are the failure modes: burst or clogged blood vessels, buildup of arterial plaque, leakage through the walls of the vessels, … ).
But biology does not involve a lot of such modeling, because it is not primarily a model-based discipline, but a data-based one. The recent flood of big data in biology has left many classically trained biologists floundering, because they don’t have the heavy-duty statistics education to properly interpret the data. Good bioinformatics work requires a pretty heavy dose of both Bayesian and frequentist statistics.
A lot of physicists are (justly, sometimes) proud of how much math they use and how well it works for the problems they study. To a large extent, this is because physicists mainly study extremely simple systems that can be modeled and measured very precisely (the double-pendulum mentioned is an excellent example of interesting behavior from an extremely simple system). Partial differential equations are a very powerful tool for such systems. Biological processes are almost all stochastic at the bottom, so classical PDE approaches, which work so well for physics, fail pretty quickly in application to biology problems—the interactions of individual cells or even molecules is more complex than can be handled by the bottom-up physics-based approaches.
Biological systems are much harder to control and measure than physical ones—in a gene-expression experiment, getting results from experiment to experiment that agree within a factor of 2 is considered very good repeatability. So the math that biologists need is somewhat different from the math that physicists need, and the models often have to kept very simple to avoid overfitting the noise (a common problem for physicists entering biology is to use all the data for fitting their models, resulting in serious overfitting problems).