A few weeks ago, with his post "What do theoretical physicsts actually do?", Thoreau inspired me to write a post about what my work entails. But, theory and computation are certainly not the province of physics, and an idea for this carnival was born. I am very excited to share with you a wonderful set of entries from scientists at different stages in their career, from academia and industry, and spanning a number of different fields.
Thoreau at Unqualified Offerings describes his career trajectory, from becoming a theorist in a somewhat unconventional way to being a physics professor. He discusses the multiple facets of his work and shares the joys and excitement that come from cracking a really important longstanding problem.
HFM left a comment, which I reposted as a guest post for easier reading. HFM is a graduate student who considers themselves a "semi-theorist" in that they interface closely with experimentalists but can speak the pure theorists' language well, too. HFM considers making sense of complex data their strength, and writes about the likes (variety) and dislikes (not belonging anywhere, hard to find a postdoc) of their daily work.
Bee who blogs at Backreaction is a theoretical physicist who works on the phenomenology (a part of theory that makes connection to experiment) of quantum gravity. She writes about her field in accessible terms and discusses what it takes to make a successful model, one that addresses certain experimental features while maintaining mathematical consistency. She also shares that, when working on a paper, she will frequently communicate with others in the same field, travel to conferences or organize a workshop.
Miss MSE over at Periodic Boundary Conditions tells us about her work employing the molecular dynamics technique. She studies interfaces between polymer and nonpolymer systems, systems where "there's no good way to study them experimentally without fundamentally changing the structure, and therefore the properties, of the interface." She loves how broadly applicable the technique is, talks about here code-development experiences, and emphasizes that everything she does is informed by experiments.
Anonymous Mad Scientist in a Strange Land writes about his work on first-principles quantum-mechanical calculations used to look at interfaces of materials in systems previously computationally inaccessible due to their complexity. He also shares how he's always wanted to be a theorist, and how his career progressed so far, leading him to his current position as a postdoc in France.
Dr. Sneetch of The Sneetch Blog is a mathematician who enjoys crossing the boundary between pure and applied math seamlessly, following her work. She feels that, with mathematicians, you cannot separate the person from the work, that "Mathematics sustains and nourishes us as much as we sustain and nourish it. There is no distinction between the person and the mathematician and perspectives matters." Dr. Sneetch also talks about the singular focus necessary to do research in mathematics.
Pika from Academic International writes about her work in a pseudonymous field of Beachinformatics. She says that she's always been interested in applied mathematics and discusses several aspects of her work, such as data mining and visualization, that are inherent in dealing with complex data systems.
In her daily work, Rebecca from Adventures in Applied Math helps scientists of different specialties with their computational woes. Rebecca wrote an interesting post that emphasizes how computational techniques cross the boundaries between fields very well: scientists in many different specialties all need to solve partial differential equations, eigenvalue problems, or simply need their codes to run faster or parallelize better.
ScienceGirl from Curiosity Killed the Cat writes how being a computer scientist focused on scientific computing enables her to satisfy her great curiosity in all fields of science, as she says "Performing computation in a smart way to do unprecedented science. I don't really care what science - physics, climate, medical research, all are important and all fascinating. So I know I've chosen my trade well - as a computer scientist, I get to have my fingers in any of them!"
Cloud from Wandering Scientist brings the perspective of "a scientist and a techie" with a career in the biotech industry. For example, she likes the variety of her work, the intellectual challenges, and when wearing a project manager's hat, "figuring out how to bring all the pieces together to get a project to complete successfully- it is like a big logic problem." She is not too keen on some of the corporate politics and the industry's volatility.
Nauromath contributed a guest post. His field is theoretical neuroscience, and in his work he tries to understand how the brain works by studying in detail the environment we live in. He also discusses how his work addresses some of the limitations of experiments and reveals a great passion for his work, because, as he says "brains are very cool," and there are many open problems where the theoretical approaches he develops are the best bet for a solution.
A related guest post was contributed by Dr. Cow, who is interested in "human cognition, specifically developmental cognitive neuroscience." Dr. Cow discusses why computation is useful in this line of research, and cites reasons such as inability to run experiments directly "either because of a gap in methodology or due to ethical considerations" and the ability of computational methods to "define the complex and dynamic relationship between the neural structures and behavioral outcomes."
Gasstationwithoutpumps contributed a post on his work as a bioinformatician. In contrast to, for instance, computational physics where typically one uses a computer to solve a mathematical model, his work is not model-driven but data-driven. He says "It is rare in bioinformatics that we get to build models that explain how things work. Instead we rely on measuring the predictive accuracy of “black-box” predictors, where we can control the inputs and look at the outputs, but the workings inside are not accessible." He also talks about the research path he took to his current field.
Thanks to all the wonderful bloggers who took the time to contribute to this carnival and share what it is that makes us, theorists and computational scientist, enjoy our work so much. There is a great deal of passion for their craft that radiates from each one of these posts and I trust you will find it contagious. Enjoy!
Monday, September 26, 2011
Sunday, September 25, 2011
Guestpost by Dr. Cow
Computational Brain and Cognitive Sciences
by Dr. Cow
To put what I do in a sentence: I use simulations to attempt to figure out some of the complex and dynamic interactions between the biology (your brain) and behavior (cognition). Brain and Cognition covers a vast research area full of many different types of questions, different methodologies, many interesting results, and open questions. The levels of analysis range from the molecular to the behavior of large groups. I myself am interested in human cognition, specifically developmental cognitive neuroscience.
So, why use computational methods?
-There is a lot of potentially interesting experiments that we can't run directly. Either because of a gap in methodology or due to ethical considerations. Computational methods can use known constraints from related data and fill in the gaps. We can even make novel predictions about behavior or the biological systems.
-If you're going to pain a picture of how the biology and behavior interact with each other then you're going to need a way to draw that picture. Computational methods have the precision necessary to define the complex and dynamic relationship between the neural structures and behavioral outcomes.
My research involves reading papers ranging from systems neuroscience to cognitive psychology. My lab produces both computational data and matching behavioral data from human participants. I also sometimes collaborate with brain imaging researchers.
Overall I think it's a very fun (I like math) exciting and useful area of research.
by Dr. Cow
To put what I do in a sentence: I use simulations to attempt to figure out some of the complex and dynamic interactions between the biology (your brain) and behavior (cognition). Brain and Cognition covers a vast research area full of many different types of questions, different methodologies, many interesting results, and open questions. The levels of analysis range from the molecular to the behavior of large groups. I myself am interested in human cognition, specifically developmental cognitive neuroscience.
So, why use computational methods?
-There is a lot of potentially interesting experiments that we can't run directly. Either because of a gap in methodology or due to ethical considerations. Computational methods can use known constraints from related data and fill in the gaps. We can even make novel predictions about behavior or the biological systems.
-If you're going to pain a picture of how the biology and behavior interact with each other then you're going to need a way to draw that picture. Computational methods have the precision necessary to define the complex and dynamic relationship between the neural structures and behavioral outcomes.
My research involves reading papers ranging from systems neuroscience to cognitive psychology. My lab produces both computational data and matching behavioral data from human participants. I also sometimes collaborate with brain imaging researchers.
Overall I think it's a very fun (I like math) exciting and useful area of research.
Guest post by neuromath
While our computers are getting better and better all the time, one of the persistent mantras in science and engineering is that our machines still fall far short of the performance of human brains in their ability to understand the world around them. This is, in fact, the basis of things like CAPTCHAs (http://en.wikipedia.org/wiki/Captcha) which rely on varying abilities to decipher deformed letters to distinguish humans from machines. What's even more maddening is the that brains perform these types of computations with relentless efficiency (less power than required to light a lightbulb).
Unfortunately, our detailed understanding of how the brain converts raw sensory data into complex perceptions and judgements is still very primitive. Most people think of neuroscience as an experimental discipline. Indeed most of our understanding of how brains work comes from the (sometimes heroic) efforts of experimentalists to lift the hood and get a glimpse of what's going on. But, due to the extreme technical challenges of conducting these experiments, there are two persistent problems: 1) we only ever get to observe a miniscule fraction of the system operating at a time, and 2) most of our data comes from the system when it's operating in a very non-natural state.
Experimental work has given us a lot of information about how brains (and the basic building blocks of brains) work. But, given the two issues mentioned above, it is nearly impossible to think that we will be able to understand how this large, complex and nonlinear system actually works from simply stitching together these experimental snapshots. It has been said that neuroscience is a field that is data-rich and theory-poor. In other words, there is a deluge of data coming from experimental labs (and sometimes very good models accompany this data), but very little overarching understanding of how all of these pieces fit together to give brains their amazing computational abilities. This situation is basically exactly the opposite of modern physics, which has theory coming out its collective ears but very little data to test its ideas.
The area I work in is theoretical neuroscience, which attempts to fill in some of these gaps. The approach of my lab is essentially to try and understand how the brain works by studying in detail the environment we live in. To be specific, consider our visual system. If the brain is efficient at taking raw visual data and using it to make judgements about how to understand the world, then that brain must be highly adapted to the specific visual structures that exist in the world. In other words, that brain should be much more efficient at handling a natural image (i.e., anything you would imagine taking a photo of) than any old random pattern of light (e.g., static on an old TV). It turns out that there is a lot of structure in things like natural images, and the language of statistics is a very natural way to describe it. It also turns out that fields like engineering and applied mathematics have developed many approaches to optimally exploit this type of structure.
The main approach of our lab is something like this: 1) build a statistical model of the signals of interest, 2) look to engineering/math to determine the optimal ways to deal with that structure, 3) given what we know from experiments and other theories, look for a way to port these mathematical ideas to a hypothesized neural processing strategy, and 4) look to see if this hypothesized system can explain any significant aspect of experimental data. While this does have an element of "guess and check" to it, the hypotheses are based on a significant prior knowledge and are not out of the clear blue sky.
The main hope of this type of approach is that given a small amount of data (relative to the system complexity), it is much easier to check a specific theory than to divine the inner workings of an unknown black box. This is not a replacement for experiment, but a complementary direction that can help to guide future experiments and can tie together our previous findings. While this is a fairly young field, there have been several surprising successes where some aspect (developmental features, response properties, etc.) have been shown to emerge from the statistics of the world combined with simple hypotheses about the system. We've also learned some things from this approach that can be taken back to the engineering world to improve the systems we build.
I chose this area, and continue to be fascinated by it, because I love the combination of pure insight that comes from the mathematical theory and the curiosity about a complex and fascinating system that is described in the experimental literature. The bottom line is: 1) brains are very cool, 2) I think this type of theoretical work is honestly some of our best hope for really understanding what's going on in them, and 3) right now this type of approach is a relatively untapped area. It's hard for me to imagine another area where we know so little and the tools of mathematics have been so rarely applied. Consequently, it's hard for me to imagine an area that has such a high potential payoff, and I'm excited by the opportunity to try and make a really fundamental contribution to our understanding.
Unfortunately, our detailed understanding of how the brain converts raw sensory data into complex perceptions and judgements is still very primitive. Most people think of neuroscience as an experimental discipline. Indeed most of our understanding of how brains work comes from the (sometimes heroic) efforts of experimentalists to lift the hood and get a glimpse of what's going on. But, due to the extreme technical challenges of conducting these experiments, there are two persistent problems: 1) we only ever get to observe a miniscule fraction of the system operating at a time, and 2) most of our data comes from the system when it's operating in a very non-natural state.
Experimental work has given us a lot of information about how brains (and the basic building blocks of brains) work. But, given the two issues mentioned above, it is nearly impossible to think that we will be able to understand how this large, complex and nonlinear system actually works from simply stitching together these experimental snapshots. It has been said that neuroscience is a field that is data-rich and theory-poor. In other words, there is a deluge of data coming from experimental labs (and sometimes very good models accompany this data), but very little overarching understanding of how all of these pieces fit together to give brains their amazing computational abilities. This situation is basically exactly the opposite of modern physics, which has theory coming out its collective ears but very little data to test its ideas.
The area I work in is theoretical neuroscience, which attempts to fill in some of these gaps. The approach of my lab is essentially to try and understand how the brain works by studying in detail the environment we live in. To be specific, consider our visual system. If the brain is efficient at taking raw visual data and using it to make judgements about how to understand the world, then that brain must be highly adapted to the specific visual structures that exist in the world. In other words, that brain should be much more efficient at handling a natural image (i.e., anything you would imagine taking a photo of) than any old random pattern of light (e.g., static on an old TV). It turns out that there is a lot of structure in things like natural images, and the language of statistics is a very natural way to describe it. It also turns out that fields like engineering and applied mathematics have developed many approaches to optimally exploit this type of structure.
The main approach of our lab is something like this: 1) build a statistical model of the signals of interest, 2) look to engineering/math to determine the optimal ways to deal with that structure, 3) given what we know from experiments and other theories, look for a way to port these mathematical ideas to a hypothesized neural processing strategy, and 4) look to see if this hypothesized system can explain any significant aspect of experimental data. While this does have an element of "guess and check" to it, the hypotheses are based on a significant prior knowledge and are not out of the clear blue sky.
The main hope of this type of approach is that given a small amount of data (relative to the system complexity), it is much easier to check a specific theory than to divine the inner workings of an unknown black box. This is not a replacement for experiment, but a complementary direction that can help to guide future experiments and can tie together our previous findings. While this is a fairly young field, there have been several surprising successes where some aspect (developmental features, response properties, etc.) have been shown to emerge from the statistics of the world combined with simple hypotheses about the system. We've also learned some things from this approach that can be taken back to the engineering world to improve the systems we build.
I chose this area, and continue to be fascinated by it, because I love the combination of pure insight that comes from the mathematical theory and the curiosity about a complex and fascinating system that is described in the experimental literature. The bottom line is: 1) brains are very cool, 2) I think this type of theoretical work is honestly some of our best hope for really understanding what's going on in them, and 3) right now this type of approach is a relatively untapped area. It's hard for me to imagine another area where we know so little and the tools of mathematics have been so rarely applied. Consequently, it's hard for me to imagine an area that has such a high potential payoff, and I'm excited by the opportunity to try and make a really fundamental contribution to our understanding.
Guesf post by HFM
I'm a new grad student in "Quantitative Stuff", where Stuff is an established science field without much of a quantitative tradition. So I'm at least a semi-theorist.
How I chose it: When I was choosing my undergrad, I knew I wanted to work on Stuff. I also knew that Stuff was reaching the point where engineering approaches were feasible, and I wanted to do that - I'm a hacker/tinkerer at heart. So I got a BS in engineering.
What I do: I've been hiring myself out to old-school Stuff labs, with the promise of solving problems with my bag of engineer-tricks. This can be almost anything - modeling, data analysis, data extraction, automation, assay development, etc. Also, lots of "can you fix it"...anything from "I superglued this expensive object to my desk" to "the department's server died [no backups, no password, obscure OS, grad students whose entire theses were on there crying in the background]". (Yes, I fixed both - acetone for the first, Google and calling in favors for the second.)
What I want to do: Something I've been thinking about! I do want an independent career of some kind - working on other people's problems is okay, but I've got my own opinions on what is interesting and feasible, and I'd like the chance to set an agenda and defend my own work.
One challenge is figuring out a niche. I realize it's in my best interest to have a non-trivial solution for "world expert in X", but research ADD and an interest in methods makes this harder. I've seen Stuff labs that do tools, but they tend to get stuck on milking the old tool for all it's worth - especially in this funding climate, it's hard to give up the safe paper and go build the next big thing.
For me, I think I'll choose a corner of Stuff to work in, and choose the subset of those stories that could use a technical nudge. I'm okay with using non-cutting-edge engineering to do this - given the early state of Quantitative Stuff, there are real contributions to be made this way.
I'm not a gifted mathematician; I can muddle through the stuff you need for engineering, but there are heaps of people you'd want before me. And I can't theorem-proof my way out of a wet paper bag, though I'm also not trained to. What I am awesome at, however, is finding structure in ugly data. Then I can build the tools and analytics to bring that structure out, or if I can't, I can at least explain the problem to a hardcore theorist in language they understand.
So for this very interdisciplinary semi-theorist, here are my likes and dislikes:
Likes: Variety. A brand new field, one I'm sincerely excited about, with enough useful low-hanging fruit to keep a hundred of me busy. Doing stuff no one else is, so you're not fighting it out with a bunch of labs chasing the same result.
(And if I'm being honest...cookies and beer from grateful Stuff students when you can "fix it". Also, the option to bail into Quantitative Better-Paying Things. I'd rather not, but if I fail, the Stuff option is an eternal postdoc or worse.)
Dislikes: You don't quite belong anywhere. Finding jobs is hard, whether industry or academia - nobody's quite sure what to make of you. (On the grad school trail, I got interviews at two departments in the same school; both rejected me, on the grounds that I really belonged in the other one. Hmph.) Nobody's going to get all of what you do; intro sections of grants/papers/etc get much more interesting to write. You'd better hunt down multiple mentors if you're in training. I worked in a pure Stuff department; having nobody to talk to on the tech end was hard.
How I chose it: When I was choosing my undergrad, I knew I wanted to work on Stuff. I also knew that Stuff was reaching the point where engineering approaches were feasible, and I wanted to do that - I'm a hacker/tinkerer at heart. So I got a BS in engineering.
What I do: I've been hiring myself out to old-school Stuff labs, with the promise of solving problems with my bag of engineer-tricks. This can be almost anything - modeling, data analysis, data extraction, automation, assay development, etc. Also, lots of "can you fix it"...anything from "I superglued this expensive object to my desk" to "the department's server died [no backups, no password, obscure OS, grad students whose entire theses were on there crying in the background]". (Yes, I fixed both - acetone for the first, Google and calling in favors for the second.)
What I want to do: Something I've been thinking about! I do want an independent career of some kind - working on other people's problems is okay, but I've got my own opinions on what is interesting and feasible, and I'd like the chance to set an agenda and defend my own work.
One challenge is figuring out a niche. I realize it's in my best interest to have a non-trivial solution for "world expert in X", but research ADD and an interest in methods makes this harder. I've seen Stuff labs that do tools, but they tend to get stuck on milking the old tool for all it's worth - especially in this funding climate, it's hard to give up the safe paper and go build the next big thing.
For me, I think I'll choose a corner of Stuff to work in, and choose the subset of those stories that could use a technical nudge. I'm okay with using non-cutting-edge engineering to do this - given the early state of Quantitative Stuff, there are real contributions to be made this way.
I'm not a gifted mathematician; I can muddle through the stuff you need for engineering, but there are heaps of people you'd want before me. And I can't theorem-proof my way out of a wet paper bag, though I'm also not trained to. What I am awesome at, however, is finding structure in ugly data. Then I can build the tools and analytics to bring that structure out, or if I can't, I can at least explain the problem to a hardcore theorist in language they understand.
So for this very interdisciplinary semi-theorist, here are my likes and dislikes:
Likes: Variety. A brand new field, one I'm sincerely excited about, with enough useful low-hanging fruit to keep a hundred of me busy. Doing stuff no one else is, so you're not fighting it out with a bunch of labs chasing the same result.
(And if I'm being honest...cookies and beer from grateful Stuff students when you can "fix it". Also, the option to bail into Quantitative Better-Paying Things. I'd rather not, but if I fail, the Stuff option is an eternal postdoc or worse.)
Dislikes: You don't quite belong anywhere. Finding jobs is hard, whether industry or academia - nobody's quite sure what to make of you. (On the grad school trail, I got interviews at two departments in the same school; both rejected me, on the grounds that I really belonged in the other one. Hmph.) Nobody's going to get all of what you do; intro sections of grants/papers/etc get much more interesting to write. You'd better hunt down multiple mentors if you're in training. I worked in a pure Stuff department; having nobody to talk to on the tech end was hard.
Friday, September 23, 2011
A Gentle Reminder: Entries for Carnival on Theoretical/Computational Sciences Due Sunday, Sept 25
If you do theoretical/computational work in the sciences, please consider writing a post that tells us a little bit about what your work entails, what you enjoy/dislike, what types of problems you tackle, what made you chose your specialization, etc. You don't have to be too specific, but you certainly can if you want. Send a link (a comment here or drop me an email) so I can aggregate the posts. Even if you don't blog, consider either writing a lengthier comment here and I will link to that as your post (please don't post as Anonymous, give yourself some nickname for the occasion) or, alternatively, email me the text and I will put it up as a guest post.
It's perfectly fine to recycle some of your old favorite posts.
Why this carnival? Yes, we use math and computers. A lot. But I would like everyone else to learn a little bit about what makes us tick, what makes us successful, what types of problems we encounter, and why what we do is so darn awesome!
Deadline: Sunday, September 25.
Please leave a comment here or drop me a line if you are participating (geekmommyprof at gmail.com) so I can be on the lookout.
Ideally, I would like to put the carnival summary post up on Monday, September 26.
Thanks in advance for participating!
It's perfectly fine to recycle some of your old favorite posts.
Why this carnival? Yes, we use math and computers. A lot. But I would like everyone else to learn a little bit about what makes us tick, what makes us successful, what types of problems we encounter, and why what we do is so darn awesome!
Deadline: Sunday, September 25.
Please leave a comment here or drop me a line if you are participating (geekmommyprof at gmail.com) so I can be on the lookout.
Ideally, I would like to put the carnival summary post up on Monday, September 26.
Thanks in advance for participating!
Friday, September 16, 2011
Random Irritations -- Episode I
I really, really hate it when people suck at their jobs and I have to depend on them.
I am organizing a conference in my specialty in the spring. It's not a huge one, 250 people or so, but enough to require some serious planning. So I started well ahead, early last spring, and among other things contacted the campus business center and was connected with a person who was supposed to help everything run smoothly. However, I should have guessed it would be a rough ride when she stood me up at our first meeting, where I was supposed to tour the beautiful, newly-built facility with lots of meeting rooms. No one is more pissed than an uncomfortable hugely pregnant woman who is wet, cold, and has been stood up. We rescheduled and I eventually toured the facilities.
Then I gave her a detailed schedule (this conference has a well-defined layout and duration) sometime last spring and discussed options for online registration, different catering options, how to most easily have prepaid lunches for participants etc., where to have the banquet, poster session etc. She said she'd get back to me shortly with a break-even budget, so I can go and start looking for support from different agencies and professional societies. That was last spring. Over the course of the summer, I contacted the woman via email several times to ask when I could expect the budget. She completely ignored me. It's been months since I last heard from her. I know she's busy with other conferences in the meantime, but WTF? Can't she write "I will get back to you in [however many weeks]?" So I decided to give up on her and will be working with my department's staff person instead, who has a lot of experience with organizing these events and is very sharp. We already talked about different ways to keep the costs down and different venues that we could use to organize parts of the event inexpensively.
Then all of a sudden I hear from a completely new person who supposedly is to provide the budget. She drafted the most ridiculous piece of information I have ever seen. She pulled up registration fees from the last conference incarnation which was in Europe; all the prices are in euros, and she didn't bother converting (we all know that 500 euros is totally the same as 500 US dollars). Btw, this was completely unnecessary because I plan on setting the conference registration fees after I know how much the event will approximately cost. She looked at the conference itinerary but didn't price it, instead sent me a generic "pricing" which is not even an order of magnitude correct. It's a mixture of per-person costs (such as food per day per participant), quantities that don't scale with the number of people (such as the fee for using a room), as well as some ridiculous cumulative numbers such as rentals of 800 poster boards (where the hell did that number come from?). Am I now supposed to be thrilled that they graced me with their attention after the whole summer of ignoring me, and offered such a half-assed effort on pricing out a very specific itinerary?
Let's just say I am thoroughly unimpressed.
I am organizing a conference in my specialty in the spring. It's not a huge one, 250 people or so, but enough to require some serious planning. So I started well ahead, early last spring, and among other things contacted the campus business center and was connected with a person who was supposed to help everything run smoothly. However, I should have guessed it would be a rough ride when she stood me up at our first meeting, where I was supposed to tour the beautiful, newly-built facility with lots of meeting rooms. No one is more pissed than an uncomfortable hugely pregnant woman who is wet, cold, and has been stood up. We rescheduled and I eventually toured the facilities.
Then I gave her a detailed schedule (this conference has a well-defined layout and duration) sometime last spring and discussed options for online registration, different catering options, how to most easily have prepaid lunches for participants etc., where to have the banquet, poster session etc. She said she'd get back to me shortly with a break-even budget, so I can go and start looking for support from different agencies and professional societies. That was last spring. Over the course of the summer, I contacted the woman via email several times to ask when I could expect the budget. She completely ignored me. It's been months since I last heard from her. I know she's busy with other conferences in the meantime, but WTF? Can't she write "I will get back to you in [however many weeks]?" So I decided to give up on her and will be working with my department's staff person instead, who has a lot of experience with organizing these events and is very sharp. We already talked about different ways to keep the costs down and different venues that we could use to organize parts of the event inexpensively.
Then all of a sudden I hear from a completely new person who supposedly is to provide the budget. She drafted the most ridiculous piece of information I have ever seen. She pulled up registration fees from the last conference incarnation which was in Europe; all the prices are in euros, and she didn't bother converting (we all know that 500 euros is totally the same as 500 US dollars). Btw, this was completely unnecessary because I plan on setting the conference registration fees after I know how much the event will approximately cost. She looked at the conference itinerary but didn't price it, instead sent me a generic "pricing" which is not even an order of magnitude correct. It's a mixture of per-person costs (such as food per day per participant), quantities that don't scale with the number of people (such as the fee for using a room), as well as some ridiculous cumulative numbers such as rentals of 800 poster boards (where the hell did that number come from?). Am I now supposed to be thrilled that they graced me with their attention after the whole summer of ignoring me, and offered such a half-assed effort on pricing out a very specific itinerary?
Let's just say I am thoroughly unimpressed.
Wednesday, September 14, 2011
Call for Entries: Carnival on Theoretical/Computational Sciences Due Sunday, September 25
If you do theoretical/computational work in the sciences, please consider writing a post that tells us a little bit about what your work entails, what you enjoy/dislike, what types of problems you tackle, what made you chose your specialization, etc. You don't have to be too specific, but you certainly can if you want. Send a link (a comment here or drop me an email) so I can aggregate the posts. Even if you don't blog, consider either writing a lengthier comment here and I will link to that as your post (please don't post as Anonymous, give yourself some nickname for the occasion) or, alternatively, email me the text and I will put it up as a guest post.
It's perfectly fine to recycle some of your old favorite posts.
Why this carnival? Yes, we use math and computers. A lot. But I would like everyone else to learn a little bit about what makes us tick, what makes us successful, what types of problems we encounter, and why what we do is so darn awesome!
Deadline: Sunday, September 25.
Please leave a comment here or drop me a line if you are participating (geekmommyprof at gmail.com) so I can be on the lookout.
I would like to put the carnival summary post up on Monday, September 26.
Thanks in advance for participating!
It's perfectly fine to recycle some of your old favorite posts.
Why this carnival? Yes, we use math and computers. A lot. But I would like everyone else to learn a little bit about what makes us tick, what makes us successful, what types of problems we encounter, and why what we do is so darn awesome!
Deadline: Sunday, September 25.
Please leave a comment here or drop me a line if you are participating (geekmommyprof at gmail.com) so I can be on the lookout.
I would like to put the carnival summary post up on Monday, September 26.
Thanks in advance for participating!
Wednesday, September 7, 2011
I Heart Theory
Fellow theorist Thoreau just had a cool post "What do theoretical physicists actually do?". In the post, he answers the question by looking at some of his papers and analyzing what the work entailed and how it differed from what students are taught to consider as theory based on their lecture courses.
The purpose of this post is twofold. First, I would like to tell you a little bit about some of the work I do, in a fashion similar to what Thoreau did. I have formal training in theoretical physics as well as an engineering discipline, and I my work is theoretical/computational* in the field of condensed matter physics. I work on problems ranging from very basic (firmly in the physics realm) to very applied (firmly in the engineering realm). Most of my work is published in Physical Review B, Applied Physics Letters, Journal of Applied Physics, and ACS Nano (reputable society level journals). Higher impact papers are published in the prestigious Physical Review Letters (often lovingly called Physical Review Lottery) and certain GlamourMagz (Nature, Nature Materials/Physics/Photonics/Nanotechnology).
My most cited paper is a GlamourMag paper with experimental colleagues. They came to me with some excellent experimental data, but that alone was not enough for a high profile paper, because it was not clear what was going on. We talked a few times about the details of experiments, and we came up with a qualitative picture that we all felt corresponded with the experiment. Then it took me a few days to set up the theoretical model -- in this case, an ordinary differential equation with appropriate boundary conditions; the novelty was in recognizing how the boundary conditions need to be set up in order to have a well-posed problem that corresponds to experiment. The derivations took a few days and another couple of days to code it up, and we had a quantitative model that corresponded with the data well and was intuitively plausible. Ultimately, we published a nice high impact paper, and I often joke that this is the easiest paper I have ever done, yet it brings the best rewards. The code, which is not very complicated or long, is still being used and fiddled with by many of my collaborators' experimental students.
I do a lot of theory connected to experiment (Massimo would call me an "experimentalist without a screwdriver"). My students enjoy such projects, as it means they get to collaborate with experimental students a lot, so the more the merrier. Also, this work helps my students get to know other professors really well (great for when you need those recommendation letters). I also have projects as part of larger theory/experiment collaborations, where the theory is virtually independent. For instance, on one such project, the question we asked was a very broad, general question, which at the time had neither a theoretical nor an experimental answer. So the experimental folks did some serious equipment building, while a theory collaborator and I, with a joint student, did some serious code development. This was a type of problem where you generally know what types of partial differential equations you need to write down to describe the system, but solving them together is extremely complicated. So the challenge for us in this case was to develop a computational tool that could solve all these equations in realistic systems and self-consistently (e.g. output from the solution to one set of equations is input for the second set, output from the second one is input for the first; you need a set of outputs that simultaneously satisfies both sets of equations; this is usually achieved using iterative procedures). It took a full three years with a very talented and independent student (so it would have taken significantly longer with an average one), and we now have a series of papers that promise to become very influential as they open completely new computational vistas.
My own personal inclination and most of my formal training was towards the "pen and paper" theory. However, once I came to the US and started working in a more applied field in grad school, I have come to really enjoy numerical computation, as it really enables you to get much closer to reality (especially in condensed matter physics) than you ever could with just a pen and paper. Some of my best cited "theory only" work deals with careful calculations of properties of systems that have become very "hot" in the last several years. We offered a rigorous analysis of their properties, with many important details, and predicted that the properties were much more modest than some initial flashy experiements suggested. Indeed, several years later, careful experiments showed exactly what we had predicted (quantitatively). Often, I feel my group's job is to deliver bad news -- that things are not as fast, cool, or otherwise shiny as one would hope. That's your fate when you work in the so-called "dirty limit" (means systems with lots of disorder, strain, and various imperfections; realistic systems are nearly always "dirty").
And then there are the super fun theory projects, where perhaps you get to dip into some math that you don't use every day, such as differential geometry or group theory in my work, and you get to derive a completely new and beautiful theoretical model (i.e. a new set of equations) that is capable of describing a whole class of heretofore un- or underexplored systems. I loooove this type of work; my PhD was like that and I published this type of work alone for the first several years on the TT. Now, I find this type of work is very rewarding (intellectually), but it is not for everyone. Most of my students are interested in more applied work, with ties to experiment, where the model (underlying equations) is known at least in principle, and where the bulk of the work is computational. Those who thrive on the more math-heavy project are harder to find; it's even more rare to find a student who is really into these mathematically challenging problems but can also write code well. I am lucky to have one excellent student who is like this (yes, that's the one who gives me the most headaches; but he's super talented. *sigh*), and it seems that one of my newbie students will be like that too. Otherwise, these projects would have to wait for me, and that would unfortunately be a very long wait, considering how busy the rest of my job keeps me.
All in all, I enjoy a diverse research portfolio, even if it makes me "a jack of all trades, a master of none" (and is likely not the best way to quickly achieve upper echelons in your chosen subfield). As doing theory costs much less than experiment, it is much easier to change fields or research directions -- you don't have to raise lots of funds to do the transition, which is a major perk (I wrote previously about the relative benefits of doing experiment vs theory.)
This brings me to the second purpose of this post: to start a carnival of sorts, with people who do theory/computation in the sciences contributing with a post on what their experiences in their daily work are. I have no idea how successful this call would be, but let's say within the next week or so:
If you do theoretical/computational work in the sciences, please consider writing a post that tells us a little bit about what your work entails, what you enjoy/dislike, what types of problems you tackle, what made you chose your specialization, etc. You don't have to be too specific, but you certainly can if you want. Send a link (a comment here or drop me an email) so I can aggregate the posts. Even if you don't blog, consider either writing a lengthier comment here and I will link to that as your post (please don't post as Anonymous, give yourself some nickname for the occasion) or, alternatively, email me the text and I will put it up as a guest post.
Finally, this post would not be complete without a link to the (fairly new) APS Division of Computational Physics news blog.
Happy calculations, and may all your iterations converge!
------
* See, for instance, this post by Massimo on why computational physics is not a special third branch of physics, but means you are a theorist who does calculations primarily on a computer.
The purpose of this post is twofold. First, I would like to tell you a little bit about some of the work I do, in a fashion similar to what Thoreau did. I have formal training in theoretical physics as well as an engineering discipline, and I my work is theoretical/computational* in the field of condensed matter physics. I work on problems ranging from very basic (firmly in the physics realm) to very applied (firmly in the engineering realm). Most of my work is published in Physical Review B, Applied Physics Letters, Journal of Applied Physics, and ACS Nano (reputable society level journals). Higher impact papers are published in the prestigious Physical Review Letters (often lovingly called Physical Review Lottery) and certain GlamourMagz (Nature, Nature Materials/Physics/Photonics/Nanotechnology).
My most cited paper is a GlamourMag paper with experimental colleagues. They came to me with some excellent experimental data, but that alone was not enough for a high profile paper, because it was not clear what was going on. We talked a few times about the details of experiments, and we came up with a qualitative picture that we all felt corresponded with the experiment. Then it took me a few days to set up the theoretical model -- in this case, an ordinary differential equation with appropriate boundary conditions; the novelty was in recognizing how the boundary conditions need to be set up in order to have a well-posed problem that corresponds to experiment. The derivations took a few days and another couple of days to code it up, and we had a quantitative model that corresponded with the data well and was intuitively plausible. Ultimately, we published a nice high impact paper, and I often joke that this is the easiest paper I have ever done, yet it brings the best rewards. The code, which is not very complicated or long, is still being used and fiddled with by many of my collaborators' experimental students.
I do a lot of theory connected to experiment (Massimo would call me an "experimentalist without a screwdriver"). My students enjoy such projects, as it means they get to collaborate with experimental students a lot, so the more the merrier. Also, this work helps my students get to know other professors really well (great for when you need those recommendation letters). I also have projects as part of larger theory/experiment collaborations, where the theory is virtually independent. For instance, on one such project, the question we asked was a very broad, general question, which at the time had neither a theoretical nor an experimental answer. So the experimental folks did some serious equipment building, while a theory collaborator and I, with a joint student, did some serious code development. This was a type of problem where you generally know what types of partial differential equations you need to write down to describe the system, but solving them together is extremely complicated. So the challenge for us in this case was to develop a computational tool that could solve all these equations in realistic systems and self-consistently (e.g. output from the solution to one set of equations is input for the second set, output from the second one is input for the first; you need a set of outputs that simultaneously satisfies both sets of equations; this is usually achieved using iterative procedures). It took a full three years with a very talented and independent student (so it would have taken significantly longer with an average one), and we now have a series of papers that promise to become very influential as they open completely new computational vistas.
My own personal inclination and most of my formal training was towards the "pen and paper" theory. However, once I came to the US and started working in a more applied field in grad school, I have come to really enjoy numerical computation, as it really enables you to get much closer to reality (especially in condensed matter physics) than you ever could with just a pen and paper. Some of my best cited "theory only" work deals with careful calculations of properties of systems that have become very "hot" in the last several years. We offered a rigorous analysis of their properties, with many important details, and predicted that the properties were much more modest than some initial flashy experiements suggested. Indeed, several years later, careful experiments showed exactly what we had predicted (quantitatively). Often, I feel my group's job is to deliver bad news -- that things are not as fast, cool, or otherwise shiny as one would hope. That's your fate when you work in the so-called "dirty limit" (means systems with lots of disorder, strain, and various imperfections; realistic systems are nearly always "dirty").
And then there are the super fun theory projects, where perhaps you get to dip into some math that you don't use every day, such as differential geometry or group theory in my work, and you get to derive a completely new and beautiful theoretical model (i.e. a new set of equations) that is capable of describing a whole class of heretofore un- or underexplored systems. I loooove this type of work; my PhD was like that and I published this type of work alone for the first several years on the TT. Now, I find this type of work is very rewarding (intellectually), but it is not for everyone. Most of my students are interested in more applied work, with ties to experiment, where the model (underlying equations) is known at least in principle, and where the bulk of the work is computational. Those who thrive on the more math-heavy project are harder to find; it's even more rare to find a student who is really into these mathematically challenging problems but can also write code well. I am lucky to have one excellent student who is like this (yes, that's the one who gives me the most headaches; but he's super talented. *sigh*), and it seems that one of my newbie students will be like that too. Otherwise, these projects would have to wait for me, and that would unfortunately be a very long wait, considering how busy the rest of my job keeps me.
All in all, I enjoy a diverse research portfolio, even if it makes me "a jack of all trades, a master of none" (and is likely not the best way to quickly achieve upper echelons in your chosen subfield). As doing theory costs much less than experiment, it is much easier to change fields or research directions -- you don't have to raise lots of funds to do the transition, which is a major perk (I wrote previously about the relative benefits of doing experiment vs theory.)
This brings me to the second purpose of this post: to start a carnival of sorts, with people who do theory/computation in the sciences contributing with a post on what their experiences in their daily work are. I have no idea how successful this call would be, but let's say within the next week or so:
If you do theoretical/computational work in the sciences, please consider writing a post that tells us a little bit about what your work entails, what you enjoy/dislike, what types of problems you tackle, what made you chose your specialization, etc. You don't have to be too specific, but you certainly can if you want. Send a link (a comment here or drop me an email) so I can aggregate the posts. Even if you don't blog, consider either writing a lengthier comment here and I will link to that as your post (please don't post as Anonymous, give yourself some nickname for the occasion) or, alternatively, email me the text and I will put it up as a guest post.
Finally, this post would not be complete without a link to the (fairly new) APS Division of Computational Physics news blog.
Happy calculations, and may all your iterations converge!
------
* See, for instance, this post by Massimo on why computational physics is not a special third branch of physics, but means you are a theorist who does calculations primarily on a computer.
Tuesday, September 6, 2011
Public Service Announcement
Gossip rule No 1:
If you want to maintain the (clearly false) appearance of a kind and thoughtful online persona in front of certain people, then I recommend that you mock those people behind their backs only where they truly won't be able to see it. So, yeah, you may want to avoid Twitter.
If you want to maintain the (clearly false) appearance of a kind and thoughtful online persona in front of certain people, then I recommend that you mock those people behind their backs only where they truly won't be able to see it. So, yeah, you may want to avoid Twitter.
Thursday, September 1, 2011
A Quick-and-Dirty Post on Work-Life Balance
Busy with white papers and Smurfilicious adventures, but couldn't help but notice recent waves around the blogosphere (notable posts by DrugMonkey, Odyssey, and Thoreau) that surrounded this Nature News article.
Here is a bit of a medley post from the comments I left a few places. From the comment at DM's place:
Odyssey said: "Dumb and lazy will kill your career. Having children will not."
I will agree with Odyssey here, but with a qualifier. Having children will not destroy your career, but it will likely alter it -- temporarily or permanently -- unless you have someone to completely shoulder the burden at home. But do you really want to have a family and never be around to enjoy it?
Anyway, regarding altering one's career: I have kids and am at an R1 public school, the state's flagship, and according to all metrics I am doing pretty well. If I hadn't had children, would I have been at MIT or Stanford (top places in my field)? Maybe, but maybe not. There is nothing that guarantees reaching the upper echelons of academia; for every laser-focused workaholic who got there by forgoing all else, there are hordes of people who sacrificed just as much or more and didn't get there. All I know for sure is that I wasn't going to have a family and not be there to raise them.
To me, and I dare say to most scientists with families, family is what brings sanity and balance back into one's life. For me, it was never a question of either-or; I would not be anywhere near happy without my family or without my career. They are both great passions, if you will, and the only way for me to feel successful is to combine and enjoy both of them. Anything else -- even the highest imaginable professional standing without the personal life -- would feel like a failure.
(This is NOT a judgement of people without kids. This is my view of my own personal choices.)
It is fine if you are willing to sacrifice everything for your career, but just be aware that career is a harsh and fickle mistress and your everything may still not be enough. Odyssey nicely points out that luck is a factor in reaching awesome professional heights. There are never guarantees, no matter how smart, ambitious, driven, focused, and ready to jump under the bus for your science you are. If you make it, don't delude yourself that it's all just your merit and awesome planning. You also lucked out, so go buy some lottery tickets already. (Some of us would say: your career is like a highly nonlinear classical system -- change initial conditions just a little bit, the system's evolution changes dramatically.) I know several very sad people who are middle-aged and lonely, who worked like maniacs during their youth and completely let their personal life fall by the wayside, are divorced and without children, which they say wish they had had. And their science is still mediocre.
Thoreau discusses the Slave-Driver Superstar (works 24/7 and expect the same from underlings) with the Perfect Balancer Superstar (PBS for short) -- you know, those people who are nauseatingly perfect at everything they do and they do 3x more of everything than a mere mortal. He makes a very astute observation: I privately suspect that these people have far more in common with Quinones-Hinojosa [the 24/7 guy] than the folks constantly talking about “balance” realize. In other words, he believes that PBSs are actually much closer to the slave drivers than us slightly (or not so slightly) unbalanced mortals, and it's not just that they both occupy the tails (albeit different ones) of the normal distribution. I completely agree, based on my experience working with one closely (from my comment):
I actually know a couple of “perfectly balanced” colleagues. One of them a frequent collaborator. I can tell you that in the case of this person the balance is just a manifestation of extreme control-freakishness and perfectionism. Their schedule is perfectly partitioned and there is absolutely no room for deviation. Yes, the schedule is 8 or 9 hours of work, however many hours of activities with kids, church, whatever, but the point is that they are in control of every single minute. I think they get high on control, it’s very very important to them. (This extends to this person's relationship with food, I find.) This does not make for very good collaborators, I will tell you that — it takes me several weeks to be graced with 10 min of this person’s time (because their schedule is so jam-packed for weeks and no changes are allowed). And don’t get me started with turnaround time for returning comments on joint papers.
The thing with work-life balance (for the commonly unbalanced specimens among us) is this: how often do you feel happy? I mean, we are all stressed out, grants get rejected, students and colleagues annoy us, our spouses and kids can sure make our blood boil, but how often do you stop and feel the warm breeze of pure unadulterated joy? If it never happens, your choices suck for you. My family and my work both bring me headaches but also tremendous joy (kids are awesome in the joy-bringing department), and I wouldn't have it any other way. Although I would not object to higher funding rates and a bit more sleep...
Here is a bit of a medley post from the comments I left a few places. From the comment at DM's place:
Odyssey said: "Dumb and lazy will kill your career. Having children will not."
I will agree with Odyssey here, but with a qualifier. Having children will not destroy your career, but it will likely alter it -- temporarily or permanently -- unless you have someone to completely shoulder the burden at home. But do you really want to have a family and never be around to enjoy it?
Anyway, regarding altering one's career: I have kids and am at an R1 public school, the state's flagship, and according to all metrics I am doing pretty well. If I hadn't had children, would I have been at MIT or Stanford (top places in my field)? Maybe, but maybe not. There is nothing that guarantees reaching the upper echelons of academia; for every laser-focused workaholic who got there by forgoing all else, there are hordes of people who sacrificed just as much or more and didn't get there. All I know for sure is that I wasn't going to have a family and not be there to raise them.
To me, and I dare say to most scientists with families, family is what brings sanity and balance back into one's life. For me, it was never a question of either-or; I would not be anywhere near happy without my family or without my career. They are both great passions, if you will, and the only way for me to feel successful is to combine and enjoy both of them. Anything else -- even the highest imaginable professional standing without the personal life -- would feel like a failure.
(This is NOT a judgement of people without kids. This is my view of my own personal choices.)
It is fine if you are willing to sacrifice everything for your career, but just be aware that career is a harsh and fickle mistress and your everything may still not be enough. Odyssey nicely points out that luck is a factor in reaching awesome professional heights. There are never guarantees, no matter how smart, ambitious, driven, focused, and ready to jump under the bus for your science you are. If you make it, don't delude yourself that it's all just your merit and awesome planning. You also lucked out, so go buy some lottery tickets already. (Some of us would say: your career is like a highly nonlinear classical system -- change initial conditions just a little bit, the system's evolution changes dramatically.) I know several very sad people who are middle-aged and lonely, who worked like maniacs during their youth and completely let their personal life fall by the wayside, are divorced and without children, which they say wish they had had. And their science is still mediocre.
Thoreau discusses the Slave-Driver Superstar (works 24/7 and expect the same from underlings) with the Perfect Balancer Superstar (PBS for short) -- you know, those people who are nauseatingly perfect at everything they do and they do 3x more of everything than a mere mortal. He makes a very astute observation: I privately suspect that these people have far more in common with Quinones-Hinojosa [the 24/7 guy] than the folks constantly talking about “balance” realize. In other words, he believes that PBSs are actually much closer to the slave drivers than us slightly (or not so slightly) unbalanced mortals, and it's not just that they both occupy the tails (albeit different ones) of the normal distribution. I completely agree, based on my experience working with one closely (from my comment):
I actually know a couple of “perfectly balanced” colleagues. One of them a frequent collaborator. I can tell you that in the case of this person the balance is just a manifestation of extreme control-freakishness and perfectionism. Their schedule is perfectly partitioned and there is absolutely no room for deviation. Yes, the schedule is 8 or 9 hours of work, however many hours of activities with kids, church, whatever, but the point is that they are in control of every single minute. I think they get high on control, it’s very very important to them. (This extends to this person's relationship with food, I find.) This does not make for very good collaborators, I will tell you that — it takes me several weeks to be graced with 10 min of this person’s time (because their schedule is so jam-packed for weeks and no changes are allowed). And don’t get me started with turnaround time for returning comments on joint papers.
The thing with work-life balance (for the commonly unbalanced specimens among us) is this: how often do you feel happy? I mean, we are all stressed out, grants get rejected, students and colleagues annoy us, our spouses and kids can sure make our blood boil, but how often do you stop and feel the warm breeze of pure unadulterated joy? If it never happens, your choices suck for you. My family and my work both bring me headaches but also tremendous joy (kids are awesome in the joy-bringing department), and I wouldn't have it any other way. Although I would not object to higher funding rates and a bit more sleep...
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