Just had a thought while taking a shower the other day. These days, there's lots of effort to rate appliances by their efficiency. But it occurs to me that inefficiency leads to heat, and if you are heating your home, then you are basically using all that "wasted" energy. So even if some of the gas used for our water heater doesn't actually heat the water, as long as its in the basement and the heat travels upward, that heat is not going to waste. So the effective efficiency of the appliance is actually higher than expected. Conversely, in summer, if you use the air conditioner, the opposite is true. I guess the overall efficiency would depend on your mix of heating and cooling.
I was also thinking about this a while ago when I installed a bunch of LED lightbulbs. Although they use much less energy, they are producing much less heat to warm up the house. I mentioned this to Gautham, and he pointed out that using electricity to heat your house may be considerably less efficient than, say, natural gas, and so that means it's not 100% efficient, relatively speaking. Still, it's better than what one would naively expect.
Of course, the best thing about LED lightbulbs is not so much the electricity or cost savings (which are pretty modest, frankly), but the fact that they don't burn out. If you have a bunch of 50W halogen spotlights, you know what I mean. By the way, just got a "TorchStar UL-listed 110V 5W GU10 LED Bulb - 2700K Warm White LED Spotlight - 320 Lumen 36 Degree Beam Angle GU10 Base for Home" from Amazon, and it looks great (better than the other one I got from Amazon for sure).
Friday, November 7, 2014
Thursday, November 6, 2014
Why are papers important for getting faculty positions?
Loved Lenny's post about how a high profile paper out of your postdoc is not required for many positions in academia. The list he has is pretty good proof of that fact, and I know firsthand from my own experience–I think my "big postdoc paper" was just submitted by the time I had my last interview.
I think it's important to keep in mind, though, that the existence of such examples is not a proof that there are no causal connections between the two. I think a lot of this is field dependent as well as institution dependent. For instance, I definitely feel like my job search might have been easier with a published paper, especially in biology/medical departments. And I have definitely heard of places, for example in other countries, in which applicants have been explicitly told that the job is theirs if and only if their postdoc paper is accepted. And I have heard this multiple times, so it was not a one-off.
Why? If the search committee understands the work and the researcher and believes in them both, then why does the existence of an accepted high profile paper matter so much in and of itself? A big part of the answer is that visibility matters.
One thing I realized after starting my faculty job was that starting a lab is a hard business, and part of that business is getting people interested in your research. There are tons of people out there doing science. Why should someone want to join your lab? Why should anyone care about your work? Why should anyone give you funding to do this work? Why should you be the one to succeed when everyone else is out there doing good science as well? Having a high profile paper when you start is undeniably a part of the answer to these questions. And it’s also a simple metric of success that is readily interpreted by people across disciplines.
Departments generally want the people they hire to succeed. There are many reasons why it's a lot easier to succeed if you have a fancy paper as you are starting your lab. It helps in recruiting students and postdocs, and in getting grants and getting invited to talks. Same thing goes for coming from the lab of a big-name PI. The big-name PI will be out talking about your work at venues and forums that you can only dream about as a junior PI. These are all different pieces of the puzzle, and nice papers are for sure an important piece of that puzzle, for better or for worse. And the fact is that there is at least some correlation between where you publish (especially averaging over time) and the quality and importance of your work. Not a perfect one for various reasons, and I hate the current publishing system, but it is disingenuous to pretend that this is not the case.
I'm sure many people out there are saying "It should all be about the science, not where it's published or who you worked with or all that other stuff." Sure, sounds nice in theory, but in practice, it's harder than people think. Imagine you are in the market for a washing machine. You go to the store and there are hundreds of washing machines to choose from. Some come from name brands, some are completely unknown. Some of name brand ones are rated in Consumer Reports by a handful of "washing machine experts", and some are rated much higher than others. Which one would you buy? Now imagine you are in the market for a colleague for at least the next 6-7 years, hopefully the next 30-40+ years, and you will be investing millions of dollars in this person and be interacting with them regularly on a professional basis. Their success or failure will reflect directly on your department. You better believe people make a pretty considered decision here. And yes, visibility matters. Personal connections matter. Papers matter. Your personality matters. Your science matters. EVERYTHING matters. Seriously, think about it: how could it possibly be otherwise?
Why? If the search committee understands the work and the researcher and believes in them both, then why does the existence of an accepted high profile paper matter so much in and of itself? A big part of the answer is that visibility matters.
One thing I realized after starting my faculty job was that starting a lab is a hard business, and part of that business is getting people interested in your research. There are tons of people out there doing science. Why should someone want to join your lab? Why should anyone care about your work? Why should anyone give you funding to do this work? Why should you be the one to succeed when everyone else is out there doing good science as well? Having a high profile paper when you start is undeniably a part of the answer to these questions. And it’s also a simple metric of success that is readily interpreted by people across disciplines.
Departments generally want the people they hire to succeed. There are many reasons why it's a lot easier to succeed if you have a fancy paper as you are starting your lab. It helps in recruiting students and postdocs, and in getting grants and getting invited to talks. Same thing goes for coming from the lab of a big-name PI. The big-name PI will be out talking about your work at venues and forums that you can only dream about as a junior PI. These are all different pieces of the puzzle, and nice papers are for sure an important piece of that puzzle, for better or for worse. And the fact is that there is at least some correlation between where you publish (especially averaging over time) and the quality and importance of your work. Not a perfect one for various reasons, and I hate the current publishing system, but it is disingenuous to pretend that this is not the case.
I'm sure many people out there are saying "It should all be about the science, not where it's published or who you worked with or all that other stuff." Sure, sounds nice in theory, but in practice, it's harder than people think. Imagine you are in the market for a washing machine. You go to the store and there are hundreds of washing machines to choose from. Some come from name brands, some are completely unknown. Some of name brand ones are rated in Consumer Reports by a handful of "washing machine experts", and some are rated much higher than others. Which one would you buy? Now imagine you are in the market for a colleague for at least the next 6-7 years, hopefully the next 30-40+ years, and you will be investing millions of dollars in this person and be interacting with them regularly on a professional basis. Their success or failure will reflect directly on your department. You better believe people make a pretty considered decision here. And yes, visibility matters. Personal connections matter. Papers matter. Your personality matters. Your science matters. EVERYTHING matters. Seriously, think about it: how could it possibly be otherwise?
Monday, November 3, 2014
Why don’t bioinformaticians learn how to run gels?
Just read an interesting post from Sean Eddy about genomics. Lots of points there about sequencing and big science and other stuff that seems well above my pay grade. But the post also brings up the notion that biologists should be able to do their own data analysis, in particular scripting with Perl/Python. I’ve heard this subjected debated before many times, and I’m sure I’ll hear it again. But I don't think it's the right way to think about it.
First off, I want to say that I agree with the underlying premise in theory. Yes, it would be great for everyone to have some basic skills in quantitative analysis and programming. It would certainly be useful for biologists to be able to analyze their own data, and we do all our own analysis at the command line in the lab, typically using tools graciously and freely provided by others. For others with different skills and interests, there is finite time in the day, and maybe they don’t have the time and inclination to learn this stuff. To require biologists to learn to do things at the command line is I think missing a huge opportunity, and is also a bit unfair.
Consider the following: how many bioinformaticians are required to learn and perform library prep to do their work? And what if we told them to “just figure it out by Googling around”? I’m not even talking about understanding all the various technical aspects of library prep, I mean even just doing the basic protocols. Probably not very many have been required to do this. I’m sure they could do it and figure it out, but why should they, you might ask? A reasonable question. Well, then why should biologists be subjected to the pain of shell/Perl scripting just to figure out if some genes’ expression went up or down? Why does this work in only one direction? Remember, scripting is NOT SCIENCE. It is just a tool. I see no reason why everyone should have to learn about all the details of every tool in order to do their science. This even applies just within the realm of computation: how many people who use the log function know anything about how to implement it? Going up the chain, I don’t need to know why MATLAB uses Householder transformations to compute a QR factorization instead of Gram-Schmidt or even that it does so at all–I can just call it and trust that MATLAB does the best thing by default. That is the nature of a mature tool.
Indeed, it is particularly ironic to hear these calls for DIY learning from genomic informaticians, when the experimental side of that same work is amongst the most commoditized and standardized bench work in existence (funnily enough, to a point where bioinformaticians might actually be able to do it with only minimal training!). Basically, add and remove liquids to/from each other for 1-2 days, squirt it in some sequencing chip and say go, then download the data. It’s pretty close to the big green “GO” button that everyone dreams about. And it comes from years of careful thought and consideration about the needs of the USER of the tool, not of the provider. Make no mistake, the technology underlying sequencing is very complicated and sophisticated. But the reason sequencing has taken off the way it has is because USING the (hardware/wetware) tool is very simple. Just like scripting/data processing, sequencing is not science, but a tool. It is, at this point, a much easier to use one than analysis software, in my opinion.
I of course appreciate that part of the reason that sequencing itself is so well developed is because there are huge companies with tremendous resources backing the effort. Fair enough. Perhaps it will require a commercial effort to build an easy to use pipeline for analysis. Maybe not. Either way, though, I think the main thing to keep in mind if you are in the tool business is that if you want people to use your tool, you will get a lot further by LISTENING (and I mean actually listening) to your users and their needs than you will by simply telling them about all the things that they ought to do and ought to know. It’s hard work, and requires a lot of thought and attention, and I certainly understand the sentiment that it may not fall within the purview of academic work. But I think it needs to happen one way or another. In the same way that simplified mobile operating systems brought computation to many more people than before, so will easy to use bioinformatics pipelines bring sequencing tools to many more biologists, which is a good thing.
This is most certainly not to say that biologists shouldn't be getting some more quantitative training, especially in computers. There is no doubt that learning some principles of programming and quantitative/statistical analysis can be hugely beneficial, given the way science as a whole is headed. Again, that is not the same thing as learning scripting. In fact, being able to script is completely unrelated to quantitative thinking and only moderately related to any high level concepts in programming. It is busywork, plain and simple. In my lab, we do quantitative work, and writing these scripts is still basically what I would consider a big waste of time. We can do it, but it has nothing to do with science, quantitative or otherwise, and most of us would much rather not have to bother. Even worse for science is that the requirement of scripting leaves those who can’t do it because of limited time or whatever out in the cold.
Oh, and by the way, I think Galaxy is a great step in this direction. Bravo to the developers, and thank you for your hard work!
Update, 11/4: In case you're wondering if we practice what we preach, we have two versions of our image analysis software. One is open source, very powerful, completely extensible, fancy software engineering, etc. The other one is super limited, but designed for use by scientists, not programmers. Both are freely available, but guess which one gets used by orders of magnitude more people...
First off, I want to say that I agree with the underlying premise in theory. Yes, it would be great for everyone to have some basic skills in quantitative analysis and programming. It would certainly be useful for biologists to be able to analyze their own data, and we do all our own analysis at the command line in the lab, typically using tools graciously and freely provided by others. For others with different skills and interests, there is finite time in the day, and maybe they don’t have the time and inclination to learn this stuff. To require biologists to learn to do things at the command line is I think missing a huge opportunity, and is also a bit unfair.
Consider the following: how many bioinformaticians are required to learn and perform library prep to do their work? And what if we told them to “just figure it out by Googling around”? I’m not even talking about understanding all the various technical aspects of library prep, I mean even just doing the basic protocols. Probably not very many have been required to do this. I’m sure they could do it and figure it out, but why should they, you might ask? A reasonable question. Well, then why should biologists be subjected to the pain of shell/Perl scripting just to figure out if some genes’ expression went up or down? Why does this work in only one direction? Remember, scripting is NOT SCIENCE. It is just a tool. I see no reason why everyone should have to learn about all the details of every tool in order to do their science. This even applies just within the realm of computation: how many people who use the log function know anything about how to implement it? Going up the chain, I don’t need to know why MATLAB uses Householder transformations to compute a QR factorization instead of Gram-Schmidt or even that it does so at all–I can just call it and trust that MATLAB does the best thing by default. That is the nature of a mature tool.
Indeed, it is particularly ironic to hear these calls for DIY learning from genomic informaticians, when the experimental side of that same work is amongst the most commoditized and standardized bench work in existence (funnily enough, to a point where bioinformaticians might actually be able to do it with only minimal training!). Basically, add and remove liquids to/from each other for 1-2 days, squirt it in some sequencing chip and say go, then download the data. It’s pretty close to the big green “GO” button that everyone dreams about. And it comes from years of careful thought and consideration about the needs of the USER of the tool, not of the provider. Make no mistake, the technology underlying sequencing is very complicated and sophisticated. But the reason sequencing has taken off the way it has is because USING the (hardware/wetware) tool is very simple. Just like scripting/data processing, sequencing is not science, but a tool. It is, at this point, a much easier to use one than analysis software, in my opinion.
I of course appreciate that part of the reason that sequencing itself is so well developed is because there are huge companies with tremendous resources backing the effort. Fair enough. Perhaps it will require a commercial effort to build an easy to use pipeline for analysis. Maybe not. Either way, though, I think the main thing to keep in mind if you are in the tool business is that if you want people to use your tool, you will get a lot further by LISTENING (and I mean actually listening) to your users and their needs than you will by simply telling them about all the things that they ought to do and ought to know. It’s hard work, and requires a lot of thought and attention, and I certainly understand the sentiment that it may not fall within the purview of academic work. But I think it needs to happen one way or another. In the same way that simplified mobile operating systems brought computation to many more people than before, so will easy to use bioinformatics pipelines bring sequencing tools to many more biologists, which is a good thing.
This is most certainly not to say that biologists shouldn't be getting some more quantitative training, especially in computers. There is no doubt that learning some principles of programming and quantitative/statistical analysis can be hugely beneficial, given the way science as a whole is headed. Again, that is not the same thing as learning scripting. In fact, being able to script is completely unrelated to quantitative thinking and only moderately related to any high level concepts in programming. It is busywork, plain and simple. In my lab, we do quantitative work, and writing these scripts is still basically what I would consider a big waste of time. We can do it, but it has nothing to do with science, quantitative or otherwise, and most of us would much rather not have to bother. Even worse for science is that the requirement of scripting leaves those who can’t do it because of limited time or whatever out in the cold.
Oh, and by the way, I think Galaxy is a great step in this direction. Bravo to the developers, and thank you for your hard work!
Update, 11/4: In case you're wondering if we practice what we preach, we have two versions of our image analysis software. One is open source, very powerful, completely extensible, fancy software engineering, etc. The other one is super limited, but designed for use by scientists, not programmers. Both are freely available, but guess which one gets used by orders of magnitude more people...
Friday, October 24, 2014
The eleven stages of academic grief
Have had a spate of bad luck in the lab with several papers getting rejected. Ugh. Been getting used to the following cascade of emotions:
- Shock (30 seconds). E-mail from journal! Oh no, subject line says decision. Could be good decision, right? … Oh, not a good decision…
- Disbelief (1 minute). Really? Did I get the wrong email or something? Am I really reading this? Where is that link to resubmit? What do you mean there’s no link to resubmit?
- Reading the e-mail (2 minutes). Hmm. [Keyword search] “Should be published in this journal” Yes! “Not a big enough advance in the field” No! “Very exciting” Yes! “Hard to get excited about this paper” No!
- Anger with reviewers (5 minutes). What are they talking about? We already did that experiment in supp fig 97! Well, if I knew the answer to that, we would have submitted to a better journal! Oh, correlation isn’t the same as causation? Why didn't I think of that? Thank so much for your super wise words of wisdom dear reviewer. May you rot in hell, where you will have eternity to think about our paper, instead of the apparently 17 minutes you spent on this stupid review.
- Reviewers, part 2 (7 minutes). I bet that reviewer is [random perceived academic enemy]. Grudge deepening.
- Anger with editors (10 minutes). What are they talking about? Why don’t these people get a spine? Do these people even know anything about this field. Or any field. Or anything at all? They must be failed academics. Or just stupid. Or both.
- Self doubt, abilities (6 hours). I am a failed scientist. Soon to be a failed academic. Or just stupid. Or both.
- The dark path (1 day). Wait, but my paper is much better than this other stupid paper in a higher profile journal. What gives? [You know not to go down this road. But you will.]
- Self doubt, career choice (1.5 days). Why am I working so hard? Why didn’t I just go to industry and never have to worry about papers ever again? Why should I be sweating these stupid reviewer comments? Am I still going to be sweating these reviewer comments for the next 30-40 years? Is this really it?
- Resignation and acceptance (2 weeks). We will get this paper out in the end. Time to move on. This study is good, it just needs to find the right home. The darkest hour is just before the dawn. There are many fish in the sea. Every cloud has a silver lining. Who knows. Maybe the reviewers even had a point about that one aspect of our paper. Wait a minute…
- Reviewers, part 3 (2 weeks and 30 seconds): $^!# those reviewers! What do they know anyway? What are they talking about? If I ever see [random perceived academic enemy] again…
- Revise
- Resubmit
- Rejected
- Revise
- Resubmit
- Rejected
- Revise
- Resubmit
- Rejected
- …
Thursday, October 16, 2014
What makes a scientist creative?
Science is about generating knowledge, but it’s also about the process of generating knowledge, and few things delight as much as creative ways to generate knowledge. Some of my favorite examples include ribosome profiling from Jonathan Weissman’s lab, or Michael Elowitz’s two color noise experiments. Not that all scientific progress comes from creative experiments, nor do the results of all creative experiments stand the test of time. It’s just that these are the ones that are so awesome that you never forget about them.
Some scientists are just really good at coming up with creative ideas (Sanjay Tyagi, my former PhD advisor, is one of them). Where does scientific creativity come from? There is I think some notion that creativity is an innate ability, but I’ve come to think of creativity as a skill, which has an important distinction: skills can be learned and honed, whereas innate abilities cannot. Some amount of creativity is innate (perhaps having as much to do with interest in a topic as raw brainpower), but if you have someone with the raw materials to be a creative scientist, then you can help shape that material to make that scientist more creative than they would be otherwise. How? Does some of this just rub off from the mentor to the mentee? What in particular is it that can rub off?
I’m guessing there’s a lot of psychology research in this area, but here is a thought that I had recently. It came from an e-mail I had with one of my (very creative) trainees, which was an awesome moment as an advisor. I had just e-mailed the trainee, posing a question like “hmm, what are the implications of these results.” My trainee wrote back, saying “well, could inform x or y”, which is pretty much the current thinking in the field. And then I got another e-mail 10 minutes later saying “These are both silly answers. It is definitely something to continue to think about.” I was so proud!
This exchange got me thinking that maybe one of the underappreciated elements of being creative is just not settling for being not creative. If you are in science, there’s a pretty good chance that you have ideas, probably many ideas, maybe all the time. The key is really in the evaluation. When am I just settling for the status quo of thinking? When is the status quo probably right and there’s maybe nothing here? When have I really hit the foundation of the problem we’re working on? If I could do any experiment to test this, possible or impossible, what would it tell me? What is the closest I can approximate that in the lab? These are all things that we can consciously think about and that mentors can teach their mentees, and I think it can help us to be creative. I also think that establishing a rigorous culture of idea generation and evaluation can help the group as a whole become more creative.
Thinking about creativity reminds me about when I was in a band back in college. The leader of our band, Miguel, was one of the most creative people I have ever met–lyrics and music came out of him in ways that seemed mysterious and divine. (Incidentally, I feel like not settling was a big part for him as well.) He was really good friends with this other amazing songwriter named Joel, and Miguel used to say “You know how I know that Joel is a better songwriter than I am? Whenever I play someone a song I wrote, they say ‘Man, how did you ever think of that?’ When they hear a song Joel wrote, they say ‘Oh man, why didn’t I think of that?’” Same applies in science, I think.
Some scientists are just really good at coming up with creative ideas (Sanjay Tyagi, my former PhD advisor, is one of them). Where does scientific creativity come from? There is I think some notion that creativity is an innate ability, but I’ve come to think of creativity as a skill, which has an important distinction: skills can be learned and honed, whereas innate abilities cannot. Some amount of creativity is innate (perhaps having as much to do with interest in a topic as raw brainpower), but if you have someone with the raw materials to be a creative scientist, then you can help shape that material to make that scientist more creative than they would be otherwise. How? Does some of this just rub off from the mentor to the mentee? What in particular is it that can rub off?
I’m guessing there’s a lot of psychology research in this area, but here is a thought that I had recently. It came from an e-mail I had with one of my (very creative) trainees, which was an awesome moment as an advisor. I had just e-mailed the trainee, posing a question like “hmm, what are the implications of these results.” My trainee wrote back, saying “well, could inform x or y”, which is pretty much the current thinking in the field. And then I got another e-mail 10 minutes later saying “These are both silly answers. It is definitely something to continue to think about.” I was so proud!
This exchange got me thinking that maybe one of the underappreciated elements of being creative is just not settling for being not creative. If you are in science, there’s a pretty good chance that you have ideas, probably many ideas, maybe all the time. The key is really in the evaluation. When am I just settling for the status quo of thinking? When is the status quo probably right and there’s maybe nothing here? When have I really hit the foundation of the problem we’re working on? If I could do any experiment to test this, possible or impossible, what would it tell me? What is the closest I can approximate that in the lab? These are all things that we can consciously think about and that mentors can teach their mentees, and I think it can help us to be creative. I also think that establishing a rigorous culture of idea generation and evaluation can help the group as a whole become more creative.
Thinking about creativity reminds me about when I was in a band back in college. The leader of our band, Miguel, was one of the most creative people I have ever met–lyrics and music came out of him in ways that seemed mysterious and divine. (Incidentally, I feel like not settling was a big part for him as well.) He was really good friends with this other amazing songwriter named Joel, and Miguel used to say “You know how I know that Joel is a better songwriter than I am? Whenever I play someone a song I wrote, they say ‘Man, how did you ever think of that?’ When they hear a song Joel wrote, they say ‘Oh man, why didn’t I think of that?’” Same applies in science, I think.
Sunday, October 12, 2014
Disabled Google Plus comments
Hi there readers,
Quite some time ago, I enabled Google Plus comments on this blog, not fully knowing exactly what that would do. Seemed like a good feature, I thought. Only just recently did I realize that it required people to be on Google Plus to leave a comment, which really sucks. So I'm disabling that feature, because I know it discourages some commenters (like my mom). Sadly, this means that virtually all the comments on the posts for the last however long will be gone (which is why I was reluctant to switch). So sorry about this! Just want to say that I really appreciate all the comments that people have left here, and the only bright spot in doing this is that maybe this will result in more people leaving comments. If there isn't any uptick in comments, I'll re-enable the feature and all the old comments will come back.
Arjun
Quite some time ago, I enabled Google Plus comments on this blog, not fully knowing exactly what that would do. Seemed like a good feature, I thought. Only just recently did I realize that it required people to be on Google Plus to leave a comment, which really sucks. So I'm disabling that feature, because I know it discourages some commenters (like my mom). Sadly, this means that virtually all the comments on the posts for the last however long will be gone (which is why I was reluctant to switch). So sorry about this! Just want to say that I really appreciate all the comments that people have left here, and the only bright spot in doing this is that maybe this will result in more people leaving comments. If there isn't any uptick in comments, I'll re-enable the feature and all the old comments will come back.
Arjun
Saturday, October 11, 2014
What have I learned since being a PI?
Our lab started at Penn in January 2010, and the last several years been probably the most busy and action packed of my professional life. I still vividly remember the very beginning, when we had far more boxes than people. Actually, I guess that’s still the case. But lots of other stuff has changed, and the lab now feels like the bustling, fun place I had always hoped it would be. What I had not anticipated was how much I would change and learn, both as a scientist and as a person, since I started. Here are some musings and observations:
- I realized that as a group, scientists (meaning grad students, postdocs, PIs and all the other folks that make a lab go) are pretty lucky. They are by and large smart, talented, driven people who could succeed in many different walks of life. They happen to do academic science, but can probably do many other things successfully. It would be okay to do so. Also, staying in science is a privilege, not a right, one handed out with a lot more care than many people think.
- I stopped worrying as much about my career. Like, I need this paper to get this grant to get this job to get this… whatever. Partly, I’m just too tired and busy to do so. Partly, though, it’s also because I have realized just how lucky I am to do something I love, which I think is very rare in this world, especially for something as generally useless to the world at large as science. Not to say that I don’t want to get papers or grants or tenure or anything like that, nor is it something that I never think about, but just saying that the day to day makes me happy, for the most part.
- Life is long and can take scientists in many different directions. Academics have curious minds and will always be searching for new challenges, and doing what I'm doing now is just one of those challenges.
- I really want to try to do something important. I’ve now been in science just long enough now to have seen a few scientific fads come and go, and while I’m not much of a scholar of science history, I think that experience has helped me gain a somewhat better perspective on when we really learn something about the world. I also realize that I will probably fail to do something important, because it’s just really hard to do so. But I hope to have fun trying.
- Related to this last point: it’s hard to predict where your science will take you, whether it will lead to something important or not, either in your time or the next. But the quality of how you execute your science and the conclusions you draw is the one thing you can enforce. And in a way, it’s the only thing that matters.
- I learned to not dismiss crazy ideas, and allow flexibility to let them grow. Starting out, I thought that I was going to run this super tight ship, with every project subjected to rigorous risk/reward analysis. I still think that’s actually not a bad thing and that most people don’t do enough of that, but sometimes its good to just let things go. Some of the best things going in the lab come from projects that I didn’t think had much future at the time.
- It is hard to change fields. Once you’re going in a certain direction, it’s what everyone expects of you: your trainees, your colleagues, yourself. On top of this personal inertia, the system is also set up to prevent you from changing fields, because you rely on your social network for papers, grants, etc. Your only hope is to develop enough clout that people outside your field might give you the benefit of the doubt. Or to just be such a small fry that nobody really cares.
- The colleagues I admire most are the ones who don’t take things too seriously, especially themselves.
- I’ve learned a lot about how to do science over the last few years, and I’m a much better scientist for it. How do you frame a problem? What can you really claim based on this data? What are alternatives? Looking back at myself coming into this job, I feel like I was hopelessly naive in so many ways, and now at least somewhat less so. I owe this development almost entirely to the incredible people in my lab, who really helped push me to think harder about virtually everything, and to my excellent colleagues here at Penn.
- It’s cheesy, yes, but it’s very satisfying to make a difference in someone’s life. A view from the outside is that this is about reaching students in class. That doesn't work too good for me–I’m not a natural lecturer, and as such, I think my classroom teaching is just OK, despite a fair amount of effort. But I love working with people (graduate students, undergraduates, postdocs) in the lab, and for me, that’s how I feel like I make the most difference. I had one undergrad tell me that working in my lab was his single best experience at Penn. That was so awesome!
- Speaking of connecting with people, this blog has also been one of the most fun things I’ve done since becoming a PI.
- Got a lot to learn about leading a group, but I have learned one thing: personnel isn’t everything, it’s the only thing.
- “Failing to reach a trainee” (i.e., someone flames out of the lab) happens to everyone. PI will be traumatized, trainee too. It sucks. And it has happened to virtually every PI I know. It’s just one of those things people tend not to talk about.
- Don’t give up on people. Or do? One school of thought preaches that people never change. Another school of thought is that there is some nugget of talent inside of everyone that is waiting to be nurtured. The truth is somewhere in between. I have now seen people who just can’t seem to figure it out no matter how much time gets put into them. I’ve seen others who seemed hopeless at first transform so utterly that it’s like talking with a different person by the end of their PhD. Personnel: completely maddening!
- For some aspects of running a group, there are clearly some right and wrong things you can do. But I feel like I've seen as many different paths to success as to failure. If you get conflicting advice, it probably means nobody really knows, so just trust your gut.
- Some people are out there to take advantage of you. Some people really want to help. Seek out the latter. Avoid the former. But you will encounter the former, so don’t let worrying or fuming about them take over your life because it will destroy you.
- Lots of stuff is broken. The temperature is off in your scope room. The bulb is out in the bathroom. The website for submitting grants was designed by masochists intent on making you cry up until the grant deadline. Some engineering undergraduates with good AP calc scores apparently don’t know what a derivative is. You can’t fix it all. Choose your battles.
Oh yeah, and one big thing I learned: setting up a lab is HARD WORK. One of the beautiful things about being young is thinking that you'll do it better yourself once you get the chance. Maybe. But I’ve developed a deep respect for anyone who has managed to set up a functioning, productive lab. Cheers.
- I realized that as a group, scientists (meaning grad students, postdocs, PIs and all the other folks that make a lab go) are pretty lucky. They are by and large smart, talented, driven people who could succeed in many different walks of life. They happen to do academic science, but can probably do many other things successfully. It would be okay to do so. Also, staying in science is a privilege, not a right, one handed out with a lot more care than many people think.
- I stopped worrying as much about my career. Like, I need this paper to get this grant to get this job to get this… whatever. Partly, I’m just too tired and busy to do so. Partly, though, it’s also because I have realized just how lucky I am to do something I love, which I think is very rare in this world, especially for something as generally useless to the world at large as science. Not to say that I don’t want to get papers or grants or tenure or anything like that, nor is it something that I never think about, but just saying that the day to day makes me happy, for the most part.
- Life is long and can take scientists in many different directions. Academics have curious minds and will always be searching for new challenges, and doing what I'm doing now is just one of those challenges.
- I really want to try to do something important. I’ve now been in science just long enough now to have seen a few scientific fads come and go, and while I’m not much of a scholar of science history, I think that experience has helped me gain a somewhat better perspective on when we really learn something about the world. I also realize that I will probably fail to do something important, because it’s just really hard to do so. But I hope to have fun trying.
- Related to this last point: it’s hard to predict where your science will take you, whether it will lead to something important or not, either in your time or the next. But the quality of how you execute your science and the conclusions you draw is the one thing you can enforce. And in a way, it’s the only thing that matters.
- I learned to not dismiss crazy ideas, and allow flexibility to let them grow. Starting out, I thought that I was going to run this super tight ship, with every project subjected to rigorous risk/reward analysis. I still think that’s actually not a bad thing and that most people don’t do enough of that, but sometimes its good to just let things go. Some of the best things going in the lab come from projects that I didn’t think had much future at the time.
- It is hard to change fields. Once you’re going in a certain direction, it’s what everyone expects of you: your trainees, your colleagues, yourself. On top of this personal inertia, the system is also set up to prevent you from changing fields, because you rely on your social network for papers, grants, etc. Your only hope is to develop enough clout that people outside your field might give you the benefit of the doubt. Or to just be such a small fry that nobody really cares.
- The colleagues I admire most are the ones who don’t take things too seriously, especially themselves.
- I’ve learned a lot about how to do science over the last few years, and I’m a much better scientist for it. How do you frame a problem? What can you really claim based on this data? What are alternatives? Looking back at myself coming into this job, I feel like I was hopelessly naive in so many ways, and now at least somewhat less so. I owe this development almost entirely to the incredible people in my lab, who really helped push me to think harder about virtually everything, and to my excellent colleagues here at Penn.
- It’s cheesy, yes, but it’s very satisfying to make a difference in someone’s life. A view from the outside is that this is about reaching students in class. That doesn't work too good for me–I’m not a natural lecturer, and as such, I think my classroom teaching is just OK, despite a fair amount of effort. But I love working with people (graduate students, undergraduates, postdocs) in the lab, and for me, that’s how I feel like I make the most difference. I had one undergrad tell me that working in my lab was his single best experience at Penn. That was so awesome!
- Speaking of connecting with people, this blog has also been one of the most fun things I’ve done since becoming a PI.
- Got a lot to learn about leading a group, but I have learned one thing: personnel isn’t everything, it’s the only thing.
- “Failing to reach a trainee” (i.e., someone flames out of the lab) happens to everyone. PI will be traumatized, trainee too. It sucks. And it has happened to virtually every PI I know. It’s just one of those things people tend not to talk about.
- Don’t give up on people. Or do? One school of thought preaches that people never change. Another school of thought is that there is some nugget of talent inside of everyone that is waiting to be nurtured. The truth is somewhere in between. I have now seen people who just can’t seem to figure it out no matter how much time gets put into them. I’ve seen others who seemed hopeless at first transform so utterly that it’s like talking with a different person by the end of their PhD. Personnel: completely maddening!
- For some aspects of running a group, there are clearly some right and wrong things you can do. But I feel like I've seen as many different paths to success as to failure. If you get conflicting advice, it probably means nobody really knows, so just trust your gut.
- Some people are out there to take advantage of you. Some people really want to help. Seek out the latter. Avoid the former. But you will encounter the former, so don’t let worrying or fuming about them take over your life because it will destroy you.
- Lots of stuff is broken. The temperature is off in your scope room. The bulb is out in the bathroom. The website for submitting grants was designed by masochists intent on making you cry up until the grant deadline. Some engineering undergraduates with good AP calc scores apparently don’t know what a derivative is. You can’t fix it all. Choose your battles.
Oh yeah, and one big thing I learned: setting up a lab is HARD WORK. One of the beautiful things about being young is thinking that you'll do it better yourself once you get the chance. Maybe. But I’ve developed a deep respect for anyone who has managed to set up a functioning, productive lab. Cheers.
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