Tag: academia


Every professor is a startup

There has been a lot of discussion lately about whether to be in academia or industry. Some of it I think is a bit unfair to academia. Then I saw this post on Quora asking what Hilary Mason’s contributions were to machine learning, like she hadn’t done anything. It struck me as a bit of academia hating on industry*. I don’t see why one has to be better/worse than the other, as Roger points out, there is no perfect job and it just depends on what you want to do. 

One thing that I think gets lost in all of this are the similarities between being an academic researcher and running a small startup. To be a successful professor at a research institution, you have to create a product (papers/software), network (sit on editorial boards/review panels), raise funds (by writing grants), advertise (by giving talks/presentations), identify and recruit talent (students and postdocs), manage people and personalities (students,postdocs, collaborators) and scale (you start as just yourself, and eventually grow to a group with lots of people). 

The goals are somewhat different. In a startup company, your goal is ultimately to become a profitable business. In academia, the goal is to create an enterprise that produces scientific knowledge. But in either enterprise it takes a huge amount of entrepreneurial spirit, passion, and hustle. It just depends on how you are spending your hustle. 

*Sidenote: One reason I think she is so famous is that she helps people, even people that can’t necessarily do anything for her. One time I wrote her out of the blue to see if we could get some Bitly data to analyze for a class. She cheerfully helped us get it, even though the immediate payout for her was not obvious. But I tell you what, when people ask me about her, I’ll tell them she is awesome. 


Why all #academics should have professional @twitter accounts

I started my professional Twitter account @leekgroup about a year and half ago at the suggestion of a colleague of mine, John Storey (@storeylab). I started using the account to post updates on papers/software my group was publishing. Basically, everything I used to report on my webpage as “News”. 

I started to give talks where the title slide included my Twitter name, rather than my webpage. It frequently drew the biggest laugh in the talk, and I would get comments like, “Do you really think people care what you are thinking every moment of every day?” That is what some people use Twitter for, and no I’m not really interested in making those kind of updates. 

So I started describing why I think Twitter is useful for academics at the beginning of talks:

  1. You can integrate it directly into your website (like so), using Twitter widgets. If you have a Twitter account you just go here, get the widget for your website, and add the code to your homepage. Now you don’t have to edit HTML to make news updates, you just login to Twitter and type the update in the box.
  2. You can quickly gain a much broader audience for your software/papers. In the past, I had to rely on people actually coming to my website to find my papers or seeing them in journals. Now, when I announce a paper, my followers see it and if they like it, they pass it on to their followers, etc. I have noticed that my papers are being downloaded more and by a broader audience since I joined. 
  3. I can keep up on what other people are doing. Many statisticians have Twitter accounts that they use professionally. I follow many of them and when they publish new papers, I see them pop up, rather than having to go to all their websites. It’s like an RSS feed of papers from people I want to follow. 
  4. You can connect with people outside academia. Particularly in my area, I’d like the statistical tools I’m developing to be used by folks in industry who work on genomics. It’s hard to get the word out about my methods through traditional channels, but a lot of those folks are on Twitter. 

The best part is, there is an amplification effect to this medium. So as more and more academics join and follow each other, it is easier and easier for us all to keep up with what is happening in the field. If you are intimidated by using any social media, you can get started with some really easy how-to’s like this one.

Alright, enough advertising for Twitter, I’m going back to work. 


Finding good collaborators

The job of the statistician is almost entirely about collaboration. Sure, there’s theoretical work that we can do by ourselves, but most of the impact that we have on science comes from our work with scientists in other fields. Collaboration is also what makes the field of statistics so much fun.

So one question I get a lot from people is “how do you find good collaborations”? Or, put another way, how do you find good collaborators? It turns out this distinction is more important than it might seem.

My approach to developing collaborations has evolved over time and I consider myself fairly lucky to have developed a few very productive and very enjoyable collaborations. These days my strategy for finding good collaborations is to look for good collaborators. I personally find it important to work with people that I like as well as respect as scientists, because a good collaboration is going to involve a lot of personal interaction. A place like Johns Hopkins has no shortage of very intelligent and very productive researchers that are doing interesting things, but that doesn’t mean you want to work with all of them.

Here’s what I’ve been telling people lately about finding collaborations, which is a mish-mash of a lot of advice I’ve gotten over the years.

  1. Find people you can work with. I sometimes see situations where a statistician will want to work with someone because he/she is working on an important problem. Of course, you want to be working on a problem that interests you, but it’s only partly about the specific project. It’s very much about the person. If you can’t develop a strong working relationship with a collaborator, both sides will suffer. If you don’t feel comfortable asking (stupid) questions, pointing out problems, or making suggestions, then chances are the science won’t be as good as it could be. 
  2. It’s going to take some time. I sometimes half-jokingly tell people that good collaborations are what you’re left with after getting rid of all your bad ones. Part of the reasoning here is that you actually may not know what kinds of people you are most comfortable working with. So it takes time and a series of interactions to learn these things about yourself and to see what works and doesn’t work. Of course, you can’t take forever, particularly in academic settings where the tenure clock might be ticking, but you also can’t rush things either. One rule I heard once was that a collaboration is worth doing if it will likely end up with a published paper. That’s a decent rule of thumb, but see my next comment.
  3. It’s going to take some time. Developing good collaborations will usually take some time, even if you’ve found the right person. You might need to learn the science, get up to speed on the latest methods/techniques, learn the jargon, etc. So it might be a while before you can start having intelligent conversations about the subject matter. Then it takes time to understand how the key scientific questions translate to statistical problems. Then it takes time to figure out how to develop new methods to address these statistical problems. So a good collaboration is a serious long-term investment which has some risk of not working out.  There may not be a lot of papers initially, but the idea is to make the early investment so that truly excellent papers can be published later.
  4. Work with people who are getting things done. Nothing is more frustrating than collaborating on a project with someone who isn’t that interested in bringing it to a close (i.e. a published paper, completed software package). Sometimes there isn’t a strong incentive for the collaborator to finish (i.e she/he is already tenured) and other times things just fall by the wayside. So finding a collaborator who is continuously getting things done is key. One way to determine this is to check out their CV. Is there a steady stream of productivity? Papers in good journals? Software used by lots of other people? Grants? Web site that’s not in total disrepair?
  5. You’re not like everyone else. One thing that surprised me was discovering that just because someone you know works well with a specific person doesn’t mean that you will work well with that person. This sounds obvious in retrospect, but there were a few situations where a collaborator was recommended to me by a source that I trusted completely, and yet the collaboration didn’t work out. The bottom line is to trust your mentors and friends, but realize that differences in personality and scientific interests may determine a different set of collaborators with whom you work well.

These are just a few of my thoughts on finding good collaborators. I’d be interested in hearing others’ thoughts and experiences along these lines.

Related Posts: Rafa on authorship conventions, finish and publish