Traditionally, jumping from a long-term industry job back to academia—or vice versa—has been difficult for scientists, but that may be changing. Just last week, for the first time, an industry scientist was awarded a Royal Society Research Professorship. Microsoft Research’s Luca Cardelli will continue in his industry position while spending 20% of his time at the University of Oxford. "Dr Cardelli’s new dual affiliation is a perfect example of the sort of flexibility we need to show towards scientists who wish to work at the interface of academic and industry science," the Royal Society’s president, Paul Nurse stated in the news announcement.
Cardelli has worked at Microsoft Research Cambridge since 1997; today he is a principal researcher in the Programming Principles and Tools group. Originally from Italy, Cardelli obtained a Ph.D. in computer science from the University of Edinburgh in 1982. He spent 3-plus years at AT&T Bell Labs, working as a member of the technical staff. That was followed by 12 years as a researcher at the former Digital Equipment Corporation Systems Research Center. While his foundation is in programming languages and distributed systems, Cardelli has lately been applying his skills and knowledge to systems biology and molecular programing. Science Careers talked to Cardelli about how his career came to straddle the divides between computer science and biology and, now, between industry and academia.
"I’ve been lucky enough to be in industrial research labs that were always very forward looking and open."—Luca Cardelli
Interview excerpts have been edited for brevity and clarity.
Q: What made you decide to take an industry job right after your Ph.D.?
L.C.: Well, that was Bell Labs, in the days when it was the main industrial research lab in the United States and probably the world, and so it was a very interesting opportunity. It was industrial, but it was basically pure research.
I also looked into academic jobs, but I didn’t find anything really interesting to me. It takes a long time to get established in academia, and you have to do a lot of administration, so I thought that having a job in research without any kinds of administration duties would be a good idea.
In the second year at Bell Labs, I took a position as an adjunct professor at the University of Pennsylvania, so I was there for 6 months teaching a course. I love giving lectures, but I hated preparing them, so I was more comfortable in an industrial research environment.
Q: You now work at the interface of computer science and biology. What got you into biology?
L.C.: I’ve always been interested in biology, but I never thought that I would do it professionally. I got involved accidentally, in the early 2000s. At that point I was studying the notion of computation in environments that contain computing agents and the notion of compartments and nesting. I was approached by an old colleague, Ehud Shapiro from the Weizmann Institute of Science, to work on biological modeling in cellular environments. Cells have lots of compartments, which are nested and also contain computational agents like proteins, and so on. Shapiro got me into supervising a biology Ph.D. student named Aviv Regev, who was working on these topics, and that’s when I started reading biology textbooks and all that. Then I really got hooked, and so I started working increasingly on biological modeling. I started a number of projects with collaborators, typically in biology, trying to apply techniques of computer science to biological problems.
Q: What projects are you currently pursuing?
L.C.: I have basically two main, current projects. One is more on the systems biology side, trying to reverse-engineer natural mechanisms to try to understand how they work, and this is about the cell cycle. One of the interesting things is that the basic biological network of genes and proteins that implement the cell cycle is the same in all eukaryotes. From yeast to us, they use different proteins, different components, but the connectivity between these components is always the same. So I’ve been working on modeling that from the computational point of view, asking questions like: How do they talk to each other? How do they work fast, and how do they compute what needs to be done?
The second project is more on the engineering side of things, and it is in DNA computing. The idea is that, if you want to build something at the nanoscale, the best way is to set up a kind of programmatic control over how matter arranges itself. The best way we know to do it is to use DNA, primarily because it is a sequence of letters and you can program the letters, and secondly because there is an industrial technology which allows you to read and write DNA. And so you can see DNA as a construction material, and you can program matter at the molecular scale through DNA.
Q: Just as one does not readily associate Microsoft with biology, one does not readily associate industrial research with scientific freedom. How free have you been to pursue your interests?
L.C.: I guess it depends on where you are. I’ve been lucky enough to be in industrial research labs that were always very forward looking and open. Places like these are quite unique. The best example was Bell Labs, which was this open research environment where everybody was basically setting their own agenda, of course in the context of computation and telecommunications. The same is happening now at Microsoft Research. So you’re working in an environment where, yes, it is industrial, and there is certainly an expectation to produce industrial applications at the end. But also we’re working in an environment which is very well connected to academia, and the expectation is that you are looking for new things that may affect the industrial landscape or the academic landscape and try to explore them. So our output is either publications or internal technology transfer.
In the case of biology, this is a bit different for a company like Microsoft that doesn’t have a clear agenda to do biology, but, even so, computer science is branching out in many other sciences, and so from an industrial point of view it is our job to figure out how to help people working in scientific areas use computational tools. So this is now becoming our agenda as well, and, in fact, we do have a program here at Microsoft Research for scientific computing, so we’re involved in biology and also in ecology because, in order to understand what is needed, we actually need to try it out and do it ourselves.
Q: Did you ever have to make a case for pursuing biology?
L.C.: Well, this started as a hobby, and then one day one of my vice-presidents said, "Oh, I didn’t know you were publishing in that area." But fortunately, he looked around the company and found other people who have the same hobby, so at the time we had a kind of grassroots group working in bioinformatics. So now it’s a combination of a grassroots movement from scientists and a new strategic direction being set from the top.
Q: In what ways do you expect academia to be different from your industry experience?
L.C.: We have a lot of students here at Microsoft Research, but they all come for 3–month periods. So the difference is that in academia there will also be students on a long-term project. Another difference is funding. Here at Microsoft we do not have to apply for funding; we decide what we want to do, and if we’re good then there is no problem. In academia, I will have to go through the standard funding application process. In the past I’ve applied as a supporting participant in various European research projects from academia, but I’ve never done it as a principal investigator so that is going to be a new experience.
The Royal Society sponsorship initially is for one Ph.D. student, and of course we have many programs inside Microsoft like Ph.D. sponsorships and postdoc sponsorships, so probably I will be able to take advantage of some of these programs, but they have their own review process so it’s not automatic.
Q: In what ways do you expect the two worlds to complement each other?
L.C.: As I kept working more and more toward biology, it became more and more critical to be working with collaborators. In academia, it’s easier to find people to work with in areas where you don’t know as much as in your own traditional areas. So, for 20 percent of the time I will be in Oxford with students, and for the other 80 percent I’ll still be here with my free research time without any obligations, so hopefully I will combine the best of both worlds.
Q: How can young scientists decide whether academia or industry is the right fit?
L.C. They have to decide based on this trade-off between having this great resource of long-term students that work with you but essentially becoming a research manager, or working in a research group with a lot of peers as opposed to students. It depends on the personality of people, what they want to do, and how they want to do it.
Q: Any advice for young scientists interested in working at the crossroads of biology and computer science?
L.C. Interdisciplinary programs exist in a number of places, in bioinformatics and bioengineering, that try to educate people in biology and computing, so try to find them. Also, attend conferences and postgraduate workshops if they can find any, and in general look for advisers who work in this area.