“I think I became bilingual”

Valentina Boeva, when did your interest in bioinformatics start, and what was the path that led you to ETH Zurich?
I have been interested in the application of mathematics and computer science to biology, which is now called bioinformatics, since high school. Since bioinformatics didn’t exist as a research field in Russia at that time, I joined the Department of Mechanics and Mathematics at Moscow State University to study mathematics, thinking I could later apply this to biology. After I graduated in mathematics, I started my doctoral studies at the same university at the newly created department of Bioengineering and Bioinformatics. Then, I went to the École Polytechnique in Paris for a postdoc, and this is where I started to work in cancer research, the main direction of my research today. Shortly after I started my postdoc, machines to sequence the human DNA and assemble genomes started to become available. I was super excited by these new technologies and joined the external page Curie Institute to work on the analysis of sequencing data for cancer genomes. This is where we developed the first computational methods for analysing variants in cancer genomes, and it is also when I started developing the first methods to analyse cancer epigenomes, also from sequencing data.

By then, I had become so interested in cancer epigenetics (a language of signposts on DNA that regulates how genes are expressed) that I wanted to have my own team driving this direction. In 2016, I opened my lab, called “Computational Epigenetics of Cancer”, at the external page Cochin Institute. My institute was mostly focused on biology, with many clinicians and biologists working there. We were the only computational team and I sometimes felt quite isolated. It was challenging to recruit students with strong mathematical and computer science backgrounds. I started looking for other positions, and when I saw an opening at ETH Zurich, I applied. Fortunately, I was selected. In 2019, I joined the Department of Computer Science as an Assistant Professor to continue working on cancer research. Our main interests are computational methods that we can develop and apply to better understand cancer biology and suggest better treatment strategies for cancer patients.

Did you also study biology or learn on the go?
I learned on the go. It happened that I have never taken a class in biology, but I consider myself a biologist and I can speak the same language as biologists and clinicians. It all comes from reading articles, talking to biologists, and attending conferences. When I first joined the Curie Institute for my second postdoc, I was affiliated with two labs: the bioinformatics lab and a biology lab working on pediatric cancers. I was attending their lab meetings every week, and in the beginning, it was like a foreign language to me. All these gels and images of cells: I couldn’t follow it! But then after a year of attending these meetings, I realised that I understood everything they said. I think I became bilingual.

What was most helpful to you in getting this position?
Practicing doing interviews. When I realised that I didn’t want to stay in Paris, I started interviewing for other positions abroad, particularly in New York. I had several interviews at different universities there, and every time I went to an interview or gave a “job talk”, I think it got better. When I arrived at ETH for the interview, I was rather prepared.  My research plan was ready, my talks were rehearsed, and I knew what kind of questions people can ask at the interview. I think that had it been my first interview, it would have been more difficult to get an offer.

When you were offered the position, what decided you to choose ETH?
ETH Zurich is very well placed in many ranking amongst European institutions. But mostly it was that the students are very strong, and the education in computer science and mathematics is amazingly good here. I wanted to have students in my team with a mind for analytical reasoning, who could learn biology but who also had a background in mathematics and knew how to program. That was the main reason I chose ETH. And, luckily, it happened to be in Zurich, which is one of the best cities to live in, in my opinion. I love nature and the lake where you can swim in summer. I like winter sports and hiking. I also love that it is very international.

When you joined, did you receive support from ETH to get settled in?
Yes, I was really amazed by how much support ETH Zurich provides. I received support for moving, learning German, getting all the equipment I needed to work… The administrative support is also fantastic, and the Office for Faculty Affairs was very helpful in trying to solve the “two-body” problem. One of the most amazing things was providing very good daycare for my child when he was born. It allowed me to start working full-time straight after my maternity leave.

You joined ETH Zurich as a Tenure Track Assistant Professor: can you tell us a bit more about the evaluation process?
We have three evaluations. The first one, after 18 months, is to make sure that you are on track. The second one happens after three years and is more serious. The final one happens after five years. After every evaluation, the department provides you with very detailed feedback, including what they like and what they don’t, and you are advised to address it for your next evaluation.

We also have two mentors, who are very helpful. I can ask the first mentor, a professor from the same field, questions about difficult situations: for instance when there is a problem with a student or to seek any type of career-related advice. The second mentor works on a different topic and helps you see how your career looks in the eyes of somebody who is not from your exact field. This is very useful, especially during the evaluation period: for example, I could ask if my research proposal was good or detailed enough for somebody who is not from my field. They provided me with feedback and helped me see where I should be more technical or instead provide some bigger vision.

How is it going for you so far?
My evaluations have gone well so far. There were some points to be addressed, which I did. And now the focus is on publishing our recent results. I think the publication process is a difficult one. But we have a lot of work that is ready to be submitted this year, and we hope it will be out soon.

You also teach in several courses. Do you like teaching?
It takes a lot of time, but I really like it. In Spring Semester 2022, we started offering a new class called Machine Learning for Genomics, created from scratch. This class is specifically designed for computer science students who don’t know any biology but wish to know how machine learning can be used to create a breakthrough when analyzing our DNA. I don’t teach them the basics of machine learning as they have learned them already; instead, I focus on more sophisticated AI methods used in genomics and explain the biology that students need to know.

Last year, we had 40 students. Today, we already have about 120 registrations, showing the class is getting popular! According to the feedback we received from students last year, many said that they became more interested in the topic after they took the class than they were before. Many students who took the class later applied to do projects in our team, so it is working really well.

In France, you interacted a lot with biologists. Do you have a lot of collaborations with cancer researchers or clinicians in Zurich?
Yes, I am trying to establish new connections here with several groups. We have an SNF Sinergia Grant with external page Isabelle Opitz’s group working on mesothelioma at the University Hospital Zurich (USZ), as well as ongoing collaborations with other groups at USZ and the University of Zurich.

You said you were “bilingual” yourself. How do your students coming from computer sciences manage the multidisciplinary work?
Some students come from the Computational Biology & Bioinformatics Master’s programme. They have very good training, both in statistics and biology. I also have students who come from purely statistical, mathematical or computer sciences, but they learn things quite fast. We have a wide range of projects in our team that can fit students with different backgrounds. By seeing the large spectrum of projects that others are working on, new students get a full picture of the different types of biological questions one can ask.