MSSE Feature: Silvia Crivelli

An Interview with Dr. Silvia Crivelli, Founding Executive Director of MSSE

“Reach out, ask lots of questions, have an open mind, imagine whether this is what you want to be, and then go for it! It may be scary, but if you enjoy it, it will be all worth it in the end.”

Silvia Crivelli is the Former and Founding Executive Director of MSSE (Master of Molecular Science and Software Engineering) at UC Berkeley. Here, she shares about her path to becoming a computational biologist, her passion for mentoring, and her vision for future and prospective MSSE students. Note: Silvia is no longer our Executive Director, but has returned to work on her research as of early 2022. 

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Tell us about yourself

I am Silvia Crivelli and I am the Executive Director of the MSSE program at UC Berkeley. I completed my undergraduate degree in applied mathematics, my PhD in computer science, then did my postdoc research in computational biology at UC Berkeley under Teresa Head-Gordon, who is now the faculty director of MSSE. 

Through my academic career, I was, in a way, training to become a computational scientist before the concept even existed. It was really cool. There were a lot of advantages to understanding the math, computer science, and science, and it gave me the opportunity to talk to different people in all of these fields comfortably — it was a huge asset!

What made you dive deeper into this intersection of computation and scientific research?

When I was a postdoctoral researcher under Teresa Head-Gordon, we talked many times about software engineering, especially how it was lacking in academia. Researchers and scientists were just not taught about software engineering! Software engineering is important because software is needed in every industry and government lab. Software development typically involves a group of developers and requires a lot of time, effort, and money. In MSSE, we focus on software engineering for scientific computing which covers computer architectures and software features that have the greatest impact on performance. Performance is very important to model and simulate large systems or to deal with big data sets. 

Software engineering is also needed in almost any research field, as we need to develop code to be used by others. We as researchers are not trained for that, and we’re not trained to understand what’s happening at the computer level — what the computer’s doing and how to get the most performance out of it. That’s all something we typically learn on the job, but that shouldn’t be the case — we should be trained for that in college.

As Teresa and I had this discussion over and over, I went on to become a scientist at the Lawrence Berkeley National Lab (LBNL). I worked in computational biology, and I ended up hiring students and postdocs to work for me. There, I noticed that many were not prepared. My husband also worked in industry at the time, so we were both concerned about what skills and tools students were lacking to work as software engineers, or at least to be comfortable with those skills. 

Then, I decided to start mentoring students. I became really involved with mentoring, particularly helping students from underrepresented groups. It’s a passion of mine to this day. Being underrepresented [in STEM] myself, I think it’s important that these students have the support, role models, and guidance to succeed.

In 2019, Teresa came to me and she told me about this new program she needed help with. And at the time, there were three things very important to me: the topic of computational science, the topic of teaching software engineering skills, and the idea of having a program that could bring the opportunity to train underrepresented groups in the field. I got excited about that opportunity and that’s how I came to Berkeley to work on the [MSSE] program. I still work at the Lawrence Berkeley Lab; I’m still a scientist — I could never not be a scientist — but I’m trying to do both.

What inspired you to further pursue mentorship in your field?

I am a member of the Advanced Scientific Computing Advisory Committee (ASCAC) that provides guidance to the Office of Science, under the US Department of Energy. Around 2011, one topic they wanted us to assess was the topic of workforce development. We actually looked at a lot of documents at the time and found several things that were interesting and made me get involved in the topics of diversity and inclusion in the STEM fields.

Back in 2011, we saw that in 2021, there was going to be an increased need for a workforce trained in computational science, and it was projected that we weren’t going to have enough trained people to work in this field. Even if we counted international students, we still wouldn’t have enough people. What we really needed to do based on that information was to support our underrepresented students — there’s plenty of them, but they are not well supported. It’s important not only because of the numbers but also because we need to bring in people with different perspectives. These different experiences are important in science especially if you have to tackle difficult problems like cancer or climate change; we need new ideas and creativity and that comes from people with different viewpoints. That research made it all of that very clear.

Also, our discussion occurred at a time where companies were releasing their diversity demographics, even the government labs, and those demographics were terrible! We could see that women made up around 10-17% of the workforce, Hispanics were 8%, Blacks were 4%, and that was all over — from private industry to government labs. The question then was, how can we move the needle and make a change? Instead of waiting, I decided to do something myself. 

I said, “Okay, I have this opportunity to bring students and faculty from diverse groups to the lab during the summer for internships.” I wanted to take as many [students] as I could during the summer and see what would happen. At the time, there was no space for a large group at the lab, but a colleague of mine allowed me to use the 4th floor of Donner lab on campus. I had around 15 mostly underrepresented students and faculty that came from “unusual” places—community colleges, small liberal arts colleges, and other small colleges — and were funded by the Department of Energy through the Workforce and Education office at LBNL. It was a fantastic experience that I have repeated every summer since.

Inspired by this idea, we implemented a diversity program that repeated this experience but instead of just me we have several LBNL researchers each hosting a group of visiting faculty and students. Before COVID-19, we had a record number of students coming to the lab to the point we didn’t even have proper seating capacity! This summer internship program was very successful not just because it gave these students a new opportunity to get involved in research and get excited about computational science, but also because the researchers at the lab (mostly white males) were able to change their perception about students most of whom come from underrepresented backgrounds and the usually small, less-known colleges they came from. Obviously, students from Berkeley are awesome, but we wanted to let everyone know that students from other schools can, too! This diversity program, called Sustainable Research Pathways (SRP) program, was just awarded the Workforce Diversity & Inclusion Leadership Award by HPCwire.

Now moving to your experience with MSSE, what are the benefits of a fully online program?

One important aspect of MSSE is that it gives the opportunity to people who can’t physically come to Berkeley to think about – say — pursuing a Master’s degree at UC Berkeley, all while still taking care of family or working a full-time job. We also give them the opportunity to take courses part-time and that helps alleviate whatever situation a student may be in. 

We wanted to offer a program that although online was still very high-touch. Our faculty-student ratio is smaller than the 16:1 ratio recommended in the US. We have means of delivering lectures asynchronously, but we have synchronous discussions for every class. That means the students have the opportunity to speak to the professors, not just the GSIs. They have the opportunity to discuss with an expert in the field, and that’s very valuable. That combination of synchronous and asynchronous learning gives students a great deal of flexibility without sacrificing the opportunity to interact with their professors and their peers. There’re also projects where students work with partners, and there’s a lot of collaborative interaction happening, so I want everyone to keep that in mind. It’s not a lonely path in MSSE! There’re lots of discussions with representatives from companies and faculty that make it very interactive, even if you are taking courses from home or wherever you are. 

I’d also like to add that we’re planning to offer an on-campus option for those students who want to come to Berkeley.

Also, outside of the classroom, we offer opportunities for students to have internships. I’m a big fan of them, so I try to help students based on their career interests. 

As the executive director of MSSE, where do you hope to see the program in five years?

First of all, I hope we can succeed since we’re in the early stages of the program. I hope that we have a strong MSSE community that includes successful alumni and that we can strengthen and expand our partnerships with the industry and government labs. I hope to be able to offer more financial help for our URM students. I hope to expand our curriculum and offer tracks that allow students to go deeper in some of the main areas of our program such as software engineering, high-performance computing, and deep learning.

Any advice for future and prospective students that may not have considered programs like this before?

If you are a computer scientist who is interested in applying what you learned in school to the solution of a scientific problem, this is the perfect opportunity. That was my problem, actually, as a computer science student — I knew all these methods and tools from school, but how could I use them towards something relevant? That’s why I got so excited about computational biology.

If you’re a chemistry or biology student who is interested in learning more about computer science, there are so many exciting areas happening in the field: data science, machine learning, and deep learning. This is a great opportunity for you as well. 

If you’re ready to take the risk and work outside your comfort zone and learn something new, grow professionally, and make a significant contribution to the community, then you should definitely consider MSSE. 

If you’re not sure, still reach out to us! Sometimes, by talking to someone with experience, you might be able to figure it out. Don’t say “no” until you have all the information you need to make an informed decision. For instance, I had a student that thought they wanted to be an accountant, but now they’re doing computer science and graphics — nothing to do with their original interest!

It’s interesting because we sometimes think we want to be something just because we didn’t see all the possible opportunities out there. Reach out, ask lots of questions, have an open mind, imagine whether this is what you want to be, and then go for it! It may be scary, but if you enjoy it, it will be all worth it in the end.


Learn more about the MSSE program and connect with Silvia.