Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, recently met with Dr. Mercè Crosas, Head of the Computational Social Sciences Program at the Barcelona Supercomputing Center. Dr. Crosas has had a substantial and extensive professional life, having worked in leadership roles in academia, industry, and government. Her previous positions include director of software development in the educational software industry, director of Information Technology in biotechnologies startups, University Research Data Management Officer with Harvard University Information Technology (HUIT), Chief Data Science and Technology Officer at Harvard’s Institute for Quantitative Social Science (IQSS), and Secretary of Open Government for the government of Catalunya in Spain
In their wide-ranging conversation, Dr. Crosas and Dr. Meng discussed the most enjoyable aspects of working in each of the three sectors, the key differences and similarities in the way situations are managed in a leadership role for each sector, how Dr. Crosas’s PhD training in astrophysics helped her take on these different research and leadership positions, and the importance of balancing a rich professional and personal life. The pair also brainstormed the concept of a 15-year multi-sector career program—a bold strategy for cultivating future societal leaders by having individuals sequentially immerse themselves in academia, industry, and government, each for a span of 5 years. This unparalleled journey aspires to harness the combined insights from these diverse sectors, empowering leaders to adopt a comprehensive, empathetic, and adaptable approach in their problem-solving, embodied in Dr. Crosas’s remarkable career.
HDSR includes both an audio recording and written transcript of the interview below. The transcript that appears below has been edited for purposes of grammar and clarity with approval from all contributors.
Audio recording of the interview.
Xiao-Li Meng: [00:00:00] Now, what is it like to have such a rich professional life, since a very few of us have such opportunities? What do you enjoy most in each of these types of leadership?
Mercè Crosas: [00:00:11] Thank you, Xiao-Li. First of all, thank you for having me here. It is always an honor and a pleasure talking to you. Yes, well, I do feel very fortunate to have had this breadth of experience and to have been in these multiple sectors. I think one of the main learnings is that having been inside each sector, you cannot solely identify yourself with just one. It makes you break some stereotypes. It makes you understand that each one of them is hard in its own way, and each one can be very rewarding.
[00:00:47] For example, sometimes in academia we're saying, 'Industry is evil. Nobody does science in industry.' But it depends what type of business you are in; you might be doing very interesting research in applied science, that is very relevant to society. At the same time, when we are outside government, usually we say 'Oh, government does nothing. Nobody is doing anything. But when you are inside, you realize how hard it is to do things, and actually people work very hard inside. And then sometimes also from outside academia, people say, 'Well, they're just doing nothing relevant. It's all abstract. Nothing that applies to society.' And that's not true either for many of us that have been in academia for many years.
[00:01:36] I think that in a similar way, when you live in different cultures or visit different countries, you develop an empathy [for other cultures]. It helps you understand those perspectives and break some of the stereotypes you had from outside. Having been in those sectors [academia, government, industry], it helped me with that, and I can now see from each side what the difficulties are. But you asked me what I have enjoyed from each one—this is going to be a data point. One data point, right? So don't generalize it too much, it is based on my experience. From industry, I did enjoy the level of focus on one project and doing that [the same project] until the end. You usually have a well-defined goal or project you need to do, and you can focus on that project and have fewer distractions. Even if it's not always true, you can often go home and not be still thinking about the project, like it is the case in academia. For me, the best thing in academia, or my biggest pleasure of being in academia, has been having that excuse to learn constantly. I mean, in every sector you should be learning all the time, but in academia, it's essential to what we do, right?
Xiao-Li Meng: [00:02:52] Yes.
Mercè Crosas: [00:02:52] And having that as an excuse in your profession, it helps you grow constantly. I think that's very important. But when I compare it to industry, it's harder to be focused on one thing only. You have a lot of distractions there [in academia] and you'd never go home thinking, 'I'm done now'—it's constant. But then in government, what I have enjoyed most about government, I think, is the sense of public service, of being able to do something that directly impacts a community or would help improve society, even though it's often hard to do that. But the concept that there is a type of administration, a type of organization that is there to serve the public, I think this is beautiful. So, each one of them comes with its problems, but what I have learned from going through these sectors is that you can find pleasure in each one of them in a different way.
Xiao-Li Meng: [00:03:57] You just reminded me—this is a while ago—that there were these newspaper articles that were complaining about 'What does a professor do? They only teach 6 hours a week.' When you're not in that space, it's easy to only see the superficial things.
Mercè Crosas: [00:04:14] Yes.
Xiao-Li Meng: [00:04:15] So I think one thing you mentioned, which is very important, is that I think that these kinds of experiences really help you to put yourself in other people's shoes, right?
Mercè Crosas: [00:04:23] Exactly.
Xiao-Li Meng: [00:04:23] Before you criticize, before you complain, you think about how hard each job is.
Mercè Crosas: [00:04:27] Yeah. I think it's very easy to simplify other people's life when you don't know it, and also criticize it more easily, right?
Xiao-Li Meng: [00:04:36] Right. Once you are in those kinds of positions, you really develop a sympathy for how hard everybody tries. I think that's actually incredibly important. This is the kind of thing that can unite people if we truly understand how much effort we all have been making.
[00:04:53] I want to ask you that in all these three sectors, you were in a leadership position, which means you have people to report to you, you need to manage people's relationships in the working environment. Are there are some key differences in terms of the way you managed the situations in a leadership role for these different sectors?
Mercè Crosas: [00:05:12] That is an interesting question because I would say yes and no. Yes, because I think it's almost a spectrum. If you go from government to academia to industry, you go from being more conditioned to less conditioned. Within government, there are many, many factors [that constrain you] about how you're going to spend resources, public resources. Many things condition you not to be easy to just do something you want to do. And I think in academia, it's less. I'm not talking about [doing] individual research, but as a leader shaping academia, it's less. But it's still more than in industry, where you often have more freedom of movement and less constraints. Although it's not fully unconditioned; there are still external pressures. And in industry, you often can allow more risk into your decisions. As a leader, if you are in government, you have to be very careful to lead into something or have a vision that might add a high level of risk, because society would [directly] pay for it. And the consequences are strong. But I think at the end I'd say that it's less different in terms of what leadership should be in each one of them [each sector], because at the end you deal with people. People are people everywhere.
Xiao-Li Meng: [00:06:35] Exactly.
Mercè Crosas: [00:06:35] And human complexity exists in governments, exists in industry, and exists in academia. There are competitions in every way, there are egos in every place. What at the end to me has worked better in leadership is to be collaborative, to break hierarchies, and instead motivate the team, motivate by giving them ownership [of what they contribute to]. And it's important to have a common vision and goal. There is a common guidance and vision, but there is also a common knowledge, a shared knowledge that helps you be on the same page and each one contributes to the same objectives. Having a combination of that, and understanding our cognitive biases, our psychology, our complexity— this aspect of leadership applies, I think, to each sector independent of whether you are in government, industry, or academia.
Xiao-Li Meng: [00:07:35] I see. That's a great point. Now, I have a feeling that most of our listeners probably would not have guessed that given you have done so many things, all your leadership roles, they probably may not have guessed that you were actually trained as a PhD in astrophysics. So my question for you is how did this PhD training help you take on these leadership roles? Do you see or is there anything particular you find that that kind of training is useful for you?
Mercè Crosas: [00:08:05] Yes. It's interesting. I don't sit and think about that very often. But I think that physics and astrophysics are the beauties in science to me, I still find them really beautiful, as scientific fields. They are very rigorous. They have existed for long, applying the scientific method. The verification or validation of your results is something that is very embedded in your training. Well, for example, in cosmology, the detection of the Higgs boson. They will make very sure that they have detected that Higgs boson. And they'll go over and over, and after a few years, repeat it and verify it and allow many to participate, to be very collaborative. Because even though there is obviously competition in physics—in astrophysics too—but it's collaborative in the sense that what you want is to learn more about something, and together you want to learn more, and you want to make sure that others help you verify what you've done too. That they also repeat what you've done so we as a community, or as humanity, understand the universe as well as possible. And I think that in the end it translates to many other things. For example, try to be as objective as possible, rigorous in what you do, to be able to gather all the data necessary and verify [a result or a decision], and understand, how you're going to design a public policy. If you use these elements, I think they can help you to have less biased results, and results that are more about because it works and not just because you like it. And I think physics and astrophysics train you with that. At the same time, I think it's very interesting to go from astrophysics, to understand some of biomedicine, and then to social sciences. I think each one has tremendous value to the whole knowledge. And to me, it's fascinating to have been in all of them in some ways. But I think that each one could learn something from the others, right?
Xiao-Li Meng: [00:10:16] Absolutely.
Mercè Crosas: [00:10:17] Well, I thought that this same rigor and this need to verify the results was something that I wanted to do when I was [close to] the social sciences. I think that was something to me useful to apply from physics to social sciences. But also, I think that physics could learn from social sciences. First of all, they could learn that even their science includes also social sciences, and that they [social sciences] are extremely important. And maybe every time, more and more, in almost everything that we do, we want to solve, from climate change, to understand health, even to understand more about the universe around us—at least at the end, how we're going to make that happen, it involves organizations, it involves people, and it involves societies. And understanding human behavior, political sciences, psychology, sociology, all these, and economics will be part of the answer. It's not only the natural sciences or the physical sciences that will give you the answer.
Xiao-Li Meng: [00:11:25] Absolutely. And I also think that to start from astrophysics probably gave you a grand-scale view, because everything in terms of time—.
Mercè Crosas: [00:11:34] Yes. Yes. You are very little.
Xiao-Li Meng: [00:11:38] —in terms of space, right? You don't want to think with a lack of scale, everything else is so temporary.
Mercè Crosas: [00:11:44] Exactly. Yeah, that's another good perspective of humility that it gives you, right? [Laughing.]
Xiao-Li Meng: [00:11:49] It's probably not coincidence that I see that everything you do has this grand scale—your variety of leadership roles. But you are also a scholar with very diverse interests. I know you have worked on astrophysics, physics (obviously), to scientific replicability, to algorithm fairness, to data authorship. From STEM, to social science, to data science. So I wanted you to share with our readers both the joys and the challenges in working in so many different areas. Is there any overarching theme when you choose your research topics?
Mercè Crosas: [00:12:27] Yeah. I don't know that it's a well-planned theme. You know, you don't sit there and say, 'This is what I'm going to do.' Usually things evolve because you take the opportunity at the moment or that you find yourself in a situation that [makes you think] ‘this is interesting.’ And they're a little bit less planned than it seems once you've done them. It's true that at some point to me, I had a lot of interest about how we can do science better. And a lot of the things that this overarching theme is about if we can have more access to data, how do we do that? or how do we share the data for research? Because if we have more data and better data models or better code and algorithms, we improve science. But to do that, you need to do it responsibly, but also in ways that there are incentives for everybody, right? You prioritize other things than those that we have prioritized in the past, so we can do science in a way that it's open and collaborative, and be able to reuse the work of others. So I think this was one of the themes that in the last part of my academic trajectory I emphasized more and more. That's the main thing. But at the same time, I just have an immense curiosity for everything. I could now do another PhD. I would love to do one in quantum computers, for example.
Xiao-Li Meng: [00:13:59] I want to do that too! [Laughing.] But I think you hit the keyword, 'curiosity,' because I guess this probably brings you to so many different fronts. As we just talked about all kinds of leadership roles you have taken, all kinds of research topics you have engaged yourself in. But on top of everything, you are also a very passionate teacher and a mentor for many postdocs and interns. And that really completes the whole picture, right? Because we do leadership, the research, being a scholar, but also deeply caring about teaching and your mentorship. I want to ask you that in what ways do your leadership and scholarly activities interact with your teaching and mentoring endeavors? Do they enhance each other, or do they sort of become competing for your time and attention?
Mercè Crosas: [00:14:50] Well, I think that for me, more than the standard or the traditional teaching, I've done a lot of talks and a lot of training and a lot of mentorships to smaller groups. But exactly when you do that; when you disseminate the work, when you try to pass it to others, when you have or you are mentoring postdocs, that is when you learn the most. I'm sure you had that experience, right?
Xiao-Li Meng: [00:15:14] Absolutely.
Mercè Crosas: [00:15:14] How much you have to learn in order to prepare for something. Because even when you know that you know it inside, but you really learn it or you realize that you know it when you have to explain it to somebody else, right?
Xiao-Li Meng: [00:15:32] Absolutely.
Mercè Crosas: [00:15:33] So I think it goes hand-in-hand because first of all, I think it's your responsibility when you know about something to be able to share it with others and to prepare others with what you've learned. But also because it just helps. I mean, in a selfish way, it's how you organize your thoughts and make sense of them and improve them. I have to say from [my team and] from my postdocs, I learn a lot from them too. It was always very interesting to discuss ideas and be able to learn from what they would bring too. They were always better than me in something. [Laughing.] So it was good to learn that way.
Xiao-Li Meng: [00:16:16] Well, you just revealed a secret that most professors know: the way we learn things is by teaching a course. And I—seriously, everybody was getting to deep learning, and I did not really understand it—so I decided in the last couple of years to team up with another group of people to teach how deep learning was used. And I learned tremendously. I still don't quite understand it, but I listen and now know a little bit more. And I've done this many times. Yes, you're absolutely right.
Mercè Crosas: [00:16:47] And I find that sometimes it's almost ironic that you start reading about something and learning it because you need to talk about that, and you think you're just a learner, and then all of a sudden they talk to you as the expert on that, and I think 'No, no, no, I was just learning!' [Laughing.] But I think that maybe now it happens more and more because there are so many areas of research and technology that change at a faster speed and you're constantly learning, right?
Xiao-Li Meng: [00:17:18] Yeah, absolutely.
Mercè Crosas: [00:17:18] And it's really hard to call yourself an expert if you're really honest.
Xiao-Li Meng: [00:17:22] Right, right. You're absolutely correct. I also think it's particularly important for all of us in the education space to take the teaching-mentoring opportunity as our own learning opportunity because that changes your mindset. It's not like it's competing with the research I have to do. Like, why do I have to do this? Because it's just part of your own learning. And we obviously learned the best when we have to explain to people why that is true or why that's not true. But nevertheless, we only have 24 hours a day.
[00:17:55] You seem to have 28 hours a day, because on top of everything we just talked about, there's one more aspect we have not talked about, which I personally benefit the most from you. Because you also—I mean, first, you served on HDSR's advisory board. But I know that in the past, I had been engaging you not just as an advisor, but rather as an editorial board member, and you have reviewed multiple articles for HDSR, and I remember times where we had situations where the article did not get handled well, I asked you for the emergency help. And you always not only just helped me promptly, but really provided very, very insightful comments and things. I was wondering how you do those things. Because I do work hard too, as you know that we all work hard! But I know that you have all the other responsibilities, unlike me, who is mostly focused on research and the teaching. So there has got to be some kind of secret of how you manage your time, because on top of that, apparently you also have a real life. Not just a professional life. I remember we had a conversation—you were having time to contemplate writing a play about data scientists or dating scientist, as we were talking about. So, can you share with our readers how you really do all of those things, but still manage your time? I do poorly in terms of time management. I just don’t sleep that much, which is not good for my health. I want to know, what's your secret here?
Mercè Crosas: [00:19:30] Well, that's interesting. I think that my family would say that I don't have that secret. I think that we all struggle with that. They would say that I work too much all the time, so I don't know if I have a secret. But I do know, though, that if I do have one, is that I love living. I love doing everything intensely. I work intensely, but I also think that living a filling life is so important to me that I don't want to sacrifice that just because of work. So I manage to add it into this mix. I think the thing is, for a lot of us, if you also want to do this other thing that has nothing to do with work, you make [or give] yourself the time so that the time doesn't control you. And so I'm trying not to do as many meetings. But I know we all talk about that and then we end up with many meetings. But there are some wonders about when you don't have to do a lot of meetings, how much you can do.
Xiao-Li Meng: [00:20:38] That's true.
Mercè Crosas: [00:20:38] But I think at the end, well, if you're passionate and intense about not only working but enjoying life, you also find time to enjoy life. Yeah, the data science for dating scientists . . . we still need to prove if that would work. Does it work?
Xiao-Li Meng: [00:20:56] We don't know!
Mercè Crosas: [00:20:57] To use data science to date better scientists? There's doing plays - this one which I would love to coauthor with you, thinking about dating scientists from a point of view of data science. And there is another one that I've been thinking for years, and I've been collecting information, but I need to still put more time to write it. It's about the Cosmic Microwave Background (CMB) discovery. So at 2.7 Kelvin [radiation] around us, right in the universe, but not only the cosmology or the physics part of it but mix it with the part that is more social science—the behaviors, the scientist, the people that are part of it—and mix that with relationships and human interactions.
Xiao-Li Meng: [00:21:47] That will be fascinating, and you'll be absolutely the right person to write about that because you have actually the real expertise and you have such a rich life experience.
Mercè Crosas: [00:21:59] Some time ago I interviewed Bob Wilson, from Penzias and Wilson Nobel Prizes, who is at Harvard–Smithsonian Center for Astrophysics, and I used to have the office very close to his. We talked a lot about details about the discovery of the CMB. So yeah, I was taking it as data for my play later.
Xiao-Li Meng: [00:22:22] Okay, well, I wish you great luck in collecting more data and we would love to see that. We obviously need to clone you, but clearly AI not the solution. I want to think about the ways we can train future leaders who are as versatile as you. One crazy idea I've been contemplating is this idea of why can we come up with multi-sector collaborative career path programs for our young talents for future generations? For example, having a program which every person in this program will accumulate 5 years working experience in academia, 5 years in industry, 5 years in government, in whatever order will be suitable to the individuals. It may seem long, but I think with the way the society is developing, yourself is a such terrific example, many of us—probably not me, I'm too old for that—many young individuals will be experiencing multiple careers or opportunities anyway.
[00:23:33] But if we have a systematic way of doing so, and after these people going through this 15-year program, whatever the term will be, they should be ready to be societal leaders. They could choose any particular field, because the key thing, as you said at the very beginning of this conversation, is when you go through all these sectors, you develop a sympathy, right? You understand how challenging things are, you are no longer just kind of complaining that everybody else is doing wrong and you are doing the best—you see how hard it is. I think our political leaders, if they had that kind of sympathy, I think they probably would do better. That's my hope. And of course, programs like this probably would be very challenging, for example, in who will be funding it. I was thinking that since the industry has the most money, they should fund it. But to them, the program should be a great benefit, because you have these individuals, obviously very talented ones that come for five years, and they can serve a lot to the industry. And if they decide to stay, and after 15 years be an industry leader, they will be much better informed in terms of what academia does or what the government does and vice versa. I feel like a you are the right person to talk about this because you have experienced all these.
Mercè Crosas: [00:24:45] I think that would be excellent. I think it would be great. It would be interesting to pilot it, test it, to try it for some time and see. Now when it happens, usually it's due to just opportunities [that one encounters]. It's not that you have designed it, that that's what you're going to do in your life. And then, actually, it's often the case that when you're doing one thing, it's very hard to imagine yourself that you could be doing another one, right? But if there is a program that allows you to do that, it encourages and enables it, well, then you have this excuse to say, 'Just because I chose academia, I don't need to do that all the time.' There is another type of life, another type of direction of work. For one thing, there is the advantage that now we live a lot longer, and I think a 15-year training is not a crazy thing. The time that we have to form ourselves and to work is becoming longer and longer. And having more time that you're continuously still learning but at the same time already applying it, it's an iterative learning and being already relevant to society. I think it just fits much more [now] than years ago when it was more difficult when we didn't have that much active time in our lives. Active aging is now really a thing, having the lifespan increasing. And the other part of it is that when you're young and even when you know very well 'This is what I'm going to do the rest of my life,' you could be very wrong, right?
Xiao-Li Meng: [00:26:26] Absolutely.
Mercè Crosas: [00:26:26] Or you could miss an opportunity to have learned something else that is also very interesting. And this idea that you could have a leadership career where you have experience in three different sectors, I think that would be an eye-opening for many young, talented people. I think we should try it.
Xiao-Li Meng: [00:26:43] Great! Great. I want to. Okay, let's talk slightly more on the logistics side. First, based on your experience—because I've been contemplating, and this one, I really don't know—what would be the right order? The sequence? Should we start with the industry? Should I start with the government or the academia? Or maybe that should depend on the individual's interests. What's your thought?
Mercè Crosas: [00:27:09] Maybe try it as an experiment. What causes people to choose? Some people choose more [to start on] one [sector] than on the other. I do think, though, that there is some aspects of research and academia and rigorous learning that are necessary to go through in the beginning just to make sure that there is that knowledge and preparation before you jump into becoming active in each sector. But that could be an initial phase of the program.
Xiao-Li Meng: [00:27:40] I see. That's a great point. I mean, these days, I can tell you, in academia and it has been increasing so that more and more students—it used to be they all become someone like me, become a professor—but now more than 50% of my students who all went to Wall Street, industry, not much government. And that's actually the one thing that the government side always wants to recruit more data sciences to them. I think there's partly an aspect of paying people well. That's obviously important.
Mercè Crosas: [00:28:13] Right. There is a practical side.
Xiao-Li Meng: [00:28:15] Right. I think that if we're going to talk about logistical things, this would be interesting. How do we ensure that no matter which sector you choose, the economic benefit will be similar? Because otherwise, obviously people may choose toward the ones which have more financial reward. So there are all these challenges there.
Mercè Crosas: [00:28:34] You mentioned that it's harder to come out of a busy program or just your own training and say, 'I want to go to government.' Few people do. It happens less often. And I think that in the time that we're living, even from inside government, it is also very important to understand data science. How do we get [the data]? Or just how do we process information? How do we provide more evidence for the policies we're going to apply? How do we that? So having this training across the different sectors would help to prepare people in government that might have a more objective, rigorous attitude toward solving a problem in society and would bring that into the way they would govern. And at the end, society is what would benefit the most from all this. But obviously each sector could benefit. I think that also maybe in industry, having had a mix of ethical research, training, would also help. And then you go to industry in a way that you see also the limitations of what can be done when you're in the public sector also might help.
[00:29:56] I think altogether that at this time, that you need some level of understanding of what is artificial intelligence or what it means to use data, and have to at least be able to say, 'I need some help with that,' right? That you don't do crazy things with it. I think that that's very important.
Xiao-Li Meng: [00:30:16] Yeah. I mean, you're absolutely right. Obviously, someone like you is part of the reason. I guess you will be called on to take all kinds of leadership roles because of your rich experience. We all like people that have this rich experience. But currently that happens much more organically, right? People like you, because one thing leads to another, there is no structure to provide that kind of environment. And I think lots of people, lots of young students and talents, they actually love to work in government in the sense of seeing the impact, seeing how their talents are being used directly. What they're unsure—it's just like as you said—is whether it's the environment they want to work forever. So, if we put on a structured kind of a program that you know your career paths, that will have a chance to go through all of those, then you can decide. I think that that hopefully will encourage more people to go to every sector. There's actually another sector; when I say government, I should also mention the NGOs [non-governmental organizations], right?
Mercè Crosas: [00:31:13] Right.
Xiao-Li Meng: [00:31:13] Because these are the ones that tend to have enormous impact, but on the other hand, lots of people are probably unsure whether it's an environment they will be able to work for a long time. I think what I was thinking about a program of such is to encourage the young talent to just go through them. I mean, you don't need people to really be there permanently. You need some of them be permanent. But if you have a lot of young talent, that's what makes our university such an innovative place, because we have graduate students. They come and they go. That's how the faculty benefit from working on so many different topics. If it's all just by us, we probably will be just working on one thing that gets narrower and narrower. It's the students that push us. I think of this almost like a revolving door, but with a structured revolving door so people can come and go.
Mercè Crosas: [00:32:01] Exactly.
Xiao-Li Meng: [00:32:02] That might help. So, yeah, I would love to talk to you more about how we make things of this nature not just a crazy dream. Maybe you can write a play about it! ‘See, this is like the ideal career path,’ what is it like? We can plan by writing a play about it.
Mercè Crosas: [00:32:23] Make sure it's not a play about me, but. . . [Laughing.]
Xiao-Li Meng: [00:32:29] Well, it's a play inspired by you and written by you, but for the future generations. Speaking of which, I think unfortunately, we need to wrap up this thing because I want to respect your time. But I do have a final question, which is for all the future generations, as we talked about how we provide better training or better programs, better career paths for them. But I wanted to ask you for all the future generations who are inspired to follow your footsteps, what advice would you give to them?
Mercè Crosas: [00:33:02] Yeah, it's always very difficult to give advice, right? Well, a couple of things. One is that whatever they choose to do, to care for it. When you care about something and you think it has some importance, it could be anything. If you have some commitment to something that is relevant and important [to you] and you care for it, it's a big part of it. And the other part is that you continue to being curious and wanting to learn more about it all the time. So I think that maybe there is a combination, a balance that it's hard sometimes to find between being very self-assured about, okay, you're doing something that you believe in, that you care for, and you build a self-confidence about doing that—so there is a little bit of ambition and arrogance on it—but, at the same time, there is a humility that you've never learned enough about it and that you are continually forming yourself. That to me would be the main advice, but also then don't think again that you have to design your life and tailor your entire life from the beginning, or that if you're a stuck in one thing, that's where you need to be always. There are a lot of other things out there, always.
Xiao-Li Meng: [00:34:20] And you're a great living proof of what you just said. And Mercè, I want to thank you again for being such an inspiring leader—you have done so many different things—and for being a great researcher and scholar, and a mentor and teacher for future generations. And I also want to thank you for your terrific service and help to HDSR. I think you truly embody this whole mission of the HDSR, which is everything data science and data science for everyone, because you pretty much have done everything, and I'm sure you will do it even more. Thank you so much.
Mercè Crosas: [00:34:58] Thank you. Thanks to you, Xiao-Li.
Disclosure Statement
Mercè Crosas and Xiao-Li Meng have no financial or non-financial disclosures to share for this interview
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