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Bruin to Bruin: Eric Deeds

Photo credit: Helen Quach

By Megan Vahdat

April 22, 2024 7:45 p.m.

Listen to professor, researcher and Vice Chair of the UCLA Life Sciences Core Education Department Dr. Eric Deeds discuss his career and time at UCLA with Podcasts contributor Megan Vahdat.

Megan Vahdat: Welcome to “Bruin to Bruin,” a Daily Bruin podcast that interviews influential members of the UCLA community. My name is Megan Vahdat, and I am a contributor to the Daily Bruin.

Today I am joined by Dr. Eric Deeds, a full professor of Integrative Biology and Physiology and a member of the Institute for Quantitative and Computational Biosciences at UCLA. He is also Vice Chair of the Life Sciences Core Education department, overseeing their innovative mathematics curriculum. Professor Deeds is originally from Cleveland, Ohio, and did his undergraduate studies at Case Western Reserve University where he studied both Biochemistry and English Literature. He earned his Ph.D. from Harvard University where he studied computational biophysics and did his postdoc with Professor Walter Fontana in the Department of Systems Biology at Harvard Medical School. He started his faculty career at the University of Kansas but moved his lab to UCLA in 2019. Research in the Deeds lab focuses on developing computational tools to characterize cellular heterogeneity as well as developing approaches to understand complex biological dynamics. Thank you so much for joining us today, Professor Deeds.

Eric Deeds: Oh, no problem. It’s my pleasure.

MV: You have had an amazing career as both a researcher and an educator. Can you tell us a little bit about your path to UCLA and how you ended up drawn to the sciences in particular?

ED: Yeah, I mean, I have been interested in being a scientist since my earliest memories. I very distinctly remember that when I was pretty young, I watched a documentary by David Attenborough called Life on Earth, and it was incredible – basically relating the geological time of the Earth to a year. So we laid it out, like what happened. And obviously, when various sorts of critical transition in the evolution of life on Earth happened, talked about it, talked about the fossil record. It was incredibly inspiring for me. I don’t know how old I was, maybe seven or eight years old when I first saw that. And ever since that, I wanted to be a biologist and a scientist, actually. So when I was a kid, I was really interested in paleontology. And I remember that, at the time, there was a controversy regarding whether or not dinosaurs had been endothermic – warm-blooded. And there was a professor named David Bakker who was advocating for this warm-blooded hypothesis and looked a bit like I do. I have a really big beard and a little bit, you know, scruffy around the edges. And my mom was very worried that I was going to end up like this. And sadly, her worries came true. Because that’s what happened. But I told my mom, I wasn’t going to be a maverick paleontologist, I was just going to be a normal one. But you know, I’ve always wanted to be a scientist and had a few opportunities in high school to attend summer programs that were really, really important for me at Case Western Reserve University. And yeah, I got fascinated by molecular biology. I attended a summer program when I was in ninth grade that was run by Professor Chris Collis at Case who’s still there. And it was an amazing experience, you know. I learned about the structure of DNA, the molecular nature of the cell, and just became completely enthralled. I sort of gave up the idea of working on evolutionary biology, per se, and got very, very interested in molecular biology, and ended up deciding that’s what I wanted to do. So I got very serious at that point in high school. Before that, I was not as interested in school as I should have been, perhaps, and, ya know, I just thought, well, that’s what I want to do. And I ended up going to Case as an undergraduate having a major in biochemistry, discovering that computational biology was actually where I think the best fit for me was by basically completely failing at experiments. I tried for a long time to be an experimental biochemist and simply did not have the kind of physical skills for it– broke a lot of equipment along the way. In fact, the most they had ever seen. In the old chem lab that I took, the bill was so high, they were shocked. They were like “Wow, how could you possibly break this much glassware?” And I was like “Well, you know what, I’m special.” So I recognize that. And also, I took some classes in computer science, as an undergrad. Case especially at that time had a very strong program in CS, and I had a lot of friends who were involved. And I thought, “Oh, this is really cool.” So, I learned a lot about it, took some classes, and yeah, decided that computational biology was going to be my thing. So then when I went to grad school, you know, it was really looking around at various sorts of possible research programs in computational biology that were most exciting to me. And I was very, very privileged to have the opportunity to work with a professor named Eugene Shakhnovich at Harvard who studies evolutionary biophysics, computational biophysics with a strong evolutionary interest. So, it was really perfect because it brought my long-standing interest in evolutionary biology, my interest in molecules and how they move and how they’re structured, and how that influences cellular behavior. So I was really lucky. I got to go to grad school there and work with him. And that’s what set me up to the path where I am today.

MV: Wow, not many people have that experience of really knowing at a young age that this is something I’m interested in, this is something I want to pursue. Oftentimes, we see students here at UCLA who will start off at one thing and then completely segue to something else. Something interesting about you is that you were actually an English major in addition to studying extensively in the sciences and in computational biology. Can you share a little bit about that decision–how perhaps that kind of multi-dimensional study has influenced you?

ED: Oh, yeah, it’s a huge thing. And I shouldn’t say that students should expect to have that same sort of path that they want to follow, necessarily. That’s just what happened to me. But I don’t think it’s necessarily good or bad. I do think that taking the time to explore and really think about things is important. And, you know, we always tell stories about our lives. Obviously, when I was an undergraduate, I thought seriously about pursuing a Ph.D. in literature. It was something that I thought heavily about, and I had several professors who were very influential for me from the English department there who encouraged me to think about it and thought I would really enjoy it. I took a class on “Milton” and William Blake, which was a fantastic class when I was a junior in college. And, you know, that professor was very influential for me. He was a very, very cool guy. And he suggested thinking very seriously about literature as a career. But in the end, you know, my heart has always been in the sciences. But yeah, in terms of why I did it, I think it was just interested in it. You know, I mean, that thankfully, I was able to pursue some things that I was interested as an undergraduate in and not just focus on just the things that we’re going to push forward some kind of career aspiration. I knew I wanted to be a professor, but I love, love literature. I love it and I just thought it was so interesting. I took a class on Chaucer, and I was hooked. I was like “This is so interesting and so cool and I want to learn more.” So in the end, I saw that I could do a double major and was able to put that together. You know, because it was two different degrees of B.S. and a B.A., it was really administratively challenging, but it was a lot of fun. And I think that what I often tell people is that, you know, as scientists, we often imagine that we are sort of sitting there at the bench thinking about experiments, doing controls, doing some math, all that stuff. But actually, on a day-to-day basis, if you ask me, what do I actually do with my hands? I write. That’s what many senior scientists do. They write papers, they write grants. There’s a lot of communication that’s involved with being a scientist. And it’s my position that the better you are at this communication, the more effective you are as a scientist and educator. The style of writing in science is very different, but not that much different structurally. Well, anyway, I could go into it, but the point is that it’s very, very helpful to have just practiced it. And when I was a grad student, people would often make fun of me in my science classes for being an English major. But, you know, it actually turned out really, really helpful. And you know, at some point in grad school, or maybe as an undergrad, or whatever you’re doing research, you have your first paper to write. And then your advisor looks at you and says “Yay, your figures are done.” So now you have to go from having a story in your head to telling that story to your audience, and that’s done through the written word. Many of my colleagues struggled with that because it was the first time they had any real serious writing task, something where they had to sit down and write for months and get something together that was compelling and persuasive. And I had been doing that for years, so I felt more at home in that. So it was really, really helpful. And I’ll just share another little anecdote that when I was a faculty member at Kansas, I started my career as a professor at the University of Kansas, I had a very close colleague there who was involved with a Graduate Writing Center. She was in English as a faculty member, in the literature department or whatever and was interested in Graduate Writing and gave a seminar for all graduate students. She would start it by saying “Well, who here in the room thinks their job is to write?” And all of the humanities students would raise their hands. And then she would ask “Well, who here has actually published already?” Then, all of the scientists would raise their hands. So the idea that science doesn’t entail writing or doesn’t entail communication is just not true. And I think as a field, we’re doing a better job at preparing students. But I think there is a lot that we can still do at the undergraduate and graduate level to kind of emphasize that those skills are really critical to effective communication but also just being an effective scientist.

MV: Right, and that’s something that’s so often overlooked amongst students who are so focused on those science disciplines. Being a good communicator–being able to convey what you have researched, what you’re studying, and spread that knowledge to newer generations is something that shines through in your teaching.

ED: Thank you. And it’s so important and something I tried to achieve. But I think, you know, it’s challenging, but a lot of what we do in science seems more complicated than it really is. And I think that having the capacity to kind of like incisively cut through the complexity to the core message, and then, you know, being able to convey that effectively makes it better. And I think that many, many people in our society, view science as kind of the purview of a small group of people who are somehow endowed, genetically or endowed, in some sense with a special ability, kind of like Jedi. Like I’m science-sensitive, so I can do this. But that’s not true at all. You know, I think everyone has different interests. Everyone has different proclivities. But the point is that a lot of these things that can sometimes seem off-putting, are actually fundamentally not that complicated or that hard to understand. And it’s our job as scientists and educators to make that clear. And anyone can learn a lot of this stuff that we teach. It’s just a matter of having the interest. It does take dedication, but I think that also it’s a matter of on the side of the faculty members or scientists who are trying to convey this information, to do the work to really make it accessible.

MV: Right, and like you’re saying, so many people who maybe aren’t as strong in the sciences have this sort of internal idea that perhaps they’re not meant to pursue a science career or study the sciences so extensively. And actually, what’s interesting about your research, in particular, is that it’s a cross between a lot of these heavy scientific and biological concepts with mathematical modeling. Did you yourself always know you had an interest in mathematics? Was that something you were drawn to as a high school student or as an undergrad?

ED: Actually, no. When I was young, math was really hard for me. You know, students who come to office hours will recognize that now I can do sort of arithmetic operations in my head relatively well. But when I was a kid, I really struggled with that. In grade school, I was not a top math student whatsoever. I really hit my stride in math and got interested in algebra in high school and thought “Okay, this is kind of cool.” But it wasn’t until I went to college that I really recognized that I loved math. I had the opportunity to take a class at Case with this amazing professor named Chris Butler, who is really, you know, the person that I think of. I had two incredibly sort of transformative people that I met there, and one of them was Chris Butler. The other was Dr. Ocasio, “Doc Oc,” who unfortunately passed away. But these were people who taught these large freshman classes, and I was just blown away at how good they were. And I was like, wow, Doc Oc would have a class of 400 students in his chemistry class. And he would know every single person on day one. He would even call you by name and would just say, you know, “Eric, what do you think the answer to this is?” I mean, it was incredible. So he cared so much about the students and about their learning. And anyway, with this professor, Chris Butler, I actually realized that wow, I really love this stuff, math. I became more of a quantitative scientist in college. It was not something that I thought I was necessarily going to do, but I fell in love with it. And then yeah, over the years, I’m largely self-taught as a mathematician and didn’t have the opportunity to take many sort of math classes in college, but you know, anytime you’re doing research, what I often say is, you know, no one knows what they need to know because research is fundamentally about the unknown. And so you always have to obtain whatever skills you need to answer the question in front of you. Having that flexibility to say, “Okay, well, actually, now I need to know a lot more about differential geometry than I thought I was ever going to need to know.” And it turns out, it’s really cool, and is really fun to learn about. And it has, you know, important applications and sort of analysis of emerging biological datasets. So that kind of flexibility, I think, is critical. And, yeah, it’s been a journey for me of becoming more mathematical as my career has progressed. Certainly, as a graduate student, I was much more computational than I am these days. So I think it’s something that sort of can happen to anyone. You just get drawn in a way that is most interesting to you or wherever the research takes you. I’m definitely not one of those people who is like, “Oh, I’m good at math when I was like 10.”

MV: It’s amazing, like you mentioned, that a certain professor or a class that you’ve taken as an undergraduate student or at any level in your education can really shift your perspective on a subject entirely and draw you to something that you may have otherwise overlooked or not really known that you had that passion for. And now you’re kind of in the shoes of the professors who inspired you as a young student. The course you teach LS 30A or “Mathematics for Life Scientists” is one of the most popular at UCLA and an interesting cross between mathematical application and scientific analysis. Can you share a little bit about the course and why it’s one that you enjoy teaching?

ED: It’s an amazing class. So basically, when I was a postdoc, I got exposed to the utility of what in mathematics, we might call dynamical systems theory, which is in LS30 what we call change equations. And I was exposed to this and how powerful it was for studying a variety of different biological phenomena, read all about it, learned everything I could. And if you’d asked me at that time, you know, “Could you teach freshmen this subject?” I would have been like “No, I don’t think so. I think you need to know too much stuff.” But it was really the genius of Professor Alan Garfinkel here at UCLA who kind of invented this class. He realized that, you know, a lot of the way that we’ve traditionally taught mathematics to life scientists has kind of focused on 18th or 19th century approaches to mathematical questions, and his research, just like mine, was involved in like modern dynamical systems theory. And he realized that this is not only a really useful thing, it’s actually very good pedagogically. Because, a lot of times, not in every calculus class, but a lot of calculus classes, one is, you know, sort of solving problems just for the sake of doing so. That can be useful, but the problem is that one often doesn’t understand why the integral of sine to the fourth x cosine cubed x dx? Like, why do I know I didn’t need to know the antiderivative of that thing? It could be useful in some contexts. But typically, in biology, that kind of skill of being able to kind of solve those types of problems is not relevant. At least it hasn’t been so for a lot of people that I know. And so what we do in LS 30 is start from this notion that well, we have problems in biology, we have things like pandemics, we have things like ecological disasters, and we want to predict what’s going to happen. We want to understand why things are happening the way they are in these systems. And it turns out, there’s a fantastic mathematical language for talking about that. And that’s how we introduce things. And I think it helps kind of capture one’s imagination, not just because it’s applied or practical, but because it’s actually scientifically central, right? Like, it’s not that the science is on one side and then we do the math separately. The math is necessary to do the science. And you know, the science is necessary to motivate the mathematics. And so it’s really powerful. And I think students really connect with it. So I’m privileged to have the opportunity to be involved with it, to be the vice chair here, and to kind of help run it and push it into the future. You know, it’s a really amazing class. And for anyone listening, who is thinking “Oh, well, should I take it?” I think you should. It is a great class. Don’t necessarily take it with me, but definitely take it.

MV: I think so many aspects of the course like coding and mathematical model modeling as opposed to, like you mentioned, topics studied in traditional calculus are especially useful and relevant with the advent of AI software, like ChatGPT. How do you think more institutions should adapt their math classes to better match how mathematics is really applied in the real world? I mean, like you mentioned, are traditional math classes like calculus with problems that are unrealistically written to have one perfect solution even still applicable to our new generation of students?

ED: I mean, I would argue, no. I think that it can be helpful to know some of the kind of tools and tricks that come with what we call traditional calculus. But, um, you know, certainly in the life sciences, you’re much more likely to need to model a system and solve a system of ordinary differential equations to understand what’s going on than you are to solve an integral. I think that biology is becoming an ever more quantitative discipline. In my life, I’ve seen quantitative biology or sort of more mathematical or computational approaches to biology emerge as a major theme. And, you know, if you look at the number of papers that are published using computational techniques, it’s just skyrocketing. If you generate data in the lab, typically, you have to analyze it. And the datasets that are being generated these days are not the kind of datasets that one can analyze productively, without the application of computer tools. So students need to know about that stuff. And I think that our pedagogical framework in the United States as a whole when it comes to life sciences, has been slow to react to that. And people are aware of what we’re doing here at UCLA, it’s being considered as a model for this kind of shift. I actually helped run a workshop at Harvard last summer. I’m doing it again this summer with Alan Garfinkel and Brendan Kelly who’s at Harvard and a few other people. And it’s been an incredible experience. You get to meet with educators from around the country. But part of the issue is that if I’m talking to a mathematician, the relevancy of the math that we’re teaching isn’t as immediately clear. It’s not going to be easy. I think it is the life scientists who have to actually take charge of this and be like, well, you know, “we need this for our students.” And if the math department isn’t interested in providing this kind of educational resource, we need to figure out how to get it done. And that’s what was done here at UCLA. And I think that’s going to be a model going forward. The thing we need is something that we know about and we can teach in the life sciences. I’m hopeful that this will become the norm in education in the life sciences from a mathematical and computational standpoint, you know, in the next 10 years.

MV: People so often overlook how interconnected these mathematical topics are with science. In our traditional math classes, those two ideas are so separated. We need more collaboration between those two departments at so many schools.

ED: Yeah, I would agree with that. And I think that, um, you know, oftentimes, I think you put it extremely well, that the problems you solve are contrived to have a solution. And if you solve enough of these problems, you really see it, you’re like, wow, okay, that worked out. Because we knew the answer ahead of time and when we designed the problem, we designed it so it would have a nice answer. And that can be fun. But it’s not, in practice, what happens. The universe hasn’t sent us a set of problems that have nice solutions. It’s sent us a set of problems that are messy and complicated and involve actually even a different notion from the goal being to have a formula that you start with you bang on symbolically for some time. And then you come up with another formula that represents something that can give you a lot of insight when it happens. But what if it can’t happen? What if such a thing isn’t going to work for you? What do you do? How do you gain real insight, real mathematically grounded formal insight into your system? Well, there are tools for doing that. And on the surface, they appear really complicated. But actually, when you start interfacing with them, you realize, actually, you know what I can teach freshmen about vector fields. That’s actually not that bad. Even though it’s, in many ways, a pretty advanced mathematical concept. It’s something that students get, and they can get it really, really intuitively because foundationally it’s not that complicated. And I think that, you know, that notion that we need to teach mathematics that actually sort of provides the relevant scientific insights is a very powerful one when you’re thinking about education.

MV: As a researcher, do you interact with these types of issues that you model on a day-to-day basis? Can you tell us a little bit about the types of research that you’re working on right now?

ED: Oh, absolutely. Um, you know, in terms of my recent research, my lab is absurdly broad. And I would never recommend that anyone try to run a lab the way that I do, but I have a number of projects that are very closely related to what we study in LS30. So one of them involves trying to understand the dynamics of physiological processes. So for instance, I have a postdoc in the lab right now who’s studying feedback regulation in the kidney. So you know, kidney failure is a really big deal. It happens a lot. It’s not a pleasant situation if it happens to you. My grandfather had it, and it’s really, really bad. You know, dialysis sucks, kidney transplants are hard to get, but we don’t actually understand a lot of the kind of fundamental things that are happening. And in particular, there’s a lot of cool, kind of oscillatory behaviors in the kidney, that we only barely understand and that haven’t really been incorporated in our kind of medical practice or our physiological sort of law regarding how the kidney works. So in my lab, we’re making models of how the feedback that we study in LS30 actually generates those oscillations, how it might differ between different animals in different organisms, how it might have evolved to have the properties that it does, and also how it might kind of break down. So if you take a drug, what happens if you get kidney failure? We use models just like models of the COVID-19 pandemic helped us understand what was going on and predict the future. These models can help us understand the underlying physiology. In some ways, we’re making models of what we think is true and then we can see how that plays out. And so that’s one thing we’re doing in my lab. But a major interest in my lab has to do with heterogeneity in biology. When I was an undergrad, I thought, you know, all my epithelial cells that line my gut are kind of the same thing. They’re all kind of like the way I would build a computer, all of the like little pieces that are the same or the same, you know, like all of the little transistors or whatever, they’re the same. But every time anyone makes a measurement at the single cell level, you see that cells have a huge amount of individuality. They have very different levels of all of the molecules that are important inside them. And so one fascination of my lab is developing tools and techniques to mathematically and computationally characterize that diversity based on the datasets that are emerging, but also modeling it, like what, what generates this tremendous level of diversity? And what functional consequences does it have? That kind of modeling is a little bit beyond what we typically do in LS30, but is very closely related in a lot of ways. And I think we’re just in the very, very early stages of kind of just trying to realize, well, what are we even characterizing? What is the model supposed to predict? What is it going to explain? So yeah, my lab is really interested kind of in cellular individuality.

MV: So on a daily basis, you’re interacting with the topics that you’re teaching. You mentioned COVID specifically and using mathematical modeling to determine perhaps the trajectory of certain variants. How is modeling used in relation to COVID specifically?

ED: I don’t research that myself. We have faculty here at UCLA who are absolute world leaders like Jamie Lloyd-Smith in EEB. My first graduate student, Michael Rowland, Dr. Michael Rowland, is at the Army Engineering Research and Development Center in Jackson, Mississippi. He’s part of the Army Corps of Engineers. And he helped develop their COVID model. So he did his Ph.D. with me and worked on modeling and then actually worked on the Army’s COVID model. And that was one of the 20 models that the US government was using to predict the course of the pandemic. So, you know, I think that, yeah, we haven’t worked on that kind of epidemiology in my lab. Not that isn’t interesting, it’s just that time is finite and one cannot work on all of the things that that one might like to but, um, the point is that you can use mathematical modeling in a very productive way to both predict what’s going to happen in the future but also understand what’s going on—to understand causally what’s happening, to understand what kind of causal interventions can we make to you know, have the system have some kind of desired outcome like less hospitalizations, less death, that’s very, very, very, very closely related to what we do in my lab. So although we never got to work on that directly, it was really cool. I mean, I don’t know, I’m very proud of Michael. Go, Michael! If you ever hear this, you’re awesome.

MV: It’s incredible that you’re able to see the product of your hard work and that students who have studied under you now are accomplishing such amazing things.

ED: Oh, it’s a huge privilege. It’s awesome. And you try your best to help people or teach people. But the point is that, you know, it’s the people themselves who are awesome, you know what I mean? Like, I’m just here to help people learn, and then, you know, they can go off and do the amazing things that they’re going to do because they’re amazing people.

MV: Do you work with any former LS 30A students in particular?

ED: Oh, yeah, my lab is full of LS30 people. For those of you who are listening who have taken this class, I cannot emphasize to you how different your training is from traditional biology students. It’s so cool and so powerful. So I’ve had the incredible luck and privilege of recruiting a number of LS30 students into my laboratory. These are students who, you know, started early on in the program, got really excited, saw the power of mathematical biology, got involved in teaching the classes as LAS and undergraduate TAs, and then got involved in research in my lab, just because we’re sort of in the same environment. You know, I’m working on the class all the time. I do a lot of stuff in the core, and they’re there. And they work with my colleagues. And you know, when they’re looking for a lab, they’re like, “Oh, maybe we should see Professor Deeds.” And I’m like, “Oh, that’s so cool.” So um, yeah, I’ve had I’ve had you almost every undergrad who’s done research with me has taken LS30A and has gotten excited about it. And I wish that I had the room in my laboratory for all of the amazing students that I talked to who want to do research in this area. But part of the issue is that, you know, like I said, our kind of framework, scientific systems have inertia, they’re slow to change. And so, you know, I think that one issue we face is having enough labs for people who get excited about this. Undergrad research, like all research, is pretty intensive. And so, you know, one can’t simply take 30 or 40 students in one’s lab, it’s just not a thing– at least to give them any form of mentorship or attention, which I think is really important. So that’s what I do. But basically every undergrad that I’ve ever had in my lab here at UCLA has taken all this theory. And it’s so cool. And you know, these are people you don’t have to teach dynamical systems theory to because they already know it. It’s hard to understand, if you’ve grown up in this system, how different it is from having to have an undergrad or grad student who joins your lab, and you do dynamical systems theory, but they’ve never had a class in nonlinear dynamical systems theory. So they have to start as a graduate-level class rather than a freshman class. And it’s, it’s so, so cool here at UCLA that we have that and I’m really lucky to be able to recruit people like that to my lab. Right?

MV: It must be an amazing feeling to get to watch these students mature over the years as students and to take what they learned from you and our other professors in the LS30 core and apply that on a real-world level.

ED: It’s the best thing ever, like watching your students, like, pursue their own awesomeness is really cool. You get to see it and you get to be a part of it, which is really great. It’s really amazing. It’s the best thing. All of the students I’ve ever worked with, they’re all so successful. And it’s so cool. And yeah, I mean, it’s what makes this job so amazing. You know, like, you get to do this for a living and interact with young people who are fantastic, and they’re so smart, and they’re so motivated, and they’ve got so many interesting ideas, and then you get to see them blossom, and be a part of their trajectory. That’s really, really awesome.

MV: I think there must be so many listeners right now who aspire to follow a similar path as you and look up to you like those students who work with you in your lab. To end today’s interview, I wanted to ask you, what is the number one piece of advice you have for undergraduates who are trying to figure out what they would like to pursue in the future?

ED: I don’t know if this is good advice, but it’s the advice that I’ll give which is to follow what you’re interested in. Follow your passion. I think that right now, in some ways, you know, our society has a very operational view of education in the sense that it is meant to get you to a particular job or position. I understand that and I think that’s an important and critical consideration. But as you go forward, you know, the academic and intellectual interests that drive you, you should feed those sometimes, or as much as you can. And understand that life is really much longer than one feels it is when one is 20. At least that’s what I’ve learned in my life. I don’t know if I’m representative, but certainly, I felt like when I was 20, like, “alright, I got, I got three, I got three weeks, you know, that’s it. I got three more weeks. And then I better do this”. But actually, it turns out that, like, you have years and years and years. Becoming a professor is a very long journey. And it has many weird twists and turns for every single person I’ve ever known who’s chosen to pursue that career. But it is amazing. And it’s such a cool thing to be able to do. And I think, you know, it’s unfortunate that I think sometimes we as a society don’t value the people who practice higher education at all the different levels where it’s possible. Community colleges, Cal States here in California, you know, all of these people are contributing massively towards society. So if you’re interested in that, you know, pursue that and go after it. Um, yeah, that’s my piece of advice but I don’t know that one should listen to me to be honest with you. But, if you do, you should also take LS40– you should definitely take that class. It’s amazing.

MV: Professor Deeds, thank you so much for sharing your expertise with our audiences. We appreciate your time.

ED: No problem. Thank you so much for having me.

MV: This episode of “Bruin to Bruin” was brought to you by the Daily Bruin Podcasts. You can listen to this episode and all Daily Bruin podcasts on Spotify, Apple Podcasts, and SoundCloud. The audio and transcript of today’s interview are available at I’m Megan Vahdat. Thank you for listening.

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