The Bioinformatics CRO Podcast

Transcript of Episode 79: Yang Li

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Grant Belgard: Welcome back to the Bioinformatics CRO podcast. I’m your host, Grant Belgard. Today, we’re joined by Professor Yang Li from the University of Chicago, a computational genomics researcher working at the intersection of genetics, gene regulation and disease. Yang, welcome.

Yang Li: Hi, Grant. Nice to see you.

Grant Belgard: Good to see you again. So what’s been energizing you most recently in your work, scientifically or operationally?

Yang Li: Well, since the New Year’s, I’ve been playing a lot with Claude. I mean, everyone’s, I think, playing with Claude. And I think both in terms of the science that he can help me produce and also, you know, just managing my schedule, that has been a game changer. And I’m still exploring what he can do. But yeah, so I think that’s basically what’s been what I’ve been thinking about most of the time.

Grant Belgard: What have you put into practice so far? Like what’s kind of, quote unquote, in production?

Yang Li: Yeah, we’ve been writing the revisions for one of our papers. And I’ve been using that extensively both to help me write some of the response, making it a little bit friendlier, but also rewriting some of my old code and checking for bugs and things like that. And it’s amazing. The number of things that I can do in just an hour far exceeds what I can do within a day at this point. So things like producing a plot in a slightly different way. As you know, it’s very difficult to rerun your code, especially if it’s not the best practice in the sense of software engineering. I’ve been self trained in terms of programming, mostly, and so the comments are not necessarily the best. But with Claude, it helps me comment, it helps me name my variable, right?

Or at least improve the naming of my variables, and then produce plots very, very fast, right? And so as you know, a lot of the way we check that the code is doing its job is to visualizing the underlying data in many different ways. And so Claude helps me do that. You know, as soon as I have an idea, I can just ask it to do it. And then I would see the visualization and I would sometimes I would find error. But most often than not, it gives me exactly what I expect.

Grant Belgard: When someone asks you what you do, what’s your favorite way to describe it without using jargon?

Yang Li: Well, lately, I’ve been trying to steer away from that because I’ve been doing things that are pretty technical. But in just a few sentences, I think I would just describe it as I’m trying to understand how proteins are expressed. And there are many different ways by which we can control the expression of these proteins and focusing on this regulatory mechanism called RNA splicing. And this is highly regulated. And I want to understand what is the function in different system and how to modulate it using drugs.

Grant Belgard: What makes this the right time for that?

Yang Li: Well, I think the reason why I chose this and I stuck to this ever since I think I was in grad school, really, is because almost nobody talks about genes in terms of how many proteins each gene can be producing. And so and it was clear when I was researching, even the things I was researching in grad school, which is, as you might remember, the cichlids, it was clear to me that every single gene produces many proteins or many isoforms. And to me, it felt like this has had to do something. Right. And my perspective has changed slightly since then. But because of my earlier work and the fact that no one, almost no one was really researching that, I became really interested in that topic.

Grant Belgard: So what is your current perspective on splicing?

Yang Li: Well, when you read the textbook, it basically tells us that every single gene, every single human gene can produce many different proteins and many different protein isoforms. So these are isoforms that are essentially the same, but with slight differences. So it could be one protein domain that is included in an isoform and in another isoform, this same protein domain is excluded. And so often textbook or in literature, it would be described as something intentional, as in the two version of the proteins have very different function. So one would be performing function A and the other would be performing function B. And both are very important for the survival or the proper function of a cell or the organism.

But what I think now is that the vast majority, and by a vast majority, I mean really over 90% of these different isoforms is not really to have a different function, but really as a regulatory sort of switch. So again, to fine tune, very similar to gene expression level, right? So when you regulate gene expression levels through enhancers and promoters, you’re not changing the final output or the function of the gene. You’re just changing the activity by a little bit. And I think splicing most of the time is doing that, is doing exactly the same thing. The regulatory input is a little bit different, but the outcome is very similar.

So it’s able to change the protein and have a different function, but those are really the minority of the cases rather than the majority of the cases, as is taught by literature or the textbook.

Grant Belgard: How do you decide if a problem is method worthy or just something you’ll apply existing tools and move quickly on?

Yang Li: So do you mean in terms of developing a tool or just using a tool to solve a problem? Right. So I think it takes me a long time to convince myself that I need to develop a method for something. And so in general, I try to use methods that exist already or previous method that I or my lab has developed. In some very rare case, I think, hey, we need to develop a method because there’s really something that hasn’t been done. And we really need to do that and also that we can do it. So all of these checkboxes has to be checked in order for me to move on to method development.

And I should say that we’re not particularly, I don’t think my lab is particularly good at developing methods, but we’re pretty good at identifying, I think, problems that can be solved by an older method whose goal is not necessarily for, well, hasn’t been developed for the specific question.

Grant Belgard: What are the most common bottlenecks you run into today? Is it a matter of data, compute, annotations, study design, interpretation, something else?

Yang Li: Yeah, that’s a pretty good question. I would say for me, it’s my time and getting a sense of what to focus on when there’s just so many people that I think needs my attention, so many projects that needs my attention. I think one thing that I’ve heard a friend tell me was a good example that I often talk about. It’s this context switching time. I’ve heard that the grape vines that Terry Tao, the famous mathematician, is extremely good at context switching. So he basically could switch from one problem to the next within seconds. And for others like me, we need more time to context switch. And so our schedule, when I guess you become a faculty, is that it’s spreading blocks of one hour. And I find it pretty hard to switch context from one hour to the next.

So I try to block more time, but then there are fewer blocks of the longer period of time. And so I think that’s somewhat of a bottleneck for me, is to find a longer block of time so I can have the time to context switch and then do deep work instead of just trivial work in order to make progress. It feels a lot of time I’m trying to just keep afloat and that doesn’t give me enough time to do enough deep work, which is the thing that I think I’m good at and also the most happy in doing. Yeah.

Grant Belgard: Have you found using tools like Claude impacts that in any way?

Yang Li: Yeah, yeah. So I think previously it was very hard to, I had a lot of questions about a data set or some topic and it just never felt like I had the time to do it. And with Claude, all of a sudden you could do things that would take a few hours. It would just take you a few minutes because it had the context, it remembers the context in which and you would ask and then you would remember the context and then you would just do it. So for example, plotting a figure about a data set and then it remembers where the file, where the raw data was. It would have taken me maybe 10, 20 minutes if I came back to this specific project after a week. It would take me maybe 10, 20 minutes to even recall where was the file that I was using and what exactly I was doing essentially.

I can ask Claude or summarize what I was doing or just scroll up a little bit and then ask questions and then he would give me the answer within a few minutes and then that would get me back on track much more rapidly than he would me by looking at my own code and browsing and recalling. So that has been extremely useful. I think also Claude might be able to help me manage better. I haven’t implemented this, but I’ve sort of joked around that I would have my trainee talk to an agent or Claude and then Claude would tell me, you know, summarize all of their things. And then I would only have to read through the summarized version.

Grant Belgard: So you could be like the nurse at a doctor’s appointment before you see the doctor, right?

Yang Li: Yeah, exactly. And then five minutes before I meet them, I would review that and I would think a little bit to just get into context. And then it would be, I think, a lot more productive, right? So, yeah, I often tell my students to prepare some slides or some notes before I meet with them so that it helps me switch, get into context, because oftentimes I hear about their problem on the spot when they come to me during the half an hour or the hour period. And then I have to think about it. And oftentimes when I think about it, I find it’s not really awkward, but it’s still some pressure to answer, right? I can’t think if I thought in silence for five minutes, even two minutes, right? It feels a little bit long, right? Let alone 10, 15 minutes.

But oftentimes that’s the time that you need to bring you back into context, to recall all of these different information, right? To have a very effective conversation. But the reality is that it’s also hard on them to come up every time with a few bullet points. Or at least, you know, I don’t know if it’s hard for them, but they don’t do it essentially. And this would, I think, speed up tremendously our meeting or at least make it extremely productive because everyone’s on the same page.

Grant Belgard: What’s a recent result or direction that surprised you and how did you respond to the surprise?

Yang Li: I can’t say that there’s a recent direction that really surprised me. I think I plan my projects long in advance and I can see points of failures pretty early on. And oftentimes the project or the direction does indeed fail. But then I often have a backup plan. And so I don’t think there’s anything, any direction that surprised me, I would say. And unfortunately, there hasn’t been anything like a sudden discovery that changed everything, unfortunately. So I’m either a very good planner or just not super lucky in terms of unexpected findings.

Grant Belgard: How do you think about reproducibility in practice? What’s good enough versus gold standard?

Yang Li: Yeah, I think there’s a lot that can be improved in terms of reproducibility. Unfortunately, when I think there is some amount of pressure to understand the system, the biology. I mean, there’s a speed component, right? You want to dig into the biology more rapidly. And oftentimes the solution to that is to do what you know best to do. And we’re not trained as software engineers. We don’t do these kind of unit tests. And so reproducibility and there are bugs, right? So I’ve developed LeafCutter many years ago and I still find bugs there. So in that sense, these things can be improved drastically. On the flip side, I don’t think any of these bugs or these issues affect our results, our biological interpretation of things.

Very rarely there would be a very important result that are affected by these. It does happen that it affects a very minor result, right? Or the interpretation of a minor result. And to prevent these bugs or these lack of reproducible findings, we essentially try to poke holes at our major, what we call major discovery. So the things that would, for example, break a paper or the main finding that we think we made. We would look at many different data sets and we would design tests that would essentially break it in one way or the other. So we have very orthogonal ways of trying to confirm a result. That would include, for example, looking at a just completely different data set or deriving some corollary. So based on these, if this were true, then this other thing must be true.

And so we would do more tests on whether this downstream result should be true, will be true. So we do a lot of these type of analysis. And then at some point, everything makes sense. And if something doesn’t make sense, then we have to explain this. Right. So I think this is a scientific process. And I’m not going to claim that this is foolproof, as in I will never have anything that is later falsified. But I think from my track record, I think this has worked so far.

Grant Belgard: How do you decide what to delegate and what you personally stay close to?

Yang Li: Right. So I try to delegate as much as possible. I try to delegate anything that I think a trainee or someone else or a collaborator can do to them. But I obviously weigh by importance. So the things that are the most important, even though I also try to delegate those, depending on whether I think they can do it, I would pay attention to the outcome. Yeah, in my minor things, I would just trust them to do the correct thing. And sometimes, you know, we have to backtrack when later on we find a problem.

Grant Belgard: How do you help your trainees develop taste, knowing what to do and what’s not worth the effort?

Yang Li: Yeah, that’s a very good question. It’s a little bit like asking me, how do I teach creativity, someone to be creative? And yeah, I hate to have this fixed mindset view, but I think it’s something that’s very difficult to teach, right? I think we can encourage creativity, but it’s something that has to do a lot with personality. I think I’ve noticed some type of personality that are, I would say, not as creative or don’t have as much taste and more rely on other indicators. So, for example, sometimes I notice it as not just my training, but in general, right, that a paper that’s published in Nature, right, or in a high impact factor journal, they sort of rely on that to be as a measure of what’s exciting and what’s good.

And others, they don’t rely on this and they have an internal perception of what’s exciting and what’s not. I think the one way that you can help is to read a lot, right? I think it’s, I always tell my trainees to read a lot. I don’t know if you remember, but in grad school, I just tried to read at least one paper a day and I would go through my RSS feed with, you know, hundreds of abstract every day. I mean, now it’s getting even harder because, well, much harder because there’s just a lot more papers that’s been published. But at least back then I had all my journals that I generally read and an RSS feed and I would go through all of the abstracts, title and abstract. I would do this every morning and I would read at least one paper that interests me.

And so I think that helped a lot in terms of both creativity. I mean, creativity is not just, you know, whether you can come up with new things, right? You can come up with new things to you, but someone might have done it. So you also have to know about what’s out there, right? And taste, I think, is somewhat similar as well, right, to creativity. If it’s just, if you like a paper or if you like a project just because it sounds good to you and you don’t know that much, then maybe someone might not call that a good taste, right? So I think these are linked together.

So the more you know, the more you’re likely to have good taste and you have to have your own sense of what’s worthwhile and what’s valuable and not just use some kind of, you know, external, I mean, what someone tells you, right? Obviously, at some point you have to rely on someone, right? So someone that you respect, someone that you know have good taste, if they like it, then you can maybe up weight something a little bit. But then at the end of the day, you need to build your own, you know, scoring function.

Grant Belgard: So let’s talk about your own career track. In your own words, how did you get here?

Yang Li: That’s a very interesting question. I did mention that I like to plan things ahead in terms of my research project, but my trajectory, I think it’s been, yeah, I’m reminded of the quote by Bertrand Russell. I don’t remember the quote, but essentially it goes like, you know, my life has been like great waves, right? Or great winds, like it blows me here and there. And I really feel that way, that, you know, there are periods of my life where that changed me a lot, right? And that has depended a lot on luck or maybe we shouldn’t call it luck, just circumstances that I guess I viewed favourably and therefore I called these luck. But it could have also been misfortune, right, if it didn’t end up very well. And I think these periods are what, these few periods are what took me here.

And the first period was during the last few years after high school. So right before college, I grew up in Montreal, in Quebec, and there’s this period called CEGEP, which is two years before university, but after high school. And at that point, I met some very good friends who introduced me to coding, but also hacker culture, not in terms of, you know, black hat, but more just, you know, coding hacker. And I remember that I started to also become interested in philosophy a lot and asking, you know, bigger questions such as what is the meaning of life, obviously, what is consciousness and all that. But really asking the question of, you know, what am I doing here, right? And at that point, I started to code and to read essays.

I think many of us have been influenced by essays from Paul Graham about, you know, being a little bit more intentional about who your friends are, who you hang out with. And I think this really has started this trajectory, right, being very humble, always trying to look for people who are smarter, who are more, you know, who are more knowledgeable than me. And so without that perspective, I don’t think I would be where I am right now. I mean, grades don’t really matter, but my grades were very average. I wasn’t particularly interested in anything other than video games, obviously. But at that point, something switched in me, right? So being a lot more intentional about how I use my time, who I’m friends with, or who I hang out with. And I think that worked out, right?

Immediately during university, I just identified people who were really excited about their work, excited about their craft. It didn’t have to be anything particular. But at that point, I majored in mathematics and computer science. And I met very good friends, again, that were really excited about the work that they did. And they were really passionate about something, right? So you can be passionate about video games, which I was about, right? But it’s very easy to be passionate about video games. It’s a lot harder to be passionate about something that’s very difficult and that no one cares about. And so I was looking for these sort of phenotype of people who really cared about mathematics, right?
Something that I didn’t particularly care about, I enjoyed, but I didn’t particularly care about. But then, you know, I started to model things that I cared about and use their intensity on these, I guess, passion, right? And so just recognizing the fact that real usable things like mathematics or coding or these things that are hard and tedious can be something that you really enjoy, right? Was something that’s quite new to me. And then, as you know, I got really interested still through my philosophical angle about aging, the aging process. And so I followed these passions and essentially all through that experience, changing from mathematics and computer science to biology, my main goal was to follow what I was passionate about and to do things as rigorously as I could.

And so essentially that led me to where I am right now, which is not studying what I initially set myself up to do, which is aging. And there’s plenty of reasons for that. But essentially following a passion that not everyone cares about, right? But I care about and applying the same fundamental values to these problems.

Grant Belgard: What’s something you learned early on that still pays dividends today?

Yang Li: Early on, as in how early on? I think one thing that I mentioned a lot to people is doing a degree in mathematics. And I don’t think that you have to do a degree in mathematics to get that. But this is where I got it, is this sense of what you don’t understand is actually very important, right? I don’t know if it’s, it’s definitely not a muscle, right? But it does feel like a muscle that you can use to know gaps in logic. And I think even, I wouldn’t say extremely good scientists, but because I do think that very extremely good scientists, they have this muscle, but I would say maybe many, many trainees and many faculty, I would say, and leaders, they still struggle with some gaps in logic. It’s very easy to jump, to have this logical jump.

And that impacts a lot of things, impacts the science, but also the writing a lot. That’s what I observe. That I think is one aspect that I see the most apparent when I see someone’s writing and I observe that there’s a gap in logic. So you just assume that, well, first of all, you assume that everyone knows what you know, but you also assume that one sentence followed the next sentence, or rather the next sentence follows the previous sentence. And I often see that there’s a gap in reasoning that I think is pretty hard to fix, right? Essentially, you have to say, well, why does it follow? And then a student might say, well, it follows because it’s obvious, but it’s actually not obvious. But how do you know that something is not obvious?

How do you distinguish something that’s not obvious from something that’s obvious? And especially when you’re talking about biology, for something to be obvious, there’s often a stack of unstated assumptions. Exactly, exactly. And in mathematics, when you do a lot of proofs, you’re sort of trained to always question every single step. And so I think doing this has really taught me, or at least it made me extremely careful about these steps. And in biology especially, I thought it was extremely useful because, well, sometimes you just can’t overcome, right? You cannot prove every single thing, right? In fact, the first few years when I transitioned from mathematics to biology, it was extremely difficult, because I was just hung up with the simplest thing, right?

But then I found utility in this because you can stash it, right? You can stash this gap in logic. So you notice them, and then you have to convince yourself that, well, it’s true that I can prove it, but it’s probably right in this system, right? And then you can move forward. But also at the same time, you understand what is this gap, right? And by understanding this gap or this condition, right, that it works only in one system, I think you start to understand the system a little bit better, and you start to understand how can this information that is supposedly only applies to this specific system also apply to another system. And so it helps me transfer some of my understanding from one paper, for example, to another paper, right?

So how might these be similar across papers or across cell types, right, or across disease transferred to another cell type or across another cell, another disease? So I think, I mean, this part helped me a lot, I think, in my thinking and in how I transferred knowledge across disease cell types or anything really. And I guess this was a little bit unexpected. I use very little mathematics right now, very, very little of the things I actually learned during undergrad. But this obviously has stuck with me.

Grant Belgard: What are some things you had to unlearn when transitioning between stages, you know, student to postdoc to faculty?

Yang Li: Right. I mean, I wouldn’t say that it’s unlearn, but change definitely very much so. All right. So when you’re a student and a postdoc, you’re very self-centered. You drive your project forward, and there’s some sense of the truth is the only thing that matters. The results are the only thing that matters. There’s less, I would say there are some, but much less personal touch, right? There’s some collaboration, obviously, but you’re really focused on your own project. At least that was my experience. And whatever I did was a lot focused on just obtaining the truth, obtaining, you know, understanding the way it works. What I had to, I would say, unlearn is maybe be less obsessed with the truth and how things should be done versus, you know, how it will be done by someone.

So it’s hard to force your way of doing things, even though if you still believe that it’s correct onto someone else who might not do the same way as you. Right. And so, as you know, we’re not taught to manage as faculty. And this is something that you learn because you see either students struggle or you see other people struggle. And then you notice that, hey, this is not, I mean, this is not productive, right? You cannot tell someone to work the same way as you did, even though you think or you strongly believe that this is how you would do things. And even if you could prove that this is the more efficient or the better way. So I think this is something that I think about.

Everyone’s different and some personalities are more likely to accept some ways of doing things and some other personalities are unlikely to perform well if you tell them to do it in a certain way.

Grant Belgard: What’s something a great mentor did for you that you try to replicate for others?

Yang Li: Well, I think all my mentors have been extremely kind. I think that’s something that, and at no point I felt like that the mentor just was using me in some ways to get a paper out, for example. And I’m mentioning this because I have witnessed that some mentors, they essentially think trainees, even though maybe they think it’s justified, I mean, using the students as a means to an end. And so I always think of the trainee as a person that is here to grow in terms of their ability, in terms of their knowledge. And so I think that’s something that I’m very careful about. I never try to have a student do something that is not beneficial to them.

Grant Belgard: Given the rapid changes in the field driven by AI, what advice do you typically give to early career bioinformaticians in navigating that?

Yang Li: Yeah, I think that’s a great question. And it really depends on your own personality. I think one aspect is to understand yourself, like what kind of personality you are. And I truly believe that personality matters a lot, right? And it’s really, some might say, oh, well, you have to change your personality. But I find that extremely hard. There are some personality traits that I know that I should change or that if I change, I would be happier or even more productive. But it’s very difficult to change, right? And so the way that I try to guide my trainees, for example, is to get a very broad sense first of what type of personality that person is. There are some, I think it was Ray Dalio in his book, he used to be the CEO of Bridgewater, and he developed these tests.

I don’t exactly remember the specifics, but I think what he did was for every personnel, every member of the company, he has this test that will classify them into what they’re good at and what are their personality. And one thing that I keep on thinking about is doers and thinkers, right? So oftentimes you can characterize someone as a doer or a thinker. The thinkers are those who like to think and then they’d like less to do. And the doers, they’re more, you know, they have a higher affinity to just start to do things before even thinking, right? So one thing that’s helpful and it’s only, you know, potentially related to personality is to figure out if you’re more like a thinker or more like a doer. And maybe you’re both, right? And that’s great.

But then figuring out these sort of traits for you will help you determine what you should focus on. And one advice was, if you think that you’re a doer, maybe you should team up with a thinker, right? And vice versa. If you’re a thinker, maybe you should team up with a doer and be very, again, intentional about this, right? Don’t let chance decide. If you have two doers, odds are that you’re just going to build a lot of things and it might not be very useful or not very good, right? If you’re a thinker, if you’re two thinkers and nothing gets done. And personality as well, it’s the same, right? And sometimes I also think about this diversity. I mean, lots of people say, oh, diversity is good, is good, is good.

But when pressed about exactly how diversity is good, they would say, you know, that the blanket statement like, oh, well, diversity, you know, you have different ways of thinking about things. I agree with that. I think you need a little bit more to really build a good diverse team, right? And this, for example, this diversity in thinkers and doers, and there’s also other personality traits that I forget to mention, right? So there are also, you know, personality traits about being very pessimistic, right? So I would classify myself as being a very pessimistic person. Careful, I’m trying to improve that, obviously. And then there are some people who are extremely optimistic. Like, you have an idea and they’re on board and then they’re like, OK, yeah, it could work because X, Y, Z.

I’m more of the, but it won’t work because X, Y, Z, right? But you need both, I think, on the team. If everyone is optimistic again, then this is going to be maybe an echo chamber of, well, yeah, it’s going to work and they’re just going to be hyped up. And then it’s great, right? Everyone feels great, but then it doesn’t, it’s not, you’re not going to have a good product, right? Because then you don’t consider what the negatives are. And if you’re all pessimistic, like me, if you have two rooms of me, then nothing’s going to work. So I think you need to figure out who you are and then team up with some diverse people, right, in that sense. And again, there’s lots of different, these are just two axes of variation.

There’s a lot more axes of variation that I think that you can optimize to build a very strong team.

Grant Belgard: What separates great collaboration partners from frustrating ones in CompBio projects?

Yang Li: As in me, is it CompBio or like two CompBio teams, or like one biological and one computational?

Grant Belgard: Yeah, probably a computational and a wet lab.

Yang Li: And a wet lab, I see. I think there needs to be respect, right? Respect for each other’s craft. If one is, again, using the other and without any amount of respect, I mean, that seems obvious, but it’s actually not. And it can go in both ways and often does, right? Yeah, yeah, exactly, exactly. So we, as a computational people, we can treat the experimental as just, you know, a pipette. Like, oh, you’re going to be replaced by robots soon, right? In the same way that the wet lab experimentalist cannot treat us as, you know, Claude Code, right? And in fact, I see it happen, right? Not every day, but I know who these people are, right? And so it’s a lot more prevalent than you might expect, I think.

And also a little bit of effort in understanding the other is, I think, at bare minimum, right? But obviously, accepting the fact that you’re not going to be as good as your experimental or dry lab counterpart. Another thing that is extremely important is that you have to enjoy working with them. Sometimes it could be tempting to work with someone who’s just very good, right? And you just need the resource. But personally, I just don’t think it’s worth it if you really don’t enjoy working with someone. Yeah, personally, I don’t think it’s worth it. The other thing is energy level. I think it’s very important to have the same amount of energy. If one of you is just a lot more excited, you end up being really annoyed that the other one is slacking off, right? And vice versa.

They’re probably going to be annoyed at you, or you’re going to find them pushy if you don’t have the same energy level. So I think these things are the main thing. And I’ve had, I would say, very good collaborators and pretty bad ones. And I think always these three aspects separate these perfectly.

Grant Belgard: What frameworks do you use when helping trainees decide on career paths?

Yang Li: Yeah, I think it also has to do with personality. Anyone who’s very curious and very open minded and maybe like more, you know, just very idealistic, I would try to push them towards academia. Anyone who is very practical and, you know, I don’t mean to say that one is better than other. Anyone who is very practical and have a very good sense of what they want in life and they don’t want to deviate too much. I would say that I would, you know, steer them towards industry. And I don’t tell them that, right? Everyone who goes through my lab, I tell them that I think that they could become good academics. But the fact of the matter is academia right now is not super welcoming in the sense that it’s just very difficult. It’s very difficult to have a tenure track position.

That being said, there’s a lot of position that is not tenure track. And if you’re OK with that, and I think you should totally be OK with that, there’s a lot of possibilities. And I would also encourage that. But obviously, you know, if you’re very creative and very, you know, idealistic kind of person and you really want to change the world or research something that you’re deeply passionate about that not many people might care about, then I still think that academia as a tenure track, having your own lab, at least, is the right place or the right path.

Grant Belgard: Final question. If you could give just one piece of advice to your earlier self, what would it be and why?

Yang Li: Other than buy Bitcoin? Yeah, I think communication is very important to focus on and be more open minded to things to improve. I think when I was young, I was really into doing hard things and technical things. I think you can call it hard skills versus soft skills. And I didn’t think at all about improving soft skills or maybe personal skills. So interpersonal skills. And I think I would give that advice to my past self, even though I strongly suspect that I wouldn’t listen to myself. Yeah, so personal skills is, I think, more and more important, especially with AI, which I think can replace a lot of the hard skills, to be honest.

And so the one who I can see that the one who will succeed a lot more than I will are the one who has the soft skills and know how to get AI to help them with the sort of hard skills.

Grant Belgard: Well, Yang, this has been fantastic. Thank you so much for joining us.

Yang Li: Great. Thanks for having me, Grant.