 As introduced, I'm Eckhard Gaul. I'm the head of the School of Mechanical Engineering here at Purdue, and started pretty much at a similar time as Bill, both academically, but also as a head, and really have been enjoying this position, moving the schools forward. But today, it's my distinct pleasure to introduce to you Professor Adrian Buganza Tepole. Adrian got his BS from the University of the Pan-Americana in 2010, then a master's in 2012, and a PhD in 2015. Both of those are graduate degrees from Stanford. So he moved to the US after that, after his bachelor. Then switched from Stanford to Harvard for a year to be a postdoc, and started at Purdue in 2016 as an assistant professor. So I like to go through these years, because you see the rapid progression of his career and how he moved through the system. So in 21, just this August, he was now promoted to associate professor after being and ranked for five years as an assistant professor, so there too, moving ahead quickly, and certainly very deservingly so. So now he is an associate professor, and he's part of our celebrating associate professor, which, of course, is really a nice event hosted by the college to really bring out the contributions of our associate professor. His research, Adrian's research, focuses on engineering mechanics with applications to medicine. So there's an overlap maybe to somewhat that she mentioned earlier, so overlap with biomedical engineering. But in particular, he advances computational mechanics methods and tools, and then makes these tools and methods available to clinicians for real-time surgery planning and execution, as well as for long-term treatments after surgical procedures, evolving skin and mechanical cues. His work is rooted in computational mechanics, incorporating experimental tissue biomechanics and 3D imaging analysis on animal and human subjects. Others have described his research work as innovative, cutting-edge, and real-world impact. For this type of research, he has attracted significant funding from various agencies, including NSF and NIH, as well as from industry. And he has been very productive, as you can imagine, to publishing, disseminating these results, considering his rapid progression in promotion and other activities. But on top of all of this, I would like to mention that he's an effective and innovative instructor. For example, he has incorporated innovative computational simulations and exercises into some of our basic courses, even like ME 270, which is statics, ME 274 Dynamics, but then later on also in the course of introduction to finite element analysis. And that's a really great contribution to the school using what you learn maybe from research to incorporate in your classes. So these curricular developments include Python coding, data visualization, and data curation. On a personal note, I would like to mention that he's always eager and willing to help the school, which is greatly appreciated if you're ahead of the school. He has served as the representative of our assistant professors, while he was assistant professors. And now he got promoted, and he had never nothing else to do than to immediately volunteer as well as being elected by the associate professor to represent the associate professors. Now, on our ME leadership team, that meets once a month. And his input there is really valuable. I can always count on him, and I appreciate a lot. So with that, I will hand it over to Adrian. Adrian, we're looking forward to your presentation. Thank you very much. Thanks so much for the introduction. I'm really humbled to be here. I'm sorry I cannot be there in person. But hopefully, I can give you a little bit of more details on top of what Eckhart has said. But first of all, I think following more of what Chihuan was saying, I think the first thing would be to really thank everybody that has supported me in the five years that I've been at Purdue. I think that is really the secret sauce. I don't think it's so much what I bring. Obviously, I work hard, but we all work hard. But I think the secret sauce is Purdue itself. I think the environment that has created for assistant professors is just perfect for success. So I'll give you a little bit of a background of where my trajectory and then more details on the research and teaching. I think that's kind of how I prepared the presentation. So a little bit about me. Originally from Córdoba in Mexico, so southeast of Mexico, you can see the little star there in the map. And then I moved for my undergrad to Mexico City. I was in Mexico City for five years. So I did my undergrad there, and then I was a high school teacher in Mexico City. So I knew that I wanted to stay in academia from that point. Then I came to the US in 2010. I did my master's and PhD at Stanford. So I was there for five years. Then I did a very short postdoc. So I moved across the country, and I was in Boston for one year between 2015 and 2016. And then I moved to Purdue in 2016, where I have been for the past five and a half years or so. So what do I do in terms of research? I would say my lab has two main thrusts. One is incorporating mechanics and machine learning. That's one big area that I focus on. And then the other one is trying to incorporate mechanics with biology for biomedical applications or mechanics plus mechanobiology. And I think those two have been pretty much the core of my research in these past five years. And typically, I've been working problems that tend to do with skins or wound healing, pressure ulcers, growing skin. And that's just because it's a really good model system. It's easily accessible. So you can image it without complicated imaging setups. You don't need MRI. Necessarily, you can do 3D pictures, for example. You can have tissues. So it's just very convenient as a model system. And that's not the only tissue that I've been working on, but I think that's definitely the one that I have used mostly as a model system. For different applications, for example, wound healing and pressure ulcers, skin growth to a growing skin, skin graft, and reconstructive surgery. So I'm going to talk a little bit about just one of them, which is the reconstructive surgery. And there is some why I want to highlight it, is because I'm kind of like Chihuahua. And I think one of the motivations for me is to try to do work that has some real-world impact. And this project I really, really like, because it's really connected to what physicians do. So for example, it is one case where you can have a patient that is going to undergo some surgery because he needs to replace that scar. And he needs to grow a skin in order to have new skin to replace that scar. So we have 3D imaging analysis to get that type of geometry of the patient before the surgery and then also during and after the surgery. And based on that, we can create patient-specific models that we can use to simulate what the formation of the tissue would be and try to anticipate where you have complications. This is a little bit of how we use machine learning to then try to replace the usual computational tools. So typically we'll run a finite element simulation like it's shown in the slide, but that usually requires a lot of computational power. So it's not particularly good for doing things like optimization or uncertainty analysis where you want to get an idea of, what's the most dangerous place or where do you expect that you will have problems for wound healing. And so replacing the finite element software with some machine learning meta model is usually good for doing that type of uncertainty or optimization problem. So here we were able to determine the regions that were more at risk and actually indeed, the two regions that are highlighted in our analysis actually were the ones that had slight delay in wound healing, but everything was all right at the end. So then we moved a little bit to sort of back from the patient-specific case to try to do some more genetic simulations. So these are common flaps that surgeons decide to use. They have names. So this one is called the advancement flap. This one is called the rotation flap and this one is called the transposition flap. And so we were interested in again, trying to replace these simulations with some machine learning meta model and then trying to use the meta model to go optimization. So that's sort of what we were trying to do here. So going from the basic designs to try to optimize the surgery and then trying to use that information and then going back to the patient-specific cases where we can do the same pipeline that I was showing before, we have patients. We know what surgery they're going to go through. We can take pictures, get the model and then simulate what the stress distribution will look like in the end. So the optimizations that we do are in terms of mechanics and that's because the mechanical state is related to complications, to wound complications. So it's not, in these simulations at least we are not solving the full couple of problems where we take into account things like inflammation or all the biology that takes place. That is something we are also doing but I would say that is a little bit more basic science at the moment. But in terms of what do we do with the patient-specific simulations because we can take a look at stress and then we don't need to worry about all these other details. We do this based on just basically stress distribution in the tissue. Yeah, and this is what it looks like and this is the actual outcome of the surgery for these two surgeries, these two cases. So that I think it's kind of the, I would say a little bit of flavor of what I do. There's other projects that I've been working on but I don't wanna bore you with the details but things like skin growth, to grow new skin, wound healing applications, doing more sort of basic progress in data-driven and uncertainty analysis of tissue and then applications in reconstructive surgery. That's about what I have for research and then I did want to say one thing about teaching because I also really like teaching and incorporating sort of creative ways of doing teaching. So what I have been doing this semester is I've been teaching the honors section of ME270. And I don't know if anyone in the audience is familiar with Minecraft, any of your kids, if you have kids playing Minecraft or you yourself playing Minecraft. And this is because in some of the service for students at the beginning of the semester, you ask them whether you're interested in, you do get sometimes some video game and I myself like playing video games. So then I thought, oh, why not bring these two together? And so with a group of undergrads, we've coded an elasticity solver, a mesh-free elasticity solver in this video game and we've been doing basically challenges to connect the concepts of 270 and 323 with challenges in this video game. So what I'm showing here, for example, is this barn trust challenge. So they had to build some roof over the barn so such that it didn't break. And then they also had to show me that they could do the analysis by hand. And then this other one that you have here is for a bridge. So that's a beam design challenge. Again, trying to build the structure and then making sure that it doesn't break. So that's one. And then the latest one, so our class on Thursday before Thanksgiving was this group challenge. So we were in the video game and we had a copy of the Eiffel Tower, kind of. And then the goal was to split them in groups and they had to do sort of the supports and then we ran the simulation in the end and then we see hopefully they didn't break. Apparently one student said it broke. I don't think it broke. I think it was close to breaking, but it didn't break. That's it. So that's what I've been doing this semester having fun with ME270. And just to end, I just want to emphasize again the incredible support that I've been having here at Purdue. And in particular, I think special thanks to my direct mentors, Thomas Sigmund, Eric Nauman, and Stuart Bolton and Eckard, the department chair. I think their support, especially toward the end to submit the tenure package and go through the process is really, really appreciated. And I don't have a list of all the people. I mean, there's a lot of colleagues and friends at Purdue who have supported me in these five years. I don't have a complete list, but I wanted to have this special mention to my direct mentors and department chair who were instrumental in the last year, especially during the pandemic and submitting the tenure package and going through the process. And more thanks, I think, to my lab. So all the students, definitely the talent in the group is what makes the work actually happen. Collaborators both at Purdue and outside of Purdue that I can list and then some funding sources. And that's it. That's all I have. So thanks again for giving me this opportunity to talk a little bit about the work that I do. So I guess I have the usual slide. But yeah, I think that's all I have. I'll stop sharing. Great. Thank you very much, Adrian. Really appreciate it. Any questions for Adrian? Maybe I can start out. Adrian, as a mechanic engineer, how do you establish contact with a surgeon to do some of the skin things that you have done on actual patients? I think just cold calling people. BME, for example, does a really good job of connecting. So doing events where the surgeons from IU come to campus or events where colleagues from BME and ME go to IU and have poster sessions or brainstorming sessions. So I think that type of effort is just bringing people together and then just having talk to each other. So I've benefited from that type of setup. Thank you. Anything in the chat? I see a question in the... Yeah, can you see it? Are there any other video games that you are interested in integrating into the classroom? Not at the moment because I don't know how editable they are. And the nice thing with Minecraft, if anybody knows Minecraft, is that it's very easy. There's a lot of sort of well-established methods to edit the game. So it's not like we are hacking the game. The game itself allows for people to code their... They are called mods, they even have a name. So that's why I decided to go with Minecraft. But no, I haven't really looked into other video games. It will be cool. I think it will depend on how easy it is to edit. Okay, thank you. Any other questions? Yes, Will. Adrian, this is Bill Crossley from Arrow on Astral. I actually do a lot of work on optimization myself. So I'm kind of curious about what kind of surrogate models are you using and what advantages are you seeing? Is it a massive speedup? Does it smooth the function? What's making an advantage for your problems? Mostly speedup. Just replacing the simulation that would take the order of hours to something that just takes maybe milliseconds to evaluate. Even if there's some uncertainty associated with the result, you can always use the meta model to do an optimization and then also find maybe points where you don't know much about the function and maybe refine. So you don't have to run as many of the costly simulations. But mostly speedup, I would say. Okay. Let's see. Adrian, what's next? That's one of the standard questions that we have here for our associate professor. What's your plan? What's your goals being in this profession? Difficult, very difficult question. Because I think the short-term challenges are pretty clear because there's research projects that are moving on. I think definitely tackling those. I think I will have a little bit harder time defining like a 10-year or 20-year plan at the moment. I think one plan is that, is to try to figure out more longer term now that I have gone through this first stage. But I think one thing is clear. I definitely want to have impact in real-world applications. So kind of like Ji Huang was saying, I think that is definitely a big motivation to plan things around. And of course, getting promoted to full professor. That's always next for every associate professor. But maybe the last question. What's the background very quickly? This is a new collaborative effort with Dave Humilis, who's the PI on this Institute proposal that was awarded to Purdue and other institutions. But Purdue, Dave Humilis is the PI. It's called the Embryo Institute. And this is work that I'm really excited to continue. So this just got awarded this year. And it's about integrating information from different species, different scales, particularly around morphogenesis type of problem, which is what I'm interested in. And how do we use computational models to do that integration of systems that maybe plans to mammals, to fish. So how do you tie together the rules of life question? And that's kind of what the institute is about. And this is the logo of the institute. Very cool looking. Adrian, thank you very much. Really appreciate it. Thank you. I'm always amazed by the talent that we have at Purdue. So thank you very much for coming for today.