 It is now my great pleasure to introduce Professor Elias Billionis. He joined Purdue also in 2014, same time when Marcial came, and has quickly established a predictive science laboratory, PSL, as he calls it. His research spends the general interdisciplinary space of design under uncertainty, spending a range of socio-technical systems. His research works is based on exploiting physical models to inform statistical and machine learning techniques in order to overcome inherent limitations of engineering systems due to the high cost of information acquisition and limited number of observation. His research work establishes new directions at the intersection of machine learning and artificial intelligence with engineering systems. His research has also been funded significantly by governmental organizations like NSF and NASA and DARPA, but also by industry, in particular, Ford and Facebook and some others. He's a natural collaborator who's able to make diverse contribution and add value to a wide range of research programs. I think that's really very unique about him. He's truly an interdisciplinary or cross-disciplinary researcher for us in the school with many different collaborative efforts. In addition to all of his research, he has proven himself as an excellent mentor of his graduate and undergraduate students. He was presented with the Outstanding Faculty Mentor in Mechanical Engineering Graduate Students Award and has engaged in numerous undergraduate or has engaged numerous undergraduate students in his research activities and given them opportunities through SERV as well as our Bottomley Fellowships that we have in ME and undergraduate research assistantships. So, furthermore, his teaching efforts highlight a significant commitment to developing state-of-the-art educational models, effectively integrating novel educational technologies and tools with the fundamental tools. As a result, he has been recognized as an outstanding engineering teacher three times by the College of Engineering. I would like to mention here on the teaching effort at the end also that he's instrumental in developing our data science, big data, data analytics course for our ME students currently, which is a major undertaking. So, with all that, I would like to hand it back to Ilias Elias. Sometimes I'm moving back to the German pronunciation. I'm sorry about that, but I really like to welcome him here and I'm really looking forward to his remarks. Thank you, Eckhart. And we're happy to be here, particularly with, during the same time as Marcial. Marcial was probably the first friend I made with the Mechanical Engineering Department. And so I'm really, really happy to be honored at the same time as him. Congratulations, Marcial. All right, so let me get started. I'm going to tell you a few things about myself so that you get a feeling of who I am. And I would also like to take this opportunity to honor a little bit where I'm coming from. So I'm coming from Greece, and in particular, this little town a little bit outside of Athens. It's called Aspropergos, White Tower. It's like 20 kilometers outside of Athens. And it's an industrial hub. It has basically basic, how do you call it? So oil refinery is mostly and still manufacturing plants. And it's really, it's actually a really bad part of Athens. But it looks beautiful from away. And you see it has nothing to do, nothing to do. It doesn't resemble Indiana at all. So there is a beach and there are mountains on the background. And it's always sunny, but it's also a little bit smelly because of the oil refineries. My mother is coming from the north of Greece, a town called Thessaloniki. So you can see at the top. My father is coming from the middle of the Paloponnes from a little village called Lagadia. And this is where I go when I go to Greece during the summer. I basically go to that little village and I do my work from there completely undisturbed from it. The village has about 100 families living there. And this is my favorite Greek island. I'm not going to tell you which one it is because I don't want you to go there. But if you're really interested in knowing, you can send me a personal message and I may tell you. Okay. So this is how I was educated, I started in Athens at the National Technical University of Athens. And I started applied mathematics. And to be honest, I started applying mathematics because in high school, I didn't know what to do. And I didn't know what mechanical engineering was. I didn't know what civil engineering was. I didn't know anything really. And applied mathematics seemed like not making a decision. So it was a little bit out of luck that I picked it and because I didn't want to make a decision. And continuing not making a decision, I also did a PhD in applied mathematics at Cornell. And initially I went there, wanted to study finance. But my arrival to the U.S. coincided with financial mathematics in particular. And my arrival in the U.S. coincided with the crisis of 2008. And there wasn't a lot of excitement back then for financial mathematics engineering. It was actually blamed quite a bit. So I started experimenting with more engineering products. I was good in probability and statistics. And I happened to talk with my later PhD advisor, Professor Zobaras, who helped me understand about how you can apply what I knew about probability in engineering systems. I like that a lot. So I decided to do my PhD on that. Then I worked in the intersection of engineering plus statistics or physics plus statistics at the mathematics and computer science division. And I finally came to Purdue as part of a cluster higher on predictive science and engineering. So I came to Purdue to actually do collaborative work. And that's exactly what I've been doing so far. These are my intellectual heroes. And so Von Neumann, for various reasons, mostly formulation of game theory and the groundwork on decision making. Richard Feynman, one of the best teachers in all times, a person that I listened to on my walkman, on my bike, during my high school years, I listened to his lectures. Edith Zanes, one of the pioneers of the maximum entropy principle. I love doing one of the pioneers of AI. I.J. Good, my favorite statistician. And Judea Pearl, who's a person who has formulated causal inference. And these are some of my favorite books. I like to read a lot. I don't have a lot of time anymore, mostly because of my toddler. But I like to read a lot of history and focusing on particular prehistory. And I also like biology. So one of my favorite books on biology is the selfish gene variants of dockings. And pretty much all of which are dockings. All right. So what is the mission of my lab as a so-called predictive science lab? In one sentence, it is to create artificial centers and technologies that accelerate the pace of engineering innovation. So I want to help engineers do their job faster without having to do the dirty work of programming stuff and basically accelerating what, the way they design things. Okay. And the way they bring data into whatever it is they're doing. Now, this is my philosophy. And this is the backbone of whatever I'm doing. So I developed communication channels between physics and data. So yes, I'm doing data science and mostly learning, but I'm doing it in the context of a physical problem. So I'm using the physical equations, the PDs, partial differential equations, ODs, or other physical equations. And this is all done under the following communication protocols. So there's probability theory, which I think of as an extension of logic as the language of science with an additional layer of causality expressed either implicitly through the physical laws or through graphical models. And I use model machine learning techniques to basically represent certain of the quantities that appear in whatever we're doing. All right. These are my overarching research themes. You'll see later on many projects, but these are the core problems I'm working on. So there's quite a few things under the category of theory in foreign machine learning, high dimensional and certain quantifications. So when you have a model that has a parameters that are uncertain and these parameters are high dimensional, think about, let's say, not knowing an entire function. An entire function is an inherently high dimensional quantity. So how do you quantify your uncertainty about functions? How do you propagate it through the rest of your physical model? Filtering a calibration, you are observing part of a dynamical system and you want to infer the entire state of the dynamical system. Perhaps there are parameters you don't know about the dynamical system which you would like to calibrate. This has applications in control, digital twins, sequester design of experiments and simulations. So you have a fixed budget to do a certain number of experiments or a fixed computational budget and you want to design your simulations or experiments in order to achieve a certain goal, like maximize something, estimate the probability that something happens and so on and so forth. I design algorithms that guide you into the selection of these experiments. For detection, diagnosis and prognosis, you have a system that can break down a certain way. How can you, by looking at central data, figure out when something has gone wrong and make predictions about how much time you have until you really have to fix it? This is very similar to the filtering calibration but it has some nuances added to it. This is one block of things I do. The other block has to do with modeling human behavior and I'm really talking about modeling the human as a disturbance in an engineering system. In particular, in the context of my buildings applications, I developed models of humans interacting with the lighting system or with a thermal system. Humans making decisions about the thermal start-set point, for example. And I'm also interested in humans as decision makers inside an engineering system. So once you increase the complexity of your system at a certain level, you're going to have to introduce humans because the current state of the AI does not allow for full autonomy. So you're going to have to bring humans to close the loop and have them make the difficult decisions. So how can you deal with that? So this is another view of the projects I have ordered from more physical to more human and we're not going to go over all of these. I'm just going to mention briefly at the very top, we have basically physics, design of materials and as we go down, we go to a little bit of engineering systems, electric engines, combustion engines, biomedical applications and we go to even more complex systems like extraterrestrial habitat projects about which we're going to talk about and smart building projects. Of course you may ask yourself, do you really know all that stuff? No, I do not know all that stuff. So I'm not an expert in pretty much any of these fields. What I'm an expert on is on bridging the gaps between physics and data. So I have developed the skill to understand the physics in a wide array of fields and I can help people connect with data and I can help them formulate decision-making problems and I can help them quantify uncertain DNA models. All right, I have 35 current Purdue faculty collaborators which says a lot about the way I like to do my job. I have, I'm collaborating, I have at least 14 from the mechanical engineering department. I'm working with people from electrical, civil, aerospace. I have written proposals with many more. The good thing is that we haven't won all the proposals otherwise we'll be in trouble in terms of the amount of time we have to carry out the project. And the two projects that I would like to mention is to give you a more concrete idea about how I'm involved. So the first thing is a smart and connect communities project funded by NSF where I lead the data science and mechanism design efforts. So the goal of this project is to go to communities, low-income communities that are, some of them are subsidized and to design a thermostat, a smart thermostat that gives them information, gives them feedback that incentivizes these people to reduce their energy consumption. And the idea here is that the amount of income these guys spent on energy is so significant that even a little bit of savings will have an impact on their quality of life. So what I do is I work on the part that designs what sort of feedback we should give them back. And this is a mechanism design problem, mechanism in the sense of game theory. We try to find which incentives maximize a community goal while at the same time the individuals are acting sort of selfishly and maximizing their own utilities. And the other project I would like to mention is the resilience, extra traditional habitats project where I lead one of the three thrusts, the awareness thrust, which is responsible for using the sensor data to develop an awareness about the state of the habitat, where is it right now, how likely it is that things have been broken and what are the actions we should take next to mitigate any issues. Now let me motivate a little bit this latter part and what exactly I mean by developing awareness, but I'm gonna touch upon what are the issues that we're trying to address in the next five or 10 years. So I'm gonna use an example for that. Am I running out of time? No? Okay, because I saw you turn on your camera, I was a little bit... We have a little bit more time. Go ahead, but yeah, sorry. Let's say I have one minute. Okay, how about that? No problem, no problem. All right, so let's keep this completely. Okay, if you're interested about learning more specifically about this project, please reach out to me. I'm going a little bit more slowly than I originally anticipated. I want slide nine, if you see. I just went very slowly. Okay, so I just want to mention my graduate class ME539, it's called Introduction to Scientific Machine Learning. So far, I had 350 graduate students taking this class. So this is Data Science for Engineers. It's basically specifically using physical problems to teach Data Science. That's the difference between my class, for example, Stanley's class. I don't go as deep as Stanley. I try to connect to the level of my, I'm assuming my engineers know about difference equations, and they don't know about probability summits or optimization. I'm also developing the undergraduate Data Science class. All right, I want to thank my students. These are the people who did all the work, past and current students. I want to thank my mentors. And these are not all my mentors. The very top are the Greeks. Our first one, I'm allowed to talk about probability, which we like, is told me about Bayesian statistics. Zabaras told me how to do basically a faculty job. Zites is the first person I wrote proposals with. Carava is the first person I had successful proposals with, and we're continuing our collaboration in a full-blown way. Professor Dyke, my main mentor, she told me a lot about how to do the job, and also about how to mentor students, and by watching, Rhodes is also my other mentor, by watching him teach, I improve myself considerably. And finally, I want to thank my family, my grandparents, my father and my mother on the right here. My little brother, he's three years younger than me. You see us right down at the bottom, who taught me how to tolerate people that are different. My brother is gay, and he helped me a lot to understand a different perspective. So I knew that he was different ever since we were in this picture together, ever since I was six or seven years old, and it was great growing up with him and what's in him develop into the man he is right now. And of course, my family, my wife and my son, without my wife, I don't know if I could have done anything. C&I, we managed to be together from a distance for more than six years, here in Greece, me here in the U.S., and it's been a great journey, and really without the stability that C&I had provided to me, I wouldn't be able to accomplish it. All right, that's all. Thank you very much, Elias. Wonderful presentation, wonderful remarks. I love the personal touch. Any questions from the audience for Elias? You can either unmute yourself or write something in the chat room. I can start out. I would be interested to better understand how you model humans. I'm kind of a thermal systems engineer, and I model thermal systems, right? We have some basic characterization in form of first principle to model the equipment, then some environmental inputs, and we get a performance of the system. But how do you model humans? That seems entertaining to me. There is a first principle formulated by von Neumann in the 50s. So the principle is people maximize their expected utility. So they have some sort of, they have some goals and some preferences, and these preferences are expressed as a function over their choices, and they try to maximize that objective. Now the problem is that you need to go a level above that because do people really know what are their preferences? Of course, even if they do, can they really maximize the objective? And you relax this a little bit, and you go into Simon's approach, which is that they don't really maximize it, but they are satisfying in the sense that when they find something that's good enough, they just make the decision. So they have an objective, they have some preferences, they don't try to maximize it perfectly, but if they find something good enough, they make a decision. And that can be expressed mathematically in the language of probability theory, and that's how we do it. At the end of the day, it's a matter of whether or not it matches the experimental data. It's a model, just like your thermal science models, and I could say that it does match the data sometimes, and there are a lot of examples where people deviate from this behavior. Great, thank you. Any other questions or comments we would like to chime in here? Yes, Avin, go ahead. Non-technical question, but I did want to comment that Elias put me on to a very nice wine, and I've been going back to it. So he's got a very nice, but also effective taste in wine, so I really appreciate that. Great colleague as well. I have some excellent suggestions for Greek wine that I haven't discovered. Not Redsina, I hope. Not Redsina, no. Okay, good. I'm not going to mention them here, because they're a limited supply. So the thing is, you've got this favorite island that no one knows about. You've got the favorite wine nobody knows about. What other secrets do you have on the question? I cannot tell you. You should be careful with things like that, because islands can be crowded very, very easily. So you can go to Sundarin if you want. Okay. No, no, no. Too many people there. No, no, no. I have one more question for you that may be helpful to others who are on the call. How do you manage collaboration with 35 different collaborators across Purdue? And I mean that in a serious way. Actually, I'm going to talk to Arvin about it. We might have a, should start an award for like the most type of collaborative person in the college. So you're probably on the top of that list. But I think, right, we have all kinds of different characters, mentalities, and you do need to manage that. And I think there's some logic to that as well, right? There is. So you need to spend the time to understand their application and their perspective. So I do that. I will, when I work with, for example, buildings, I'll sit down and learn about buildings. I'll learn about the HVAC system, heating and cooling, the equations they use. I'll learn about how they design the experiments. I spend the time to do that. At the same time, I'm interested instead of just doing my own stuff and write my theory papers, I'm interested in solving their problems. So I'm like, I don't count the collaboration with the technique that I want to apply. I want you to tell me, what is your problem? What is your problem? And then we solve your problem. That's my, that's my attitude. Now this has put me a little bit back on my, let's say theory endeavors. By the same time, I feel really happy solving actual problems, which is something I didn't do before. Because you know my theory papers, examples are two examples.