uh it is now my great pleasure to introduce uh professor elias billionis he joined perdue also in 2014 same time when martial 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 spanning a range of socio-technical systems uh 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 with many different collaborative efforts so in addition to all of his research he has proven himself as an excellent mentor of our uh of his graduate and undergraduate students uh 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 a surf as well as our bottomley fellowships that we have in me and undergraduate research assistantships um so far more 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 and as a result he has been recognized as an outstanding engineering teacher three times by the college of engineering um so uh i would like to mention here on the teaching effort at the end also that is instrumental in developing our data science big data data analytics course for our me students currently which is it's a major undertaking so with all that i would like to hand it back uh to elias elias i uh sometimes i'm i'm moving back to the german pronunciation i'm sorry about that but uh i really like to welcome him here and i'm really looking forward to his remarks thank you eckhart and uh we're happy to be here particularly with uh during the same time as martial martial is uh was probably the the first friend i made in the continuing department and so i'm really really happy to be uh honored at the same time congratulations marcel all right so let me get started i'm gonna tell you a few things about myself so that you 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 particularly this little town a little bit outside of athens it's called us proper ghost white tower it's like 20 kilometers outside of athens it's an industrial um hub has basically uh basic how do you call it so oil refineries mostly and still manufacturing plants and it's it's really it's actually really bad part of offense but it looks beautiful from a way and you see it has nothing to do uh nothing to do to it doesn't resemble indiana at all so there is a beach and there are mountains on the background 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 as you can see on the top my father is coming from the middle of the peloponnese from a little village called lagallia and this is where i go when 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 anyone the village has about 100 families living there and it's my favorite greek island i'm not going to tell you which one it is because i don't really want you to go there but if you if you're really interested in knowing you can send me a personal message and i may tell you okay so this is uh how i was educated i started in athens at the national technical university of athens and i studied applied mathematics and to be honest i studied applied 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 it was a little bit out of luck that i picked it and because i didn't want to make it easier 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 with a crisis of 2008 and there wasn't a lot of excitement back then for financial engineering it was actually blamed quite a bit so i i i started experimenting with more engineering projects i was good in probability and statistics and i i happened to fight my talk with my uh later phd advisor professors of virus who helped me understand about how you can i can apply what i knew about probability in engineering systems i liked that a lot so i decided to do my phd on that and then join argon again 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 to purdue to as part of a cluster higher of on predictive science and engineering and 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 for neumann for various reasons mostly uh formulation of game theory and the the groundwork on decision making richard feynman one of the best teachers of all time the person that i listen to on my walkman on my bike during my high school years i listened to his lectures he did zayn's uh the pioneer one of the pioneers of the maximum anthropic principle alex turing one of the pioneers of ai ij good my favorite statistician and that pearl who's the 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 so we're focusing on particularly uh prehis prehistory and i also like biology so my one of my favorite biology books and biology is the selfish gene variants and documents and pretty much all of richard duncan's book all right so what is the mission of my lab at the so-called predictive science lab in one sentence it is to create artificial intelligence 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 develop communication channels between physics and data so yes i'm doing data science and i'll say learning but i'm doing it in the context of a physical problem so i'm using the physical equations the pd's partial difference equations ods or other physical equations and uh this is all done uh under the following communication protocol 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 is either implicitly through the physical laws or through graphical models and i use modern macedonian techniques to basically represent certain of the quantities that appear in whatever we're doing all right these are my overrides 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 form of scene learning high dimensional certain quantification 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 uh you you are observing part of a dynamical system and you want to infer the entire state of the dynamical system perhaps there are parameters if you don't know about the dynamical system which you would like to calibrate this has applications in control digital twins sequential design of experiments and simulations so you have a fixed budget to do a certain number of experiments or fixed computational budget and you want to design your simulations or experiment 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 fault 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 and so this is one block of things i do the other thing the other block has to do with modeling human behavior uh and i'm really talking about modeling the human as a disturbance in an engineering system so in particular in the context of my buildings applications i develop models of humans interacting with the lighting system or with a thermal system uh humans making decisions about the thermostat 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 make have them make the difficult decisions so how can you deal with that so this is an overview of the projects i have ordered from more physical to more human and we're not gonna go over all of these i'm just gonna uh mention briefly at the very top we have basically physics design of materials and as we go down uh we go to a little bit of engineered systems electric engines combustion engines biomedical applications and we go to more even more complex systems like extraterrestrial habitats projects around which 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 okay 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 theory and data right so i have developed a 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 uncertainty in their models all right i have 35 current purdue faculty collaborators which says a lot about the way i like to do my job uh 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 will be in trouble in terms of the amount of time we have to carry out the projects and the 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 connect communities uh project funded by nsf where i lead the data science and mechanics 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 because the amount of income these these guys spend 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 on the part that designs what sort of feedback you we should give them back and this is a mechanism design problem mechanism consensus of game theory we try to find which incentives uh 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 resilient extraterrestrial habitats project where i lead one of the three thrusts the awareness trust which is responsible for using a sensor data to develop an awareness about the state of the habitat where is it right now uh how likely uh 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 of 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 in the next five or ten years so i'm gonna use an example for for that am i running out of time no okay because i saw you you turned on your camera i was a little bit uh uh we have a little bit more time uh go ahead but uh yeah yeah okay sorry uh 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 want if you're interested about uh 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 if you if you see i just went very slowly okay so i just wanna mention my uh graduate class me five three nine it's gonna it's called introduction scientific machine learning uh so far i had 350 graduate students taking this class so this is data science for engineers it's basically uh 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 uh i try to connect to to the level of my i'm assuming my engineers know about different equations you know they don't know about uh probabilities of mods 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 the greeks professional one of allah taught me probability cultural laggies taught me about bayesian statistics zabaras told me how to uh do basically the faculty job zitas is the first person i wrote proposals with carava is the first person i had successful proposals with and we're continuing uh our collaboration in a full-blown way professor dyke my main mentor she taught me a lot about how to to do the job and also about how to mentor students and by watching is also my other mentor by watching him teach i improve myself considerably and finally i want to thank my 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 you see us right down at the bottom who who taught me how to tolerate people that are different my brother is he is gay and he was uh he he helped me a lot to to understand a different perspective so i 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 uh great growing up with him and watching him develop into the monkey 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 she see my she and i we managed to be together from a distance for 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 it provided to me i wouldn't be able to accomplish anything all right that's all great thank you very much allah it's a wonderful presentation wonderful remarks i like i love the personal touch any questions from the audience for allies you can either unmute yourself or write something in the chat room i can start out uh right i would be interested to better understand how you model humans right i'm a kind of a thermal systems engineer and i'm 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 uh seems entertaining to me there is a first person it's the first person formulated by phone norman in in the 50s so the principle is people maximize their expected utility right so they have some sort of uh they have some goals and some preferences and these preferences are expressed as a function over their choices and they try to to maximize that objective now the problem is that you need to go a level above that because you know do people really know what are their preferences question mark can they even if they do do they really can they really maximize the objective uh 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 satisficing in the sense that when they find something is good enough they just make the decision okay so they have an objective they have some preferences they don't try to maximize it perfectly but they find something good enough they make a decision and that can be expressed mathematically in the language of probability theory uh and that's how we do it and at the end of the day really i'm not it's a matter of whether or not it matches the experimental data it's a model just like your thermal science models and you could say that it does match the data sometimes and there are very a lot of examples where people deviate from this behavior great thank you uh any other questions or comments who would like to chime in here uh if yes alvin go ahead non-technical question but uh i did want to comment that uh you know elias put me onto a very nice wine and i've been going back to it so uh you know so you know he's got a very nice um but uh also effective uh taste in uh in wine so i really appreciate that uh great colleague as well i have uh some uh excellent suggestions for greek wine that i have discovered not not rethina i hope not red cinnamon no no no okay good like affordable i'm not gonna mention them here because they're in 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 uh what other secrets do you have i cannot tell you you cannot you should be careful with things like that because islands can be crowded very very easily so you can go to santorini 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 right how do you manage collaboration with 35 different collaborators across purdue uh and i mean that mean that in a serious way actually i i gonna talk to alvin about it uh we might have a should start an award for like the most type of collaborative person in the college or so you're probably on the top of that list but uh but i think uh right we have all kinds of different characters mentalities and you you do need to manage that and i think there's some some logic to that as well right there is so you you need to to spend the time to understand their application and their perspective so i do that i will when i when i work with uh caravan for example on buildings i'll sit down and learn about buildings i learned about the hvac system heating and cooling the equations they use i learned about how they design their experiments i spent 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 a collaboration with a 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 lot 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 in all my theory papers the examples are two examples you
2021-05-02