So students, good afternoon. This is one of my most favorite topics, user-centered design. We hear a lot about user-centered design when it comes to online platforms, websites, online buying platforms when you have these online shopping portals because it immediately translates into business. When somebody comes to your site and if it's very easy to use and he's bought something on your site, if it's too tedious, they won't buy. So coming from that angle, when I go and see my products which are around me, a lot of products are extremely tedious to use because of various reasons. Some reasons could be that they are because of legislation. Some reasons could be because of material properties. And the people are not looking at how to make these products very comfortable to the user. So I'm talking about a physical product where helmets, which are used on two-wheelers. So let me tell you a small story. I had this student, Mandar Kale, in 2007, I think. And he came late for a product design course. That is, we have a P2 project where he was late for a week. Then I asked him, you know, Mandar, why are you so late? And what's happened? He said that his friend died in a motorcycle accident. And the motorcycle helmet was trapped to the back of the motorcycle. This was in Pune. And he was very disturbed because he was just saying that, if only, you know, my friend would have put on that helmet, he wouldn't be dead because he had a head injury. Head-on collision with the truck in the front because the truck did a sudden break and he was going at, you know. So then, you know, it stuck me that Mandar, if you're so concerned, can't we design a product where you're encouraged to use it?  Can't we do such a thing? Why are helmets so uncomfortable? And then we studied, we found out so many new problems with helmets and so many deaths, 140,000 deaths, you know, in the year 2015.  But I'm just saying, why should the policemen ask for fines when you're saving your life? So this is the whole challenge we started with in this journey of designing a helmet which will be extremely comfortable. But if you have an extremely comfortable helmet, will it be unsafe? Need not be very good. Will it not pass Indian standards? No, it has to pass Indian standards. Who is talking about today? We're talking about technology, materials and processes and then we have to make technology, we have to make materials and we have to make our engineering process suit the user, right?  So we started with that premise and we said, we have to look at the situation around us. Then this is one of my, you know, project associates in our design studio, Mr. Chari. He worked with us, he was also an MDS designer and he said, sir, it's so complex.   So we found it very, very challenging. So from there on, we started and we said, let us understand our culture, let us understand the local situations and habits and let us also look at the socioeconomic condition. If the helmets are too expensive, nobody will buy, right? The helmets have to be, you know, also looking at who is using the helmet for what purpose. So here we come down to a very important, you know, topic in design, which is product planning. You need to have that variety of products which are suitable for different, different type of people and it's a myth when we say that I have designed one product and it'll be good for everybody. I take a good example of your spectacles. Everybody uses a different spectacle. Let it be style, let it be convenient, let it be, let it be a type of choice you have. Similarly, you need to have helmets which are, which are that type of variety. So that you can choose the right helmet for your requirement. And we looked at, you know, condition in India, become the largest tubular manufacturer, we're still growing. 69% of the total number of motor vehicles are motorized. Injuries to the head are the main cause of death.  You won't see any injury outside, but the person is dead. So these are the challenging things we were facing. And we're saying that anyway, helmet is the most effective way of reducing these injuries and the majority of tubulars, anybody we interviewed we found out that they just don't want to wear a helmet. Then we went and started studying their market and our situations and people. People is a very, very important study for us. And then we were watching the type of risks on the road, risks are too many. What's happening when you're stuck at a traffic junction, the helmet gets very hot. Let us look at the construction of the helmet. What do you think saves the person's life in all these three parts of the helmet? The shell, the thermocol, which is the expanded polystyrene foam and the PU padding inside. Out of these three, which is the one which saves the life? The thermocol. Thermocol. And that doesn't cost anything, right? Thermocol is the packaging. This thermocol has a special property of actually reducing the acceleration of the head, the brain inside when it's going to hit the head. And that type of compression loading and we're not able to get in any of the material till now. We tried a lot of options. We're not able to get that type of compression where you get that type of cushioning so that the brain doesn't go and hit the head. I must tell you a nice story over here. In Delhi, we were given a big task by the transport department and they told us, you know, please check out whether these roadside helmets are good or not. They're selling at 400 bucks, 600 bucks, 200 bucks and they may be very dangerous for the people who are buying them. Then we took all the roadside helmets into our, we have a very good biomedical facility in IIT, Delhi when I was teaching there and you know, Professor Dinesh Mohan was in charge. We got these studs helmets, which are the best in the grade. Then we got the other brands, four other brands and we bought four local roadside helmets and we put them on the testing machine with sensors and everything. And to a shock, the roadside helmets were performing much, much better, 20% to 40% better than the studs helmet. What could be the reason? More thermocol and another interesting issue was that shell, which we thought was the most important would crack in the other helmets and break, but save the life of a person. The roadside helmets didn't use that much resin as studs were using. So if you use a lot of resin, the shell is very hard. It takes all, when it goes and hits the floor, what happens? It absorbs less energy, whereas the roadside helmet shell would go and crack. What happens when something cracks? It uses all the energy and it was much, much better. That was a shock for studs. Their thermocol, the studs, when the thermocol in the studs helmet was actually 18 to 19 millimeters and sometimes it was curvilinear because of the style and all, he had to make it in some grooves inside. Whereas the outside helmet, no fancy design. It was straight forward round shell of 22, 23 millimeter thick. That was a minimum, you need minimum 20 millimeter thick for saving the life of a person. It was 20 to 24 because it was just because of lack of things and you want to make a simple helmet. Their EPS form, which is the expanded polystyrene, was thicker and a uniform cross section. So it did very well in them in the field. So that gave us the impetus that we need to do something very drastic in making people aware of this as well as see to it that people wear helmets. So then we said, let us study all the materials available. We will use Kevlar bulletproof, very, very high end. And then we also checked out other materials like lexan, lexan is a polycarbonate material. Polycarbonate is very strong again. Why should I study these high end materials when I'm designing a helmet for a two wheeler rider? I'm not going to use them. But by studying the materials, we study the property. We're studying the properties. I can come up with ideas for better use of material in my context also. So we always do that. Whenever we have some study, we'll study parallel products, whatever materials they're using. So they can come back and do multiple work. So here then we also looked at ABSPC. This is a styrene material which is used in all plastic scooter parts. And ABS is a very good, ABS is acrylonitrile butadiene styrene which is very easy to paint. It's injection molded.  That is the type of impact. And it's pretty severe. We don't realize it. And 45 kilometers an hour is nothing. Most of us go at 60 to 80 kilometers an hour. And if that is the speed, it's like falling from the terrace of a five-story building. There's no chance of survival. But after the collision, it drops. Here it is adding on. But if I come with a, you know, a standstill truck, then my 45 kilometers become around 35 to 40 kilometers.  Because sometime a shock gets absorbed by the vehicle and then the movement of the body, all that. So it reduces a little bit. So, but then you just, you can imagine that if the speeds are much higher, it could be very, very fatal. And that's what we see outside. So many deaths because of that. And that perception is not there. That is a challenge for us. And then we went and studied the standards. And a very interesting cartel happens with the standards. In the standards, we have professors, we have material experts, and we have also industry representatives. And unfortunately, they are completely biased towards manufacturing. They are biased towards technology. And they're not having any context of user convenience. And then all the helmet standards are coming from cold countries. They, they take in the British standards and they brought that down. And all Europe, where do they use motorcycles? Racing, mountains, going. So for them and whatever weather. Very cold. So what happens if we borrow the standards from them? You'll get, your head will get cooked in India. Literally. And that's what happens in Delhi. When the temperature outside of 47 degrees and you're wearing a helmet on your head, you just can't survive. It is so hot inside. So we went and told, we then met the Indian standards and we told them, this is not right. You have to have a person who is responsible for comfort of a person in your committee so that they look at the comfort and then introduce all the new norms. And then we did a lot of observations of people's problems of how they store their helmet, how difficult it is to buckle and why people don't buckle their helmet. You know that they don't buckle the helmet. Then what happens, tell me? The helmet flies somewhere else and you hit the head, hit. So it, and we found out that at least 20% of the accidents are happening. People are wearing helmets and not buckling. It's happening because of that. That's also very, very serious concern of buckling up. So here we're looking at, it gets very hot and muggy.  So the air is blown inside the, inside the face or interaction between two people, extremely difficult when you wear a helmet. So that's a serious issue. How will you improve that? Then, you know, it restricts vision is a very, very clear thing. Your helmet sizes are so big, sometimes it restricts the vision from the side. So this is the most important thing of our design process, the product brief. Here we're saying that the helmet should be easy and convenient to carry and store. Should encourage and stimulate you. Provide ample ventilation, should be lightweight. Helmets are very heavy. That was a very important component which people did talk about. Should fit, you know, aptly on the head without wobbling. A lot of helmets would be loose. And then provide clear vision and facilitate hearing and occupy less volume when not in use. So these are the briefs. So when you have a brief points, you want to now sort of work on these briefs and make multiple ideas. Each problem should have an idea, right? Multiple ideas together will make a concept, right? Multiple concepts will compete with each other to get selected as the best concept. And the criteria of our selection will be the product brief, got it? So let us see how this whole journey went. What is this doing? Buckling. Because buckling by the side. You just move these levers and the helmet is not coming out anymore. And when you're doing ideas, remember? No questions asked, right? You did the creative brainstorming. So when the idea is there, respect the idea. Yeah, good solution. Wow, I love this idea. This is a scarf idea and a lot of pillion riders, women riders, would say that we are much more comfortable scarfing rather than buckling. Because buckling, a lot of people have fear that the buckle will not come out. Sometimes it rusts and the buckles are very difficult to pull out. So scarfing is a very common way of time. And then tell me, if I put a scarf, what will Indian standards say? It's not secure enough, very good. And what will they do? They will test the knot in the testing machine. They'll pull and check, right? All that can be done by getting the type of fabric. But if you want to have an idea to go to the next level, you can always do whatever you want. This is an interesting sweat ring on the top. Because too much of sweat is coming. So there's a ring on the top for sweat. Another idea, again, Mandar only made all these ideas where you are receding sideways to make the helmet very slim and small. Again, two-part receding helmet.  This is, again, a collapsing, you know, self-collapsing helmet. It is like all around on the top, it's got a net. Most of the protection happens on this side. This is, again, an interesting idea of swivel. You have two shells, you swivel and you lock. It becomes very slim. All storage ideas, right? Side folding, right? This has been inspired by the shakes who wear this, you know, routering cap with the cloth. So it's been inspired by that and it's like, you know, ease of wear idea of it. Just should be as easy as wearing a cap or a, you know, with a ring on the top. So there's windows on the helmet for letting air inside. So when you're in the signal, you can actually open the ventilation ports or they can wing out the helmet so that you get the breeze needed. Then, you know, inspired by various insects, different, different ways of sort of air ventilation so that your air, you know, is very, very ventilated inside. Again, different type of dimples so that your air channels are formed. So your head is cool because of the venturi effect. Slits from the sides if possible. This is the helmet with padding on the outside. Remember those people sitting next to each other on a talk, the helmet actually hurts quite a bit. A lot of people complain that they would dash against the helmet when the billion rider wants to talk to the front because of the very seat. So the padding on the helmet. So each problem, you know, to come with an idea. Biggest challenge is pollution. So much pollution that if it's an integrated helmet with pollution mask, it can be fabulous. Again, if it's easy to store or very good so we can think of a collapsible helmet and then these are only very few ideas issued. We had some 150 ideas. I'll show you some of the clusters. So what happens when you have a lot of ideas like this? You very clearly we're coming down to the first three points which talked about comfort of use, encouragement of use and ventilation. So automatically when you cluster ideas these type of situations will come forward to you. So we had a ventilation cluster that is I put all the ideas which talked about ventilation as a ventilation cluster. I put all the ideas on the board. We actually do a lotting, which was a storage cluster, easy to store cluster and all the ones where it's easy to wear to talk about motivation and less resistance to use a helmet that we put in the ease of wearing cluster. Look at this ventilation cluster. This was put in this cluster because it has chance of ventilation also. This was going into the folding cluster. So you can actually bring a couple of ideas together. Look at the window idea, look at the gaps, look at the ventilation. A lot of ideas are put together and then what you do is you actually use all those ideas. For example, these slit ideas were also used. The louver ideas also were used inside for the thermocool. The ideas where you are opening, remember that side openings were also used. So when you build an idea, you use the other ideas as your inspiration because you cluster them together. Then we chose that as the champion idea. So how will that happen? That's called amalgamation and building up of an idea into a concept. So you're building this idea into a concept now. So this idea, then you start working on this. It's too open, so I need to close it in. I need to close further in and we check up the areas which is not covering, bring those areas in and then you make a helmet which has all the features. And look at the strap, there's no buckle there. That is the most innovative point. The strap is done using an elastic. So here we have concept A and the concept A is personifying ventilation. But it's a full concept. It's got all the features which are built in.


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.


Okay in this exercise what we're looking at is a dumbbell twist. Now this is what I call as a discus twist You can use it for rotational throwers as well or rotational shot putters, but the key is what we're focusing on We're gonna create separation. So you're gonna notice that again I'm controlling the hip movement to create true separation create stretch reflex So I'm gonna have a little flex in the leg again. I'm looking at that very slight chest forward position Otherwise, I'm sitting back. So that change is significant So we're gonna wind and then I'm gonna be driving out of the legs And you're gonna see this and it's the hips that are moving the dumbbells and I'm creating separation And that's what we're gonna be focusing on and having our athletes demonstrate Think of staying member the shoulders move. They don't move out. They stay here They chest comes forward, but it rotates Keep it in position try to hold the hips


Hey there, Psych2Goers. Here we are again with another interesting take on everyday psychology. So, let's begin. It's all too easy to fall for someone's wit and charm, to get swept away by their romantic words and gestures, and that there's a lot more to falling in love than just thrill and excitement. It's about trusting someone with your most precious possession, your heart. And you can't have that with a person who just wants to have fun and flirt all the time. You wouldn't want to give your heart away to someone who won't take care of it, who might cheat on you or leave you for the next person that comes along. So, if you want to save yourself all that pain and heartache, here are eight warning signs that you might be dealing with a player. 1. They're overconfident. Confidence is an attractive quality to have, and just because someone is confident doesn't necessarily mean that they're a player, right? But there's a fine line between being confident and being cocky, and most players tend to fall into the latter category. They're never hesitant in telling you how they feel and asking you out, because they're sure that they can make you say yes. And while many get tongue-tied around the people they like, not them, they're smooth, slick, and charming as ever. It can be a red flag very early on, so watch out for this one. 2. They flatter you too much. To quote Hank Ketchum, flattery is like chewing gum. Enjoy it, but don't swallow it. Flattery is one of the oldest weapons in the arsenal of all ladies men. Now, it's not to say that whenever someone pays you a compliment, it's always some kind of play. But when someone is so quick to sweet-talk you to the point where it starts to feel disingenuous, you might want to be wary of their actual intentions. Do they use a lot of lines that feel rehearsed and insincere, or do you often find yourself wondering if they've told someone all those same things before? If so, watch out. 3. They're natural at flirting. Does this person in mind have a habit of flirting with other people? Have you ever caught them being a bit too friendly with the waiter or the barista or the receptionist? Even when you're out together? Some people love to flirt because it's fun and exciting, but if your partner is really serious about you, they'll stop the moment you tell them it makes you uncomfortable. But a player?  But your concerns are certainly warranted if you often catch him or her checking out someone else. 4. They're vague about everything. When asked where they've been or what they were doing, they'll usually shrug it off and say, oh, I was just doing some work or school stuff, or I had a thing I needed to do. They keep things ambiguous and make you feel like you shouldn't ask too much about it. And while you shouldn't jump to conclusions right away, you need to entertain the possibility that it might be because they're seeing other people behind your back. It would be understandable if you haven't made things official with them yet, but if you've already promised to be exclusive with one another, then why would they be hiding things and trying to keep their whereabouts a secret from you? 5. They're always on their phone. Is your partner constantly on their phone? And when you ask them who they're talking to, they brush it off saying, it's no one. Still, their phone won't stop vibrating and they're always receiving calls even one year together. It's more than just enjoying their privacy and you feel like they have something to hide and that should already be the first clue that they might be trying to play you. 6. They want to keep things casual. Has someone you've dated ever told you they don't want to label things or that you should just have fun and see where this goes? Even though you've already been dating for quite some time now, that means that they're not serious about the relationship and aren't ready to commit. Maybe not now or maybe not ever. They don't introduce you to their friends and family. They're unwilling to make plans for the future with you, even if it's just date ideas for next month because they don't plan on sticking around for too long. 7. They hardly know you. Getting to know each other should be the first step if you truly love someone, right? If you and your partner have been on a lot of dates together and they still don't know much about you, they might not be interested in getting to know you. They have no idea how you like your coffee or what food you like to eat. They don't know your hobbies or what your favorite color is, even when you've already told them a gazillion times. 8. You've caught them lying.  Even if it was just a little white lie every now and then, you shouldn't dismiss intentional dishonesty.  A player will never, ever admit that they see you as just another notch on their belt, so they'll try to laugh it off or steer the conversation in a different direction when you call them out on it. To save yourself the pain, steering clear of people like this is a wise choice because it's not your job to fix them. Don't fall for a player thinking that you can get them to change their ways for you because you're only going to end up disappointed and potentially heartbroken.  So please, don't settle for anything less. Do you relate to any of these points mentioned here? What other signs did we miss? Let us know in the comments below. Also, don't forget to comment and tell us what you think. Please do share this with those you think might benefit from it. Subscribe to Psych2Go for more helpful content. And as always, thanks for watching.


From Milpitas, California, at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles, brought to you by Western Digital. Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Autotech Council Autonomous Vehicles event here at Western Digital. It's part of our ongoing work that we're doing with Western Digital about data makes possible and all the really innovative and interesting things that are going on that at the end of the day, there's some data that's driving it all. And this is a really crazy and interesting space. So we're excited for our next guest. He's Mark DeSantis. He's the CEO of Roadbotics. Mark, great to see you. Welcome. Thanks for having me, Jeff. So just to give the quick overview of what is Roadbotics all about. Sure, we use a simple cell phone as a data collection device. Put that in the windshield. You drive, it records all the video. Know that video gets uploaded to the cloud and we assess the road surface meter by meter. Our customers would be public works departments at the little town of a big city or even a state. And we apply the same principles that a pavement engineer would apply when they look at a piece of pavement, looking for all the different subtle little features so that they can get a, first of all, get an assessment of the road and then they can do capital planning and fix those roads and do a lot of things that they can't do right now. So I think the economics of roads, the condition of roads, roads in general, we don't think about them much until they're closed, they're being fixed, they're broken up, there's a pothole. But it's really a complex system and a really high value system that needs ongoing maintenance. That's right. This is the example of the Romans who built a 50,000 mile road network across Europe, the Middle East and Africa. Some of those roads like the Appian Way are still used today. They were very good road builders and they understand the importance of roads. Regrettably, we take our roads for granted. The American Society for Civil Engineers annually rates infrastructure and we're rated about 28% of our nation's 11 million lane miles as poor. Unfortunately, that's just 28%. 28% and that really means that you need to invest, we'll need to invest at least a million or two million bucks a mile to get those roads back into shape. So we take our roads for granted. I'm enjoying this conference and there's one point that I want to make that I think is very poignant is the AV revolution will also require a revolution in the maintenance and building and sustenance of our road network, not just in the United States but everywhere in the world. So it's interesting in doing some research before we got together in terms of, the active maintenance that's not only required to keep a road in good shape but if you keep the active maintenance in position, those roads will last a very long time. And you made an interesting comment that now the autonomous vehicles, it's actually more important for those vehicles, not only for jolting the electronics around that they're carrying, but also for everything to work the way it's supposed to work according to the algorithms. Andrew Eng, who's an eminent computer scientist, machine learning, we were spun on a Carnegie Mellon. He was a graduate of that program, recognized early on that the quality of the roads made all the difference in the world for these vehicles to move around. We in turn were spun out of Carnegie Mellon out of that same group of AV researchers. And in fact, the impetus for the technology was to be able to use the sensing technology that allows a vehicle to move around to assess the quality of roads. And road inspection really is an important part of road maintenance, the ability to go look at an asset. Interestingly, it's an asset whose challenge is not the fact that it can't be inspected, it's the sheer size of the asset. When you're talking about a small town that might have a 60 mile road network, most of the vast majority of inspection is visual inspection. That means somebody in a car riding very slowly looking down. Now do that for tens, thousands, hundreds of thousands of miles, very hard to do. Our system makes all that very much more efficient. The interesting thing about autonomous vehicles is they'll have the capacity to use that data to do that very assessment. So for our company, we ultimately see us embedded in the vehicle itself. But for the time being, cell phones work fine. Right. So I'm just curious, what are some of those leading indicator data points? Because obviously we know the pothole by then things have gone too far. But what are some of the subtle things that maybe I might see, but I'm not really looking at? Well, I think I've changed you right now. You don't know it. You're never going to look at a road the same way. Oh, I told you. After you hear me talk for the next three minutes. I don't look at roads the same. And I'm not a civil engineer nor am I a pavement engineer. But as a CEO of this company, I had to learn a lot about those two disciplines.  And those features determine the state of that road and also dictate what repairs will be done. Concrete pavement has a similar sort of set of characteristics. So what we're looking for when we look at a road is I always say that people say, well, you're the pothole company. If all you see are potholes, you don't have a business. And the reason is potholes are at the end of a long process of degradation. So when you see a pothole, there are two problems. One is you can certainly blow out a tire, break an axle, and then pothole. But also it's indicative of a deeper problem, which means the surface of the road has been penetrated, which means you have to dig up that road and replace it. So if you can see features that are predictive of a road that's just about to go bad, make small fixes, you can extend the useful life of that asset indefinitely. Right. So before I let you go, unfortunately, we were short on time. I'd love to learn more about roads. I told you I'd skateboard, so I pay a lot of attention to smooth roads and rough roads. And you'll pay even more now. Now I'll pay more and call the city. But I want to kind of pivot off what happened at Carnegie Mellon.  There's a lot of work going on. We're close to Stanford and Berkeley here. Talk a little bit about what happens, happening at Carnegie Mellon. And I think specifically you came out of the Robotics Institute and something called the Traffic 21 Project. Yeah, Traffic 21 is funded by some local private interests who believe that the various technologies that are really CMU's known for around computer science, robotics, engineering could be instrumental in bringing about this AV revolution. And as a consequence of that, they developed a program early on to try to bring these technologies together. Uber came along and literally hired 27 of those researchers. Argo now, Argo Ford's autonomous vehicle now is big in Pittsburgh as well. On any given day, by my estimate, it's not official estimate here, there are about 400 autonomous vehicles, Ford and Uber vehicles on Pittsburgh streets every single day. It's an eerie experience being driven around by a completely autonomous Uber vehicle, believe me. I've been in a couple, it's interesting. Then we did a thing with a company called Phantom. They're the ones that step in if your Uber gets stuck, which is interesting. So really interesting times and exciting. And I will go and play closer attention for the alligator patterns on my route home tonight. All right, Mark, thanks for stopping by, sharing the insight. I appreciate you having me. All right, he's Mark. I'm Jeff, you're watching theCUBE from the Autotech Council Autonomous Vehicles Event in Milpitas, California. Thanks for watching.


In the meantime, the Nigerian National Petroleum Company limited and other marketers confirmed a new priced regime for premium motors period, otherwise known as petrol, following the announcement of fuel subsidy removal by the new President, Paula Ahmed Tinobu. Residents in a kitty state have urged the president to put an end to the hardship. They also rejected the new pump price of petrol fixed by the Nigerian National Petroleum Company limited and other marketers. And it is a slap on the face of the citizens of Nigeria because we don't expect this from our president. We don't expect this from this party called APC. It is unfair. In 2015, I can remember, when Gulogionata was on par, these people were ashamed that they will beat down the price of petroleum to $14,000 that when they get there, they promise even a net that this is what they will do for us. But it is unfair that we can see that we further our saving world is calm. It's calmer. The economic delay is going to cause a sort of economic bull drum. We are everybody, we bear that financial agony, there is no doubt about that. It's on the high side, it's not easy, it's not easy, it's going to put a problem on many homes, the economy is going to batter the economy the more.