 So I'm really pleased to introduce our colleague in Aeronautics and Astronautics, Shashwe Mo. Shashwe got promoted this year as an associate professor in Aeronautics and Astronautics. He's done a nice job on his slide here, so I don't have to read my notes. He got his PhD at Yale in 2014, spent a year in Cambridge, the Purdue of the East, right, MIT, and then joined, I had to sneak that in somewhere, right? And then joined us in 2015 as an assistant professor in Aeron Astro. Shashwe works in control and coordination of multi-agent autonomous systems. He develops distributed algorithms for solving large-scale linear equations by multi-agent systems. He works on resilience for consensus-based distributed algorithms, does some inverse optimal control to learn multi-phase objective functions, and is also working on formation control and reinforcement learning for UAVs and other drones. A couple of exciting things that Shashwe's been involved with. He's been on awards that had three Purdue seeds for success, so those are very large awards. He's been part of three of those. Our students elected him as the outstanding faculty mentor. Our graduate students did that in 2019. He teaches a lot of our undergraduate courses in the control area, but also introduced a multi-agent systems and control class in Aeron Astro at the 500 level. He's had over 21 directed study projects with undergraduates and graduate students. He's the advisor or has been the advisor for the Purdue Autonomous Robotics Club. He's the faculty advisor for the First Street Tower Resident Hall. He has really worked on a relationship with Northrop Grumman, has several contracts from Northrop Grumman, is one of the first awards with Rolls-Royce and their Cybersecurity Initiative with Purdue, so that's another tie-in. I've got a lot of stuff here. I'm not gonna read it all Shashwe, because you gotta go to class at 12, but he's also the co-director for the Center of Innovation for Control Optimization and Networks. Everybody knows that is ICON. That's the center he's done in collaboration with controls folks all across campus, strong here in engineering. That's brought in speakers both virtually and physically from all across the country and some around the world. He's also the associate director for the Center for Intelligent Infrastructure. He's on the faculty steering committee for the Autonomous and Connected Systems Initiative. He's a member of IEEE. He's been on organizing committees for different conferences. He's an associate editor for systems and control letters, and I don't know when he sleeps. He's done a lot in the short number of years he's been here, but Shashwe's an outstanding colleague. He's done a lot for engineering for the School of Aeronautics and Astronautics, so with that, let me introduce my colleague, Shashwe Moe, associate professor of Aeronautics and Astronautics. Thank you, thank you. Good morning, everyone. Thank you very much for the introduction, Harvey and Bill, and it's really an honor to be promoted to associate professor. And time flies, actually, I still feel I'm in 2021. That's why my tenure promotion actually happened in 2021, if not 2020. And my research has been focusing on control for autonomous systems. And you may ask, that's more like my career and also future career. You may ask why we care about autonomous systems. So, actually, autonomous system already existing, you know, in our day life or in future missions, self-driving cars, this very fancy humanoid robot, or the automatic ground vehicles, or the future OCT for exploration of cars. And also for, you know, the cooperative robots and for healthcare, manufacturing, or monitoring a large area. Actually, our morning society has been relying on more and more of these autonomous systems. So, the next question we may ask, well, you know, what are the fundamental challenges for current and future autonomous systems? Those, actually, the challenges are the four types of main challenges that we have identified for current and future autonomous systems, which also serve that motivation of my research. So, because it's autonomous systems, so, first, we want the system to be autonomous, means to achieve some missions without human guidance. So, that's a lot of methods developed there, optimal control, reinforcement learning. But the open challenge there is, you know, for different missions, and no matter your methods based on optimal control or reinforcement learning, they all require mission dependent objective functions. But how to get these objective functions is very challenging. It depends on different missions. So, we have done some work on that. And also adaptive, you know, our work for autonomous, mainly, we developed a fundamentally new inverse optimal control method to learn objective functions. And the state of art is based on KTT conditions under which the result cannot sense and adapt. So, our results, many could solve the IOC based on incomplete data, robot could sense and adapt. And also adaptive, that's actually, we would talk about adaptive, you would appeal or say, oh, machine learning, we have a lot of data and train the data, you know, for, to deal with uncertainties and dynamics. So, our group more like go into the other direction. What if we don't have data? We don't have a lot of data for training. For example, in Mars exploration, we don't have a lot of data. But in control field, the key concept will be called feedback. So, that's more like, you mean how to utilize the online data to serve as a baseline data to train your algorithm or to tune your algorithm. So, we have a ball with the concept of entering the learning from a machine learning community and develop a framework for adaptive autonomy. And later on, we realize that, okay, that actually it's also a method that could solve three large classes of problem, not just adaptive autonomy, but also system ID and also inverse optimization. And also cooperative, that more like, we want the autonomous system to cooperate with the human, so that a direction actually is also direction new to me. When I first studied the human robot teaming, I actually didn't have any funding that, but a good part is that our associate head, Moin Chen, gave me a lot of TAs. And I used that to explore the directions of the new. You already are a department and the TAs are assistant professor, junior faculties, how the priority to get into the TAs. That's actually very helpful for junior faculty. That time, I didn't have a lot of funding. And that enabled me to explore the new topics. And also for the human robot teaming, we actually also developed a fundamental framework for based on control, optimal control and adaptive system to allow a robot to interactively integrate the human input, which are sparse yet great. And also for the first topic is swarming and how to develop the digital control to achieve swarming autonomy. That actually I get to know that from my PhD. And under this, like the four main challenges in this area, we have developed a bunch of nice results based on integration of control with data science, robotics, machine learning and networks. So here is a quick overview about that. Actually only this part is kind of related to my PhD research, this algorithm. But after joining Purdue, Purdue does provide me excellent platform to explore new topics and also collaborate with other researchers. And so we have developed several other direction, new directions. And those have led to a bunch of research publications, basically in top journals and in control about techniques and machine learning and also several large projects I'm very lucky to have. And education mentoring, you know, our research basically cannot be successful without a student. I have graduated two PhD students, one PhD already a faculty, and a post-doc also become a faculty. And of course my group is very diverse and international from all kinds, all countries all over the world. And especially also we're also recruits, female and URIM students. And as just a bit of a thing, I'd like to share my perspective about research and education. I would say my, if there is any success on my research, I think my philosophy is focused on high quality research with impact. So in my group, usually I would suggest them, you know, we would rather spend two or three years to figure out for me is a fundamental problem, a regional problem. You know, we could use three years to get a journal paper in top journals rather than the fast conference publications. I feel sorry for my students, they don't have an auto publication. But no, all their research are, I would say, have to have the quality there, especially on the original research part. And also exploration on new topics. And I said usually my, of course now I have a lot of funding, I could explore more new topics. But at the beginning, what I didn't have a funding is that it's kind of a 70 percent of, you know, for 60 I could still be productive, but 30 percent, and for the new topic, that's mainly based on department TAs. And also fundamental research. I kind of like, you know, no matter theoretical or experimental, to my opinion, don't matter. I think focusing on the fundamental mechanism behind all these existing research, that's the part I'm passionate. And also collaborations, I like collaborations with different expertise. And also for mentoring, I would say, even in my class, I always ask the students, you have to think deep, not just to be happy about, oh, I know this method, I know how to apply that. I always ask them, you know, why those smart people develop this method, this new method. You need to understand the fundamental mechanism. And if you want, you know, developers, even experimental research is fine, but you still need to know the fundamental mechanism. And also all students are different. It's really different. But they are all excellent learners. I really, I would say, appreciate their efforts, once we identify a problem impact and all their interests. And also the research in this area, I would say, in this autonomy, I cannot move forward. It will be difficult to move forward if we don't have collaborations. This is the area, tournament and client system is the area, which really requires different expertise. So on the second month initiative, this idea of forming this center for innovation, control and optimization. So we have, initially we had 25 faculty, but now we have 64 from lab departments. We have established a bunch of research themes, and also we have the education and goal. These are more like highlights we have done for the center. It basically planning for the frontier research, last year of funding, and also the collaboration with the industry. And also the educational goals we have. We have several A&E members support. So my career goal is to advance control theories with optimization network learning to address fundamental challenges in the autonomous system and also to serve research education for university strategic initiative autonomous system. And this is my page of tech. I really have a lot of people to thank, but Erwin told me that I only have 10 slides to present. So I would like to add another four. I would like to add another maybe four slides to thank. You know, a lot of people, maybe they even didn't realize that actually they have a helping a lot. Like mom, I mentioned that he's initiating the idea of forming a center for control. This is maybe one of the many things they have done after joint Purdue, but it really means a lot to not just me, but also the control community and Purdue. And my mentors like Dan, and beside research collaboration, Dan has ensured more and mentor me in many directions, like getting funding, especially bridge a lot of connections between me and the funding agencies, and also teach me how to get funding. To be honest, I'm not a fan of writing proposals, kind of hate writing proposals, and I enjoy much more in doing research than writing proposals, but we cannot get without funding. So, but with Dan, Dan more like a mentor me to how to build reliable, you know, funding resources, which actually see me a lot of time in not writing proposal, I still get a lot of funding. And this method I have eight projects going on, but each they just asked me to write a few pages and more like to trust me and give me funding. And also Martin Corleys, the other mentor, also we have a lot of deep research discussions. But beside that, actually, Martin also, we had a lot of interactions on how to success and the junior faculty, what to do. And it's, it's especially at the first few years when I was under a lot of pressure, and because the transmission from a student to be a faculty. And also other like Arvain, I never thought a social dean could be this easy to be to be approachable. And I sometimes I need some personal advice, email Arvain and Arvain replied very quickly. And, you know, the same as mom and also went to for one year, I don't email Wen Chen just knock your door. And I know this is my problem. The way you really stop working and then talk to me. And of course, you know, all the other things like my department had Bill and Tom and the morning to provide all the support to the to my career and also a special bill for the center operation. And actually all the other colleagues and, you know, George always enjoyed talking with George and very inspiring talk, not just the research and all the other in all the organizing activities and things I can't and also my colleagues in control in Sok, Dongfeng and art and all this. And also my mother and my students. And thank you very much. This is all my presentation. Thank you. So we have some time for questions and answers. And it looks like Marsha is putting instructions up for the remote people. Shashla, when do you have to leave for class? And those are different than 12 o'clock. Okay, so we do have time for some questions and answers. So does someone in the here we go. Thank you, Shashla. I share the great experience. And yeah, so I have two questions. The first one is like, I saw so many, you know, achievements in a such short time. How did you balance or, you know, you know, balance your time for different activities, research, and teaching, preparing class and mentoring? Yeah, this is the first question. And the second question is like, on your research. So since you both work on the control theory side and the learning side, and you mentioned the human role of collaboration. So traditionally, maybe people think theoretical based methods is more appropriate for the, you know, interaction with the human for robots. But the learning based method, maybe it's a kind of black box, we do not know what's going on, maybe not good for the, you know, human role interaction. Yeah, I want to hear your comments about that. Okay, thank you. The first question, how to balance the time actually, I even didn't thought about that when I was junior faculty, because you don't have time to think about how to balance all this, and also a lot of pressures. But I do have advice more like, for me, I have a habit. The busier I am, usually I will spend more time in running, doing exercise, watching movies, things. It's really a good method for me to release all the pressure and also fresh all the minds. I learned this from my PhD advisor, like Steven Morse. So usually I, when I have a research problem, I was too passionate about the research. I would knock his door, I said, oh, this is my result, that's my result. And he said, oh, you know, so I said, you're already deep enough in the research. So I would suggest you take a break, go to a Canadian National Park, and let it, you know, let your brain automatically working on the research problem. So the same for here, if you have under pressure, then I would say, perhaps you need a key walk, like me and Dan, me and Dan, Dan, we always have a key walk kind of stuff yet. A second about our research, human robot teaming, that's kind of accidentally I run into this area, because when I first started to on the fundamental research, I didn't do any human robot teaming. And I said, you know, I have additional resources from the department when giving me the TAs. And okay, even without funding, let's do some fundamental research. That's inverse optimal control. We have achieved more like several journal papers on that. And then somehow we found, oh, if we look kind of human motion at the optimal control system, you actually, all these methods could be applied for human motion prediction. So it's somehow once I have achieved some nice result on the applications, but it's actually easy if you got the theoretical part. And then start to get more funding. So I would say usually if you have one of the box, about this, then spend 70 maybe on the research with guarantee, maybe 30 with exploration. That's very important to me actually. So I would say, you know, really appreciate the additional resources provided by the department. Anybody else in the audience that has a question for Joshua. So, so happy to be here. First of all, as I know, and Martin knows you recently went back to your workaholic days and solved an important theoretical problem that you were you and your former advisor were struggling with. So what have you learned about your ability to do that? And and have you seen any evidence that you could help your own students now develop the skills and the patience to solve a hard theoretical problem? Yeah, thank you. Thank you. So yeah, that's actually a story I shared with Dan and Martin about that. Several weeks ago, my PhD writer called me and emailed me a one page of note about the question about a problem about a very hardcore theory on consensus that actually he brought the consistent concept to the field. And I become immediately very interesting that as I cannot see. So just more and more slept on the problem. I emailed Dan and Martin and I spent like this calculation papers, which I took from our department to write to for doing the you know, I like and doing all the derivation of that kind of things. It's all the week. I more like slept three m or four a.m. sometimes emailed back. And that is more like I thought I already not that passionate about the hardcore control theory, but turned out not. Another problem is very interesting. I can see the possible fundamental impact. It just become very excited about the problem. Although I will not completely solve this problem, but actually it's a partially solved than I want to prove the result on the timely environment graph. And of course, he's interested in more general results. But from this story, I'm more like a garden dad question is that I found that first not all students are interested in this kind of theoretical fundamental research. And you're in my group, I let students decide and you know what they want to do. And for PhD, they need to be a little bit of deep in control theories. I think it's more like a kind of with our responsibility to teach students to appreciate actually the fundamental and the theoretical research result. I know flying a drone is much more exciting than writing equations, me too actually. And at the beginning of my PhD study, I was oh, why in consensus? Why gossiping? It's just, you know, I want to do the formation control to develop the thermal control for formation. But actually more like, but you know, my advisor said no, you don't want to forget on that. Otherwise, you cannot grant it. They more like force me to think deeper about all the research and also start to appreciate and oh, of course, we always say, oh, everybody has a personal interest, but that kind of our own prejudgment about research. And the current and I think I'm okay to all the research where to go instrumental. You may in my class, I said, if I don't limit you guys course project, if you want to study COVID spreading, do it, you know, go to get some data and using the tool we taught in class, or you're good to learn some new tools by yourself to model the COVID things. But I think, no, into the 30 research kind of why the personal interest the other is maybe I got trained in this way. I realized that actually the whole I got trained and the whole I do my perform my faculty life. It kind of also what we can envision how we impact our students. So when I train my PhD students, the first thing I told them I said first, our group, we don't care about another publication. You may publish a 1000 paper. But if you don't prove you are independent, you have a strong independent research capability, you may not get a PhD for a PhD. It really is a training for your research. And also, you know, how did the research could potentially benefit the society? Without that kind of vision, without that kind of capability, I told them, you know, don't tell me how many papers I don't care about. That's more like I kind of enforced the rule in my group. Great. I think we're on time. So let's all congratulate Joshua one more time. Congratulations Joshua. There was one comment from Dave Cavallieri. He said, nice job. So just so that was the chat.