 Hello and welcome to the ninth webinar of the engineering rising to the challenge initiative from Purdue engineering. My name is Arvind Raman and the executive associate dean in the college. Now this initiative started in May 2020 partly in response to the National Academy of Engineering's call to action for engineers to tackle some of the challenges posed by the COVID-19 crisis. But our initiative also looks to the longer term future to rethink and reengineer the very systems that are modern society has come to depend on so that they might be more resilient to such shocks in the future while also serving society better. Now part of the initiative involves webinars where distinguished panelists unpack some of these challenges and provide us a glimpse into what the future might look like. Today's panel is about resilient innovation for social equity. And it is my honor to introduce the moderator for today's discussion and Dr. Ajay Malshe. Dr. Ajay P. Malshe, the R Eugene and Susie E. Goodson Distinguished Professor of Mechanical Engineering at Purdue University. And he previously held a distinguished professor position at the University of Arkansas, Arkansas before moving here recently. His areas of expertise are advanced manufacturing, bio inspired designing, multifunctional material surface engineering and system integration and productization. Since 2018, he has begun to apply his manufacturing design materials and systems engineering expertise to examine the national challenges of nutritious food and other forms of techno socioeconomic insecurities through accessibility, affordability and sustainability. In his experience, he has published more than 200 peer reviewed publications and more than 20 patents which have resulted in engineered products utilized in the energy, aerospace, food manufacturing, heavy duty transportation, EV and high performance racing. His innovations have received the R&D 100 award, multiple Edison awards, the Tibet awards amongst many others. In 2018, he was elected to the National Academy of Engineering for his innovations in nano manufacturing with impact in multiple industry sectors. In addition to his research and translational activities, Dr. Malshe has educated 60 PhD and postdoctoral scholars and demonstrated commitment to undergraduate and K through 12 education. Without further ado, over to you Ajay. Well, thank you very much Arvind for a generous introduction. And I would like to welcome all the attendees. So in my introduction to the panel, if I'll be doing introduction to this little bit about the subject and the backdrop. But at this point in time, without any further delay, I would like to welcome you all to the panel as Professor Raman said. This is the initiative you are to see your COVID related. It is almost like earthquake, it is a health week. And the society has experienced the challenges across the globe. With that, the rise initiative that is launched from Purdue. What we look for is that are we doing the right innovation that can help America and the world across the globe. So with an important discovery during this time that more and more we realize that it is the inequity and inequity gaps. This is not a social economic challenge, but it is a techno social economic challenge. As all engineers and industries are poised and excited about industry 4.0 and going beyond at the same time in the same place in the same community across America, what we also see is this gap. That when everything is going super on the top, people are suffering at the bottom with average capital income of 36,000. Now that poses the question that what technology is helping the society. And that's where really the curiosity and this passion started with the attitude of servant leadership. If you look at a Maslow pyramid, the number of innovations, those are happening out at the tip where self actuation is important, but the way most of the needs are there at the very bottom. And this is something a question that one can ask, oh, what are we doing? Because during this process, the best technology that came and the innovations are like the Harvard and the word innovation that came to the rescue of the most is a mask. And that very mask was also used 100 years before. So what is resilience? This word is used like intelligence, smartness, numerous times. It is an intrinsic property of a system to resist and recover and adapt to a new and improved state in time. And further definition is right across from you, but this is a very complex way to say the simple thing. But online, if you can come across any small, large challenges and all society survives and excels through that and how we build a resilience is very important. So in that it really peeling off, as I said, the layers of this complexity. The numbers here today as I was mentioning here that the mask was to the rescue and 100 years before some of you may not know in Spanish flu. This was the solution to America and the world as well. So how do we improve what we advanced? What the technology means? What we do to the human kind? And with that, typically society with our reflections and are we innovating and investing in what matters? And with that question, how can we think outside the box? So please webinar, my colleagues will discuss how we think outside the box and we are we asking the right questions, because the questions right questions lead to potentially right answers. So the example of resilience is that flower, which is growing in the middle of a desert and that's where the equity, the quality balance and harmony, the code aside together. So the examples of what we call frugal innovations, not cheap frugal, how they perform and how they're accessible. Here's an example from Purdue. This is back called text. This is helping across multiple parts of the world. There is a three layer engineer structure, very accessible across the start of the society. This is for agriculture. This is for water. We think human needed the bottom of the Maslow pyramid in Peru, where the harvesting, the fog in the air is helping community. Providing light to the communities in Philippines and Brazil and parts of the world, we have the simple solution for recycling the plastic. This is the middle ranges of Himalaya for water. So these are some of the most simple, important science ideas. Those are applied for innovation to help the communities and bring people out of poverty and provided opportunity for upward mobility. So because we see that going forward readiness of the society, trustworthiness on people and technology and inequity would be some of the most important challenges will be facing. So we go more further delay as we think outside the box. Let me introduce my panelists, as you will find out who brings the expertise across all the various technology innovation, equity, equality, data science and health care. Our first panelist would be Dr. Joseph Seinfeld. His team of contribution will be dynamics of innovations and lesson learned. His brief bio is Professor Seinfeld is a professor of civil engineering at the university and the founding director of Andrews University College of engineering and innovation and leadership studies program. His work focuses on innovation science systems and sensors, and he has 20 years of experience as an advisor to senior leaders of multinational corporations from the methods of identifying and prioritizing commercialized growth opportunity, design new business model and manage a steady challenge. This is one of the most prolific colleagues we have on the campuses and he would be contributing to the dynamics of innovation. This colleague who is also expert in design area mechanical engineering with the distinction so many Dr. Tahir Reed Smith is an associate professor in the school of mechanical engineering at the university and is the director of the research in engineering and interdisciplinary design laboratory and visiting NASA scholar. Research interest includes qualifying and integrating human centered considerations in the design process and human machine systems. Research programs receive multiple funding opportunities from the prestige agencies like National Science Foundation, AFSR, Procter and Gamble, Polar Corporation and General Motors. The third colleague who brings industry and community centered perspective is Dr. Carl Chanel. He comes from Corteva Industries. Example he would be sharing would be data driven innovations for food equity in urban desert in Indianapolis. His brief bio is Dr. Carl Chanel is a global operational excellence leader at Corteva Agrifines. In his current role he identifies and develops cross functional R&D initiatives for strategy contributions improvement and productivity processes. Carl is also delighted to provide his time and talent in project and leadership and data analytics for the Indian hunger network, all aligned for food and food security. As a result of his team's pro bono work, new food pantry, WIC clinics and congregate meal sites have been optimally placed in the local communities. And last but not the least, my colleague Dr. Yuvarn Yee, her contribution would be in the team example of data driven innovations for health equity in urban and rural deserts and policies that move this. Dr. Yee is a professor of industrial engineering and currently serves as the academic director of laser pulse, a consortium at Purdue University that is directed by Professor Rahman and it is one of the most prestigious and important project for the country. Her core research is system modeling and decision making for complex system operation design, monitoring, evaluation and risk mitigation. Applications of research include manufacturing, supply chain and humanitarian aid, healthcare as well as sectors in global development. So all these spherical angles that we bring to the topic of resilient innovations for social equity. With that, I would like to invite our first panelist Dr. Drew Central. Thank you. Thank you so much AJ. I will try to share my screen here and provide some brief remarks to seed our conversation today about resilient innovation. This is the overall perspective that I'm sharing being driven by a topic we call innovation science. And we're really going to be looking at how we apply innovation science to grand challenges and see what perspectives that sparks about the overall issue of resilient innovation. Innovation science for those of you who haven't perhaps seen the term before is really an integration of disciplines. It is the definition of convergence in many ways. Whether the visionary perspectives and landscape perspectives that are part of strategy with the intent that comes from design, so we can ultimately create solutions that are optimized the landscape that we might encounter. We then combine that with patterns of innovation success and these patterns are reinforced through massive data science, where we can really see very high numbers of cases that suggests that a particular type of innovation format might work best in certain conditions. And at that intersection of all these fields gives us this perspective that we can move innovation from really a domain that historically might have been characterized by serendipity or luck to a domain that's much closer to rules that we can apply to have an enhanced chance of getting the outcomes that we seek. When we begin to use big data to uncover patterns of innovation, we start to see cause effect relationships between different forms of innovation and the context in which they work. And many of you have probably seen a variety of terms that are used as modifiers for the word innovation, be that terms like disruptive innovation or modular or architectural innovation. Importantly, all of these terms are actually very distinct, and they work only in specific circumstances. And so one of the efforts that we pursue in innovation science is trying to understand what characterizes these different forms of innovation and this is a very small sample of what really are dozens of variants of innovation. And what are the characteristics of the environments in which they can be applied successfully. And that way when we encounter a problem, such as a resilient innovation challenge, we can do our best to try to match the solution design to that needs of that particular context. And looking at a wide range of complex socio technical challenges over the last seven or eight years now, we've uncovered a variety of patterns that reinforce our understanding of these situations. We've examined problems as broad as looking at portable water availability in rural areas of different countries around the world, examination of food security at the level of a nation, the delivery of drugs and medications to address different disease and other situations such as poverty and urban metro areas and challenges that come with smart cities. And very interestingly one of the most exciting perspectives that I think has been yielded through this examination is that while these problems are all different in many ways they are all quite the same. And that is one of the things that can help perhaps help us drive toward a space of solutions. And in fact they share a variety of success factors regardless of the problem type. Those success factors tend to fall into the 16 areas that are highlighted in the circular graphic on the left hand side of the slide, and they really fall in four major groups. One is inevitably there are characteristics of the population in terms of driving adoption, the motivation and awareness of the challenge and having the resources they need to try to address the problem that are at hand that are critical in any situation. In addition, if we are going to provide some form of solution be it a product, a service, a policy, what have you we need the infrastructure in place and the related supply chain of resources or talents that might make that possible. And all this has to exist in an environment where we understand the organizational structure, the hierarchy of decision making, and the policies and leadership structures that are in place that make it possible to deliver a solution. These three sets of variables are really part of somewhat of a static picture of the environment, but we have to combine that with a dynamic understanding and this goes to Professor Malashi's point about resilience, we need to create a system that is resilient and sustainable over time. When we examine these factors, we've also found that a construct that we call poets is quite useful in getting multiple perspectives on the problem. We can draw an insight into the situation and its effect on physiology, on the psychology of the stakeholders that might be involved and ultimately the linkages to the political climate. We also can bring in views on the operations that might be required to carry out the delivery of a solution and its impact on economics, the environment and education in that area. Ultimately, we might have some technical developments that are required as well as sociological implications on the cultural environment. And all of these things have to be looked at ultimately at multiple levels. And we try to think about varying levels of abstraction when looking at these problems and extend from examination of the individual and their role in their households, their role in their community, then also how institutions at a regional and national level play a part in ultimately addressing these challenges. And for any one of the given complex sociotechnical problems that we've had a chance to work on in collaboration with either corporations, nonprofit agencies, and even governments in some cases. We found that regardless of the problem we have somewhere between 800 and 1000 things that have to be true in order for a solution to really work. And quite often when we come at a problem from any one discipline we're addressing only a small subset of that system. And sometimes we're fortunate and many aspects of that system are already in place and working we're able to close the final gap. But oftentimes we seem to be working on what might feel like a moving target as you talk to practitioners in the field, when they're working on a particular problem because there are so many moving pieces. And a lot of the work in innovation science is intended to help us get our arms around what those pieces are so that we can begin to dedicate resources effectively. And one of the more interesting pieces of the pattern that we found is that those populations that might be achieving a disenfranchised outcome or receiving an inequitable share of the resources that are an environment are often blocked from equity by a variety of variables. And those variables might pertain to skill or knowledge that they may or may not have about a situation, their ability to have the wealth that's required to access solutions that are available. Physical access that resources may not be available where they need them when when they need them. There are time constraints that is other parts of people's lives get in the way of pursuing certain kinds of solutions. And then there's a host of other things that are related to behavior attitude and belief systems that can have an influence on driving a disenfranchisement of different populations. Certainly these same seven factors seem to be again apparent regardless of the particular challenge that we're facing. And by being aware of them before we begin to solve the problem. We have a chance to try to proactively designed to overcome any of these obstacles. And very importantly in our work that has spanned work around the world and low to middle income countries all the way back to work in the United States and other parts of the higher income portions of the world. And that's the very relevant both beyond and within our borders. Ultimately all these factors contribute to a certain form of innovation returning back to this notion of patterns. And that form is what we call enabling innovation and enabling innovation is a form of innovation that drives high impact. It has the ability to reach many many people and drive an impact cascade over time. It's a combination of factors that yield enabling innovation that have to evolve through both technical and conceptual breakthroughs over time to aggregate into a holistic capability. And those factors might be related to technology, but they could also be related to economics to changes in habits to changes in social awareness on a problem and even elements of policy that might be part of the situation, all of which are really contributing to the creation of a much more capable innovation that can drive a system level change in the environment. And so these are just the short remarks I wanted to use to start off our discussion today and I'll leave it there to see if there's any related questions or want to continue our dialogue. Thank you, Joe. At this point in time. I think we will be for questions. So we will come back to the questions and also at the later end. So please everybody post your questions. So thank you, Joe game. We will go to Dr. Reavesville. Thank you. Good afternoon everyone. Thank you for coming to this event. And so some of my remarks will be centered around this idea of engaging the minds of engineers and social issues of our day, especially engineering students. And I thought I would open this up by just sharing a little bit about my educational journey and how I started out thinking, being trained in sort of traditional mechanical engineering, and then at the PhD level, switching over to thinking about people more, bringing in psychology into my research in the design science program, and then continuing on in a postdoc. It's mechanical engineering but still interdisciplinary. I was around people doing virtual reality, human computer interaction, all of that. And then I established a research lab here where I had the space and the room to start to continue thinking along those lines. And then bringing in the human centered aspects to my research. And then last year was on sabbatical all year. And a portion of that time was at NASA, largely in the fall and I'm still engaged with them until now and and that also the focus there was thinking largely about user stakeholders and beneficiaries with opportunity identification and using design process methods. There's this paper I love to reference it comes from several researchers in engineering education, and it talks about the people part of engineering. And engineering, you know, when you when you step into a problem, you're engineering with others in a team, you're engineering as a person you bring your whole self to that. And you're engineering, you're doing engineering work for people and it spans communities, the world, etc. But what we need to do is we need to create spaces where we can welcome students to bring their entire selves to problems. And there are quite a bit of social issues happening that appeal to the minds of students. And just really at a high level of my research lab, just to kind of give you a point of view of the types of things that I've been doing. And the topic areas I love acronyms as you can see everything has an acronym, you know, socially and culturally relevant engineering design design thinking and problem solving etc. So broadly speaking, this is what my lab does. We think about people in different ways, either the designer him or herself designer or engineer him or herself or themselves, or the end users are those that might experience what we create. Last year I was on sabbatical all year. And there were lots of things happening right we all experienced COVID together, we, you know the shutdowns were happening. Then, you know around July. This, the big thing that happened with George Floyd. That was a major, major motivator for me. It was in July around June or July actually happened in May but there was so much the protest you guys might remember global were happening and my annual conference and design engineering was happening in August. And I was in discussions with some of my colleagues and me about like how can design engineer how can we get involved and do something in this space. I was just in this, this mindset of, I don't want to just talk about research I don't want to just get together and talk with folks just to talk with them. How do we bring what we know what we do into this space and it was just sort of like, sort of this burning just unsettled kind of a space that was happening and we talked about ways we could try to do it and then quite pan out. Particularly, and then I started sabbatical time, the rest of my sabbatical time at NASA, where there was lots of, and it was all telework by the way. Lots of interactions around design design process methods how do we really think deeply about the people and what we're doing etc. And then this picture on the upper right is called digi dog it's created by Boston Dynamics. This is one of my newer motivators created by Boston Dynamics on their website it's called spot. What you see is a photograph of this digi dog deployed in New York City, the NYPD was using this digi dog in Queens Brooklyn, and the Bronx, just testing it out $75,000 investment testing it out to see well can it how can it help us and certainly it is performing great tasks like you're searching buildings and kind of going into spaces where maybe a human going into that space would be more risky is better to send a robot. But the flip side of that is have the communities were not prepared for the presence of this digi dog. So don't even know what an autonomous system is. They walk out and it's like what is this, no permission, no sort of awareness or warning about that, and largely these are marginalized communities in New York City. There are questions around deployment and some of my work involves looking at human machine interactions autonomous systems, etc. But some of the questions that we need to be thinking about is, Okay, once we have our algorithms all figured out and you know that's a whole separate question on some of biases that can get embedded in those. But once we get them figured out and start deploying these in communities, who is helping someone's grandma who's used to going down to the bodega at the corner to understand what this is when it's walking around. It's about when driverless vehicles, let's say lift gets to their goal of being able to send a driverless car into a certain community or neighborhoods, and a driverless vehicle shows up to your grandma's house or to your aunt's house and they she she's seen one and how Paul is preparing them to embrace these new technology. I don't see much discussion about that as well. And so between the social considerations between my time at NASA. And I was thinking of a graduate level class to teach and so this spring I launched this class a new one called design sprints for complex engineering problems and socio technical systems. And the motivation was to create the space where we can gather graduate students in engineering the College of Engineering to start thinking about these things to start getting to give permission for students to not just think about the technical aspects but to really think about how can the engineering influence the social aspects of this. And we have several different projects going on actually one in particular is motivated by some work that engineers are doing in parallel at NASA regarding urban air mobility. Our students are working largely in public health and emergency management. And then there's some others that have taken on there's one person in particular taken on the task of thinking about how might an engineer bring some insights to law enforcement. How do we make it a win win. How do we make it a safe situation for police officers and for suspects. We want everyone to go home and there's some decision making errors happening. How can we get in that space. Some others doing things with redefining sports and what does it mean to be safe and what does it mean to have good performance. Several on food security topics and then you have others that are working on projects that it may not be directly obvious. Who the humans are but they're being challenged to think about who they who they are and how to bring it into their project. And just briefly just to get some high level feedback that I've gotten from the mid semester evaluation students commented on the open mindedness of the lectures and their thoughts being acknowledged interactivity of the class. Engaging despite being online and the two in the bottom that's bolded I just I put those there just to kind of capture a full quote that students had talking about the eye opening experiences and unique opportunities to think about the impacts of on society that they would never have thought of. And then the bringing in of qualitative methods some some students have never thought about the use of qualitative methods or had an opportunity to think how can you bring that into engineering problem solving. And so just to conclude my comments here. I really think it's very critical that we find ways we normalize engaging engineers on social issues present day not the typical things on sustainability. Those are all important. We still need to think about that. But some of the the very human. Some of it might be political. Some of it might be messy. Some of it might be uncomfortable but we we can't stay comfortable. We need to be more intentional. It will be inconvenient if it's too inconvenient to think about some things bring some experts in from outside of the your field. But I think we're in an hour where the students are students are wanting to see how can I really make a difference with what I'm doing and learning. So with that I will pause and allow AJ to transition us. Well thank you very much. Very some of the very great points that you built upon what Joe was making before. So with that, we will go to our next distinguished panelist, Dr. Carl Chanel, Carl, please go ahead. I guess I'll probably just introduce myself first since I think I'm more of an outlier here than anyone else. I'll share my screen and show you a PowerPoint that I've made. And so really I've started out in my career in process engineering. And you can see that a whole place started my career and in a lot of different chemical companies, Monsanto, DuPont went back to school in artificial intelligence and engineering a long time ago and then ended up at Dowling and then finally Corteva where we are now and that's my progression over time for work history for my background academically I went to Vanderbilt and Northwestern and chemical engineering both, and I've really developed a passion, I think for data analytics for using data to make decisions and both in process engineering as well as R&D. So now I'm in R&D for Corteva, which is centered down in Indianapolis, just an hour or two away from Purdue's campus. And I really started to use some tools like Jump for statistical analysis, for designing experiments, for helping scientists and engineers to make better decisions with their data, as well as continuous improvement and lean and Six Sigma and many different process improvement and operational excellence tools to really help R&D. That's just a little bit about my background. What I want to talk today really was driving innovation for food security. And that's really where I've started to do a lot of pro bono work, you could say outside of my regular job. So food insecurity is really defined as lack of consistent access to food for all household members, resulting in limited or uncertain availability of adequate food. So there's many different forms that takes. It's not just the homeless that have no access to food on a daily basis. It could be seniors at the end of the month when they run out of their checks and that don't have enough money for the last few days. It could be in the winter time when people are spending all their income on housing and heat. They don't have enough food. It could be people that are perhaps five out of seven days they can eat the other two days. They really don't know where their meals are coming from. So it's a it's a large issue and it's a varied to lots of uncertainty and variability. So right now in Indianapolis, though, I think we've done some innovative things. And it's really because of these players I've listed here, the Indie Hunger Network. The IHN has been a big help in this area over the last five or ten years. The local government, NGOs and private corporations as well. You can see a couple news items here that the mayor of Indianapolis really has decided to try to sustain the efforts he started. He's not going to be the mayor forever. So he's made a division of community nutrition and food to try to sustain the efforts that the city is doing. There's also corporations like Yolanko that's headquartered here in Indianapolis that really is supporting the food security effort. But I want to talk a little bit about just the Indie Hunger Network and their effect and what they've done because really if you look at the numbers, 10 percent of the U.S. population is food insecure by that definition above, which means one in six households in Indianapolis. That's huge, huge numbers. And that's one reason I got involved with it. So I'm really talking about a probably a household or community type of level referring back to Professor Seinfeld's framework. So it's really something that's passionate for me to try to help and really see if I can apply some of my skill sets as a chemical engineer and as a systems designer type of person in background to help in this area. So if you look at a little bit more of the detail about food and where food comes from for those people that are food insecure, it's very interesting to really understand that if you look at this pie chart, SNAP supplies half of the food. That's a USDA national program along with school summer meals and WIC. So 75 percent of the food for food insecure people in the U.S. really come from U.S. government programs. So local community efforts really need to fill in only that other 25 percent. And that's what it looks like in Marion County at least last year. So I'm saying only 25 percent, but there's still a huge gap. And there's also something that's really inequitable about that gap as well. If you look at some of these percentages that have just been gathered by the Indian Hunger Network this year, looking at equity across different cohorts of the population in Marion County. So looking down at Indianapolis, really Marion County and Indianapolis are the same thing. The government is the same. And we also look at the surrounding counties as well for some of these issues. So the doughnut counties as we call them, but Marion County and Indianapolis as I said are only an hour or two away from Purdue. And being the capital of Indiana and with a large population, then that's why we're looking at food security in Marion County right now. And going back to that equity issue, if you look at that 10 percent national average, really it's much worse when you look at, for instance, Hispanic households at 16 percent, Black households at 19 percent, 28 percent of households with children headed by a single woman are food insecure. So there's huge issues and huge inequalities that we need to start thinking about. And what kind of innovation can we use to help solve these issues? So just to give you some more graphics on the left, just recently because of COVID, if you look at the meal gap is actually decreasing in Indianapolis before COVID. So we were down to 38, 380,000 meals a month gap. So that's huge, but that was decreasing until COVID. And from recent India Hunger Network surveys have been done post COVID. We are doing COVID. It seems like it's almost doubled down because of COVID. So it's not getting better. It's getting worse. And so after COVID, can we bring that number back down? And once we do that, can we bring it down even lower than the pre COVID number? That's our goal. And one way that systems analysis can work is really looking at the food system, which is on the right here. So going from the top right of donors to the bottom left of the hungry, this is basically all the organizations that deal with food insecurity in Marion County and the Donut counties. And a lot of those boxes are really partners in the India Hunger Network. So just to show you some of the things we've done with data and decision making for the India Hunger Network, it's really, his IHN is really a partnership. And it's really started a while ago. And I think that's one of the innovations that have helped Indianapolis figure out how to solve that meal gap compared to many other cities in the US. So I'm very proud to work for Corteva who gives me the time to do this. And some of their sustainability goals are community outreach and food security, as well as being in a community that has the India Hunger Network. And I'll talk a little bit about that in a few minutes here and some of these examples. But just going back through time, what we've recently looked at and helped them with is looking at trying to apply quantitative methods. I think it's sort of opposite, exactly opposite of what Professor Reed Smith just said about, can we apply qualitative thinking instead of quantitative thinking. I'm almost going the opposite direction in these micro situations. The NGOs, India Hunger Network, the partners that work in food security are not very analytical. So I think the systems approach and data analytics approach really helps them. And they have the data, but it's not organized. So it's really the old 80-20 rule, right? 80% of the time is just figuring out what the question is. They want to ask where the data is, can we organize the data, acquire it, clean it up, and then do the math modeling or analysis. So this first one on the left is really very simple. There was one of the partners, a very large food pantry north of Indianapolis decided to deliver meals doing COVID instead of people coming to the food pantry. And so they were delivering meals to all these locations and they had no idea where they were. They had addresses and they said, well, they must be all just centered around the church where the food pantry is, which is the red star. And obviously just plotting this out, we could say no, a lot of them are coming from the north side of Indianapolis. And if you look, there are people in Noblesville, Franklin, Lebanon, way outside of Indianapolis as well, driving all the way in or you're driving out there to deliver food. So that really, I open their eyes. And it's a very simple thing to do once you get that data, showing these maps, the GIS, geographical information systems that we have really helps them understand their data and helps them answer questions. The one in the middle is interesting too. This was a really partnership between Indigo, the local bus line and the Indy Hunger Network. One of the issues with Indigo was that they with cost cutting, they had to get rid of some bus stops. So immediately, the Indy Hunger Network said, well, are you getting rid of bus stops that are near any of our pantries? And in Marion County, there are probably 150 or more food pantries spread out all over the county, which are the red and the green dots on this map in the middle. So what we did was he did some GIS work and overlaid several layers. We overlaid the bus stops in the little blue dots with those locations and then did some analysis to see where are the pantries that are more than half a mile away. And those are in red. So you notice that if you look at all of the red dots are not near any of the bus lines. So taking away those bus stops did not affect the access for people that have to take the bus. So one of the big issues with food insecurity and getting access to food is where are the people in need and where are the food pantries and other sources of food and what is the transportation available to get there. On the far right, we thought in general that urban deserts would be correlated to where there are people in need and where there is food insecurity must be in the urban deserts. The urban deserts for food really is something that's been defined by the USDA as low income and low access areas. And so this is the county again on the right in those green shaded areas are census tracts. Those census tracts are smaller than the zip codes. So at least you can get data down to from the U.S. census down to the census tract, which is a much smaller area to zero in on. So there's no personal data involved, but we can at least get down to that tract. And so if you do some overlays in a GIS system again and map it out and show the India Hunger Network, it really showed that where Gleaners Food Bank was delivering food during COVID was not directly correlated to the food deserts. So you can see in the middle of Indianapolis is a hot spot, but that's not officially a food desert just north of there is. And if you look on the outskirts, there's food deserts all around the outskirts of Marion County as well. And there's also needs out there as well, but there's not a direct correlation. There's lots of need outside of food deserts during COVID. And so that's another interesting aspect that we learned from doing some modeling. Just to wrap up, this is where we really started with the IHN. When they formed, the one reason they formed in Indianapolis is because there was so many NGOs trying to help fill that gap in food. And so for the food insecure, there are enough, there's so much need that everyone was doing their own thing, getting as much resources as they could and delivering it to as many people as they could. And they didn't really care what everyone else did in the community because there's such a great need. But eventually they realized, well, we need to solve the meal gap. If we get down to zero, how do we do that? We have to collaborate. And we have to have partnerships. And so that's when the Indian Hunger Network formed. And that's, I think, the innovation really in Indianapolis is we have that network of food insecurity, food security people working in the area. So on the left here, we plotted out census tracks with people in poverty below the poverty level and looked at some of the largest food pantries. And those are in the green circles here. And they're, I think, with a mild radius around them. So you can see they're not really in the places with people in poverty. And the other thing you noticed, downtown, we had three large food pantries, but people in poverty are spread out all over Indianapolis. It's not just downtown. They're way out here around the edges as well. And so what we did with some sophisticated supply chain modeling, we looked at looking at like a cog and spoke model is what I call it, but a supply demand model where we could match people in poverty by census tract to where are the food pantries. And so this middle star diagram is really an optimal placement. So they were thinking we had what six large pantries, if we had three new large pantries for total of nine, what would be the optimal placement of those pantries in Marion County to hit those census tracks that had the highest poverty levels. And the optimization would be then to reduce the travel time or the distance between those census tracks and where we would place those pantries. Of course, this is optimal. And you can't go to a corner in Indianapolis and tear down a building and put up a food pantry. And so we take this and we looked at what was reality, which was over here. And there were some very, very large pantries in the middle, the largest pantry in Indiana. It's probably St. Vincent de Paul right down downtown. So what you noticed was there's nothing on the far south. There's nothing in the northwest as well up here. There's nothing up here, nothing down here. And it turns out there was a church in a hospital in Corteva up here that said, hey, maybe we can solve this issue. Let's put a large food pantry right here. And so because of this modeling, we actually put a large food pantry here a few years ago. So that was a big win because of the data analytics and the mapping and the supply chain modeling. And the other thing just to point out on the right, while I'm wrapping up here is that we also looked at just those different people supplying food to those in need in Marin County and where the overlapping gaps were. And one of the interesting things that really motivated different groups in the honey hunter network to get together and communicate and have meetings and really coordinate how they deliver food was this map because if you looked at meals delivered to seniors in poverty, then there's several, there's several organizations, second helpings, Meals on Wheels, Sokoa for the big three. And when you zoom into downtown, we noticed that all three of those suppliers were actually on the same corner in Indian Alps. So they never knew that until they saw this map and started talking because of Indie hunger network. And so maybe one of the things they should think about as well, do we really need to have three different facilities or places where research seniors lunch at the same corner? So let's see if we can, how do we cover Indianapolis better and get rid of some of those overlaps? So those are some of the ways that we can help with decision making using data. And it really turned out that all the data that we've had so far is we can give them bar charts and analysis, but the way to really show the people involved and to motivate them that, yes, to make decisions is to use these kinds of maps. So we've really started to try to figure out how can we show maps and make decisions with data and then display it with these kinds of maps to make it very obvious for the people involved. So some of the things we've learned over time are really connectedness. We're all connected and we need to be more inclusive, I think the meal gap might be solved, but it could come back again, just thinking we were really doing really good before COVID in Marion County. And we're not doing well now. So can we go back to where we were pre COVID? And once we get there, can we solve the gap even further? Partnerships are key as we saw in this Indie hunger network. And for me personally, it's really finding the right data analytics and the people involved at in R&D with the skill sets helps serve to solve the issues that people have in Indianapolis. So we need new approaches to solve inequities. And we need diversity in innovation to solve those as high old issues. So there's still a lot of work to do. And I'll turn that back over to AJ. Thank you. Well, thank you, Carl. My excellent key study and what data can do to reach out to bridge the equity gaps or inequality gaps. So with that, we have another expert coming up in data. And Dr. I appreciate you being there because you're going to talk how good the data could be and what data means in the context of healthcare, which is another important parameter, the foundation of the mass of a pyramid. And as so, please go ahead and share your case study. And as she's about to share her slides, I would like to make a comment that they did all started for me is they're going to start telling people that the challenges exist in America, not far from where you live. People always believe that there's somewhere overseas in Africa or in Asia or in some parts of South America. And I would like to echo and register this today. We are talking about America and our fellow citizens right here. So with that, without further delay, you're please go ahead. Thank you. Thank you, AJ. I really want to echo what you said, given I have worked in Africa as well as in the US health system. That's well said. Thank you for having me. And I would like to start with my talk that was my personal story. Back in early 90s, I have experienced working with a factory basically at the time. I am really excited of applying artificial intelligence and using neural network in real time scheduling algorithm and fully on the fully automation process working on scheduling problem. And so one of the factory actually is interested in some of the algorithms and they do have some sensors and barcode system. And that is one of the things that probably become more of the norm now, the industrial 4.0. I think that's sort of the trend now. That's become more of a norm. But back in the 90s, that's still kind of a new thing. So at the time, we have sort of do a trial. And one of the things we need is the data was a real time data. And so we'll be able to see what is the status of the at the moment. And my algorithm be able to tell will be the best strategy to schedule that particular parts. What happened is we found there's no activity in the underlined until the end of the day. So what happened is there's nothing coming through until the end of the day and suddenly there are 400 parts showing up on the data. And so we kind of wonder what's going on because my algorithm basically will not work if you don't have real time data. It's not going to work. So we did a little bit digging and then we realized, you know, people actually don't want to scan the parts as he comes. They want to scan it at the end of the day because to pick up a scanner and scan the individual parts is quite time consuming. So they don't see the point of scanning them until the end of the day. So what happened is all your data show up at the end of the day. So that was a fail. So funny story. 15 years later, I walked into the hospital. I saw a nurse with a scanner with multiple pages of barcode printed and or sticker sticker on the paper. And she was scanning on multiple pages of barcodes. And I was asking her, what is she working on? And because she was trying to concentrate on the computer system and then try to scan the barcode. It turns out she was spending time on scanning the barcode that's supposed to be on the patient's wristband. That's what you see here. So the barcode is supposed to identify the patient and also the medication that is given to the patient. However, at the time, because the computer system was on wheels, they called a cow computer on wheels, was a little bit too big that she couldn't wheel that into the patient bed next to the patient. And the scanner was just a little bit too short that she can't reach the patient. So she couldn't achieve that in the patient room. So it turns out she was sitting at the desk and tried to scan that to finish that at the end of the day. So just to share those two stories, to see how the data actually entered to the system and that eventually come through our data set. And that's what we see eventually. And of course, some of you may argue that's a data quality issue. Some of you may say, well, that would just be a better training as people do better. Let's hold our thoughts and maybe explore a little more. So my background, as I mentioned, I actually started with a manufacturing background and I have switched to healthcare in 2004. And I have an opportunity working in some of the health systems in Africa, including Kenya, Uganda, Malawi. And I also have some of the experience working with a healthcare system in the US. I just list some of the projects I have done here. And as you can see, it's very broad. I work with pediatric, I work with elder adults type of project. I also work with a community, work on some of the disparity issue. And some of the last two I want to pay attention to, let list the last two, is more on the policy part of it. I actually look at the policy that Vienna has and look at how the policy actually affect people. And some of this is affecting the stroke patient and how we take care of the stroke patient if they do have bypass hospital protocol, if they really pass that rule and what will impact the rule of residents. And the other one is something to do with they have changed the rules to regulate how some of the prescribing rules in Vienna and how that impact the outcome. So I kind of looking at this from the system point of view. And I want to share with you with my experience, people are very excited about data in healthcare because there's so much, there's just so much data there. We have claim data, we have patient data, you were talking about hundreds and thousands of different data field that you can dig your hands in and then apply all kinds of tools that you can ever think of. Imaging data and laboratory data, basically everything you can imagine is there. So here I wanted to just kind of step back a little bit. At the end of the day, we wanted to improve care. We want to improve quality of care. We want to improve safety. We want to improve access to care. This is actually something I really wanted to emphasize because there's still a lot of people don't have equal access to care. And also the equity part of it is actually do we actually provide good care for everyone regardless their race, their education background, their income level. Do we actually provide good care to all of them? So here I want to kind of ask a question. What are we considered evidence because data it is something we consider objective most of the time and how much, how objective it is. So this is a part I want to sort of walk you through about the human factor part of it in entire chain of data including how we design in the initially how the data was designed to be collected and actually initially the intent wide of data being collected. And this is an example of health data for example. Health data initially was for patient care. Doctors just jot down notes to make sure they remember what they have done in the encounter with the patient initially. Eventually evolve to billing. So this is actually evolve into billing the insurance and really need a record to understand exactly what kind of billing need to be done or can be paid and also evolve to reliability. So the lawsuit issue so they need also a good record to prove what has been done when it's not done. So their intent of what kind of record being being recorded it's really you see this list there's nothing to do with for data science to analyze for better care or better treatment because it was not really collected for that. So we just need to understand when we analyze the data it wasn't collected for that purpose it's really collected for other purpose. Now so given that contest all the design of data collection and who is collecting the data is all done by humans. So in the way we also need to understand how that also creates bias because every time we design something there's a system of process we all can think of is how that person will imagine how things will be done. So mostly we will think about the norm the majority. Now whenever there is people don't fit in that majority it will consider they break the system meaning they don't fit into whatever the framework that we designed and quite often it become a challenge in the data collection process meaning they may not fit into the field that we designed it for or they don't come in the right time and place when we need to collect the data. So this will also affect the quality of the data we collect and they may also compromise the quality of data for a particular vulnerable group because they don't fit into the norm that we will anticipate. Now I want to come down to the last two about data processing analysis because this is a part most of us are doing as an engineering scientist that we get a set of data we get excited and the first thing we do is we clean the data we want to make sure the data quality is good they are consistent we want to make sure they actually combine a different data set they actually can talk to each other they actually make sense. So the first thing we do is clean the data make sure they are taking out all the things that incomplete the things they actually are matching. So in that process we actually introduce some of the biases already and I just want to take that as sort of my own lesson learned what happened there and also small sample size quite often got ignored in the sense that statistically it's not going to be significant and they either being seen as outlier or they just become part of the error. So this is just one recent case I am working on it's a pneumonia case that I'm pulling more than 1.3 million cases out of the data set and you know by the time I look at all the criteria I need it went down to less than 3% of the data actually satisfied all the criteria I need. Now yes I need all the data set that satisfy the criteria but as you can see there's some comparison here the ratio group representation is start to change a little bit. So in the original data set you see on the right column you see that the percentage representation start to change if I narrow down to what I need and as a data scientist when you want certain things you kind of start to say well this is incomplete data set so I'm going to exclude this this data didn't have that information so I'm going to take that out so by the time you do all that you're actually selective about what you're looking at and I just want to point that out I don't have all the solutions yet but I'm just want to point that out that quite often we're doing this data cleaning but already we are being very selective and and what it changes shape of the data set implicitly even without knowing. Now this is another part about health data we have quite a bit of people that are in uninsured and a lot of them also don't interact with health system that means they don't even come to the hospital they don't come to see a provider so that means we never have a data about them and that doesn't mean they didn't get sick that doesn't mean they don't need help it just means we have no sensor at all about their health condition and we have no data and this is another thing we have to be aware that when we do data analysis what is in the data is only a bias data set with people actually have opportunity to interact with the health system so this is sort of a broad system view and I kind of touched some of the things I already talked about provider on the right side patient on the left side there are some you know personal bias liability there's a lot of things that inference how the data will come together to put into the data warehouse on the bottom but I want to pay it want to draw your attention to a few things the data warehouse on the bottom left actually when we did analysis there will be some selection analysis bias as well because we use some tools and we use the AI to do analysis but before we do that we already did some you know filtering we do selection we do inclusion exclusion criteria so that's already do a subset now after that when we did that we already school some of the population and of course there people didn't even enter the data so they are not even included now based on that we will come to risk factor was on the red box and that risk that risk factor will inform insurance payment structure meaning what kind of treatment will be paid will be reimbursed and also will inform on the right lower corner the preventive care guideline meaning what kind of preventive care should be done and will be paid for were being reimbursed and then on the left side it will give you sort of guideline for what is the best practice also how the physician will be treating a particular disease now imagine there will be a group of patients that never enter the data into the system or the end the minority that is not being analyzed or being treated as aired that is not going to be represented in this risk factor and that means they all the system is not going to consider them as one of the beneficiary in a sense so systematically we already create a system that basically don't consider them as they will benefit from this this particular whole system so you will see this is how the disparity is going to get worse and worse because the health condition probably will get worse because the system is not really supporting them so this is where I see as a sort of a data person do no harm actually is a big calling I'm not even sure I can do it because this requires a lot to be responsible about knowing and do do diligent about knowing you know what kind of data what does that mean where it come from how do we even answer some of the questions and when we draw a conclusion how do we draw a conclusion carefully and be careful what we said how do we set it in a way that's people don't misinterpret it so with that I will give my time back to Ajit well thank you again and this is being on the journey going from where we are so co-awarding then going to Joe in terms of the rich model that he has built based on in years of experience in public and private vote then Tahir speaking to that that how it comes to now the design and the engine solutions and the technology gaps to excellent examples what in the new york another place in the classroom then card you spoke about how data science can be used for reaching out to really the people those are in need of food and in fact eliminate some of the perceptions as well and then you weren't you spoke about how data need to be handled with precision and care if I may summarize then you know probably much naive if I may say so with that there are quite many questions in the in the chat box obviously and what I would like to do is some of those questions those are critical so probably you make an excellent point about accessibility of innovations and a wholeheartedly I can speak we talked about that before many times among panelists and otherwise technology that just the tip of excellence is not enough accessibility is very critical in fact for bridging those gaps so thank you for that question at the same time professor go asked a question about entrepreneurship today entrepreneurship is one of the means in fact translation of science to apply science to the purpose of the society and that's what we this team of this so is entrepreneurship is a mean to that end goal absolutely it is a part and responsibility for engineers as well I do not know how you separate two things apart and Purdue is one of the places that is recognized nationwide for multiple spin-off companies and take transfers successfully so absolutely by all means and I know Tahir you are smiling so I know wholeheartedly we all agree to that point of translation and impact so there are other questions for on complimenting presentations and some highlighted Julius highlighted drones are already delivering blood in Rwanda and again we have think about all that how we can use this technology so people have access and they get access that access is a two-point side they have access to use them and we provide and train them to get the needs to them to those technologies there was a class thank you Tahir for responding about the ME597 class the answers are there and then Dr. Mukherjee you asked a question can equity be improved by working with cutting-edge technologies or those technologies need to think about equity innovation all together from bottom up so this is an excellent question to make engage panel panels and I would love to hear so question is can equity be improved by working with cutting-edge technologies or those technologies need to think about equity innovation all together from bottom up so as Professor Raman said rethinking so this is an excellent question so let us go through that point of rethinking any colleague would like to speak to that well maybe I can jump on first and then people can add I say talk about it earlier definitely technology could assist in identifying the equity issue or disparity but that also has a fundamental issue of accessibility if the you know the fundamental issue is that people don't have access to start with and and solely imagine so this is where it come into people are thinking from their own perspective so if the design is not really putting the context of how what people really need and how would that fit in to support what their daily life and simply just thinking from the technology point of view sometimes that could do more harm than help in my opinion over well thank you if anybody would like to comment on that I would add a comment maybe that it is really about the problem it is not about the solution we have been trained quite a bit to come up with the solution we get graded one thing I admit to my class is that I really wish I can grade you for the problems you ask and not about the solutions so we are very programmed from a very younger age to how to come up with the solution so it is not about the solution it is about the problems so that's where I would I would I would probably keep an open ended answer maybe in many ways so one of our students asked the question can we use data driven analysis to develop an engineering solution for distributing slash decreasing food insecurity in the region is it necessary to create a novel engineering solution for delivering food to the insecure households or can we develop a mobile food bank so card this might be in your view house and an experience that you could share mobile food bank or solutions like those what do you think about yeah that's a that's a very big question with multiple parts so that's a good question but yeah I think so actually because of covid those things are happening already and so there are mobile food banks going out I know Gleaners food bank is in Indianapolis and they're very big and they cover a wide range of counties not just Indianapolis and they are that's exactly what they did because of covid and they've started to do some mobile deliveries because transportation for those in need is always an issue which is why we looked at that model of the indigo bus routes so that's that's been a an issue before and I think because of covid actually a lot of the partners in the India hunger network have really started to think about how can we be more resilient to sort of answer another question at the same time how can we be more resilient now and so because at the beginning of covid everyone was all hands on deck right it was an emergency they were predicting or that could be double the need which there probably was and how are we going to get double the food our warehouses are full or pantries are as full and with the volunteers as much as we can how are we going to double things and they tried to do that and they did that and the National Guard came in and they had big programs and convention centers and things and so they they sort of learned under fire I think because of covid and so the next step post covid is how do they stay resilient for the next big thing that happens whether it's another pandemic or something else and so I think they've learned to be more resilient and the other thing to think about is that there there are networks of food banks in the US there is a large network so they do communicate and I'm not sure how much analytical thinking they really do and so that might be something that to pursue in the future sort of what I like what I've done here on a micro scale with the indie hunger network but no I have not looked at that and not known what that overall system looks like but I think that they do talk and communicate which is the first step at least have that network of communication as most of the presenters have talked about to the soft skills there before you can think about the quantitative modeling but in the and like I said a lot of the large food banks with cleaners as a as an example really do cover a large area so pretty much they're trying to optimize you know central indiana there's probably only four or five food banks in indiana and then they have associated food countries that they supply and so I know it and in Lafayette you might have a food bank that covers many of the counties around around Lafayette and West Lafayette so I hope that answers the question but yeah that sounds like that's a great idea so thank you Carl and again I just want your time will be encourage people to pose their questions the professor agonifer highlighted that the grand challenge scholars program gcsp was created by the national academy of engineering to prepare students to help solve some of the largest challenges facing society and he asked the question that and there is a description you can read but can this program be integrated in engineering science courses with potential to address global challenges so to her to the class that you are teaching in this summer I experimented with the class in the similar areas so can a program like this where engineers are made aware about the grand challenges in the nation and across the nation would love to hear your perspective because this is something important as an education leader here at Purdue sure certainly it can be but the question is we all know that when we teach our courses we already kind of have things mapped out we have our curriculum all figured out it it would require effort and intentionality from the instructor and let's be honest once once we have our notes that work we just like to use them again and again I've done it so that's that's what will be the challenge is you we can do anything and then there's often discussion about well there's no room to fit yet one more thing but the real answer is it can be done it's just a matter of is there can there be time can can the instructor have the time or make the time to do that I think it can be done so and AJ if I could perhaps just build on to hear his comment there's a tremendous opportunity I think to continue in that direction you know one of the things that we've begun to work on here at Purdue in the College of Engineering is a minor focused in innovation and transformational change which is really driven toward this notion of addressing some of these complex grand challenges and I think to hear his point it's very difficult to incorporate this content into existing courses we're always struggling in the 15 16 week time frame but we have a large set of electives often that are associated with different engineering curricula and if woven together effectively from an early point in the curriculum there's a chance for students to come away with a pretty complete view the minor that we have for example is actually 18 credits so it's a pretty extensive minor and it you know it involves six courses and students really need to start that in their sophomore year first semester in order to complete that exercise but if they can they'll get perspectives from anthropology and sociology entrepreneurial views economics engineering and and design and they could bring all that together to shape their perspective on the way they could address problems in the future I think it's a challenge for for academia to try and find ways to bring those multiple perspectives into our curriculum thank you yeah if I could just follow up to that as well um just a general comment about the grand challenges so yeah there's 14 of them and I know the USAID has some grand challenges what I think is still missing is maybe some challenges that are still very important but maybe they don't have they don't fit perfectly into the grand challenge areas um you know I mean I like to think about very practical problems and how do we for example you know the problem with poverty in the United States and I know AJ you already think about that in different ways but what if there were some other types of challenges that are like your day to day like you can walk and see that like you know single mothers that can't go to school because they don't have childcare and it sounds very social right well that let social workers take care of that or I mean just there's so many other ones that don't fit perfectly into these classic grand challenge areas it's to the point now grand challenges are almost becoming cliche but what about some of just that just your local neighborhood or maybe not your local neighborhood drive somewhere and go to someone else's neighborhood and look at what are some of the issues people are dealing with that engineers can yet get their minds engaged in um you know this topic of it's a hot topic the whole policing and and how can we impact that that doesn't I don't I mean I have to check where which grand challenge that might fall under but it's still an important problem that you know people are concerned about so so how do we how do we make opportunities there as well and then it gets it gets challenging when you start getting into those spaces as well um I think most people don't like to touch hot topics or political topics um but sometimes you might need to I think engineers we have the mind to think about anything so may I just make a comment actually they're they're already student taking some of those initiative uh just you know for example full security I know there's a group of student actually take that initiative look at the full security issue at Purdue so you know that some of those the issue you mentioned you know they are people are really passionate about it and they will make you know take action and you know gather the group of people that really want to do something about it and and they make the move so I think it's not nothing is too small and in my opinion it is a matter of you know if you're interested in taking an initiative and work on something you can always you know get a small working group together and there's always student looking for this kind of topic they want to help and and I can tell you that their student already have those interest group working on those topics well thank you for highlighting there I know also at Purdue we have a very large global footprint it is almost a little united nature of students here that we have representation from pretty much every country so that is a very rich environment for that also I would like to acknowledge that one of our attendees is from the Macarena University all the way in Uganda and it is a midnight so Julius we appreciate your participation that just makes me very excited about what we do as educators so thank you for joining us and at the same time I would like to acknowledge that where we are in EDM what people call we are in flyover states so it is about that certain wealth goes from the east coast to west coast and we are left so it is very important for all America so Tahir I'm glad you mentioned about political angle as well it is not a red or a blue issue it is a red white and blue issue and I just want to echo that also we are focusing today on food insecurity but if you look at the five parameters and I would like to register that because this the dialogue is just initiating we have insecurity of food we have insecurity of housing we have insecurity of clean water and air we have insecurity of access to education during COVID-19 thousands and thousands and thousands schools because of lack of internet and access challenge to the education fundamental needs and the last which is at foremost right now that we have inequity gaps in access to COVID-19 vaccine so you know we really need to think ourselves that what we are inventing and what we are discovering and what we are innovating does it matter because if it does matter we have great opportunity to help our fellow citizens we still have six minutes left so I would like to actually ask an important question to engage all my colleagues there's a difference between frugal engineering and frugal engineering that can be performed so I would love to hear everybody's viewpoint because we are by profession scientists engineer entrepreneurs but what is frugal engineering and do you see frugal engineering as an avenue to access to provide access to the highest technology that the brightest might invent and then make them accessible and applicable to the parts of the population and the society who could be brought along and pull many out of poverty or provide them access to these five things that I mentioned so how do you see the role of frugal engineering in the context I know it is a big question we have a limited time but I would love to invite some thoughts I think I'll start so frugal engineering I think what's good about it is that it challenges thinkers to really concentrate on sort of like what is the most the most viable solution low cost easy to implement which is always the goal of engineering cheaper better faster it could be very challenging because to find an elegant solution can be very difficult and simple solution very difficult and so if we can educate and train our students on how to think about frugal engineering applications I would think that the implementation and the quickly how quickly it can get out or be deployed would be increased as opposed to more expensive clunky solutions that you often see you know large budgets to create something it might be easier to do that but something that is you know economical that might be difficult and it is but it's valuable to learn thank you Tahir anybody else would like to speak to that because sometime funding agencies may not want to fund the frugal side of the innovation it has to be a hypersonic or it has to be artificial intelligence and I would like to have something that I can go to Marion County in Indiana and really help my fellow citizens yeah is it a contradiction or is it the synergy in some ways well I can jump in next I think AJ I will follow what you say earlier get back to you on it really depending on the problem and I think sometimes that could be in a good option but sometimes that actually in long term may cost more money and not really address the problem so I think it really depending on what is really needed and sometimes it really take a big investment to make fundamental change and that actually in long term will be better off and but sometimes it could be a simple solution and sometimes the solution is about what people can adapt to what people can accept as what Tahir was saying earlier right you could have a very nice solution but nobody's ready for it that may not be something could actually help so I think it's what is it's what people ready to accept sometimes that could be also important but maybe there's something completely new that nobody know what it is but have heavy investment but that really completely change how people think how people live and that might be in long term is it's a better way to make that change also so it really depends I can see Joe how you want to say something there Joe you have a couple of minutes and I know you have years of experience in this at least few comments are most welcome thank you yeah certainly no I could definitely build on both what Tahir and and you and said I think the frugal innovation is a path one of the exciting paths I think to addressing some of these challenges but the innovation itself needs to be coupled with more of a systemic view of the problem so that we can drive adoption make it economically sustainable get leadership to buy into the ideas and I think you know even taking a look at the recent innovations that have affected the COVID pandemic really highlights this issue we had two extreme innovations you know at two different ends of the spectrum really be very influential here one is vaccines and the other is the mask as you highlighted earlier AJ and in both cases there are issues around access there's issues around knowledge and awareness and concerns about the effectiveness of these solutions there's tremendous issues around leadership engagement and in support for the initiatives and what that does to the community's desire to adopt and then there are challenges of sustainability and achieving reach at scale and so if you will the same factors come up over and over again in these issues but they are things that we can consciously think about designing for be it through a frugal mechanism which has perhaps enhanced potential for sustainability or by leveraging more advanced approaches that might be useful in certain contexts but thank you dude and I will write at five so Carl you are a distinguished gift make a quick comment if you want I was just gonna say industrial perspective I think of frugal engineering or innovation a little bit differently probably because I'm biased from my engineering background in sort of decision making and problem solving but now applying that more to continuous improvement operational excellence with respect to research and development in industry we're sort of going to where can we get innovations for free right where can we get frugal innovations don't cost much so go do some research don't spend a lot of time or money and find some new products for us and so when we're looking at industrial research especially in in discovery based research like agriculture and seeds and traits that we do that's really where I think of innovation in sort of process development innovation in how we think about getting new innovations and new products to market we're a lot like pharmaceuticals like vaccines and we don't have the resources to get a new product out in one year like the vaccine we're recently but typically that's what eight to ten years to get a new product so how can we innovate and do that in half the time and that's that's more productive more efficient and that's frugal engineering with respect to new product development and I'll stop there well thank thank you Carl and we are one minute over and I wouldn't propose take time so to thank Tahira you you and you Carl Joe and Irvin yourself and one important person I would like to recognize is Stephanie Stephanie McKinley has done all the great logistical and why I've been planning behind the scenes so big hand to Stephanie as well in the process and many people who have invested their mind in making ER2C series successful please keep in mind that going forward this is just taking off the ground our first webinar there will be series of activities in the future as well but as I said my life achieved goal is that when I tell people I work in the technology they don't raise eyebrows they look at me that I am accessible and not inaccessible and that is something we can do by frugally please recall that frugal engineers have won Nobel prizes of the world so with that I would like to thank you all for your compassion with the hearts of servant leadership and with the interest in innovation that how we can help our fellow citizens for food shelter water energy medicine and education with that thank you again have a safe and great evening and the rest of the week thank you all