How Purdue is Using AI for Good — Computer Science Professor Alex Psomas Explains

Summary

Summary of "How Purdue is Using AI for Good — Computer Science Professor Alex Psomas Explains"

Podcast Information:

• Title: This Is Purdue
• Host/Author: Purdue University
• Description: This podcast highlights stories of Boilermakers who leverage research, innovation, and determination to make significant contributions across various disciplines.
• Episode: How Purdue is Using AI for Good — Computer Science Professor Alex Psomas Explains
• Release Date: April 3, 2025

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Introduction

In this engaging episode of This Is Purdue, host Kate Young interviews Assistant Professor Alex Psomas from Purdue University's College of Science. Professor Psomas discusses his pioneering work in using artificial intelligence (AI) to combat food insecurity, focusing on the Food Drop Initiative. This initiative exemplifies how AI can be harnessed for societal good, particularly in optimizing food distribution to those in need.

Alex Psomas' Background and Journey to Purdue

Professor Alex Psomas shares his journey from his childhood in Greece to becoming a distinguished computer science professor at Purdue University. Growing up in a close-knit community, Alex was always fascinated by how things work, a curiosity sparked by his father’s work with stainless steel tanks. “I was always into how things work,” he says at [03:29].

Migrating to the United States for graduate studies, Alex attended Berkeley before joining Purdue. He recounts his first impression of Purdue as a place where impactful research coexists with a supportive and stress-free environment. “[...] it felt like everyone felt supported. My experience as well is the same. So it has lived up to this expectation” ([05:54]).

Understanding AI: A Broad Umbrella of Tools

When asked to define AI for beginners, Alex emphasizes that AI encompasses a wide range of tools and applications. He likens AI to an expansive category where diverse technologies fall under a single umbrella. “AI is a set of tools for all sorts of things. The AI umbrella is so large, so many things fit under it” ([06:42]). He highlights that AI includes reasoning, learning, knowledge representation, and language processing, among other functions.

The Food Drop Initiative: AI Tackling Food Insecurity

A significant portion of the episode delves into the Food Drop Initiative, a collaboration between Alex’s team and the Indie Hunger Network in Indianapolis. This project aims to reduce food waste by efficiently matching surplus food donations from truck drivers to local food banks, thereby preventing edible food from ending up in landfills.

Alex explains, “The idea was very simple. You had these truck drivers... and instead of taking it to a landfill, now you're matching the food to food banks” ([09:53]). The initiative utilizes AI to automate the matching process, ensuring that food distribution is both logical and fair. This automation removes emotional biases from the decision-making process, allowing for more equitable distribution of resources.

Impact and Scalability of AI Solutions

The implementation of AI in the Food Drop Initiative has several benefits. It significantly reduces the time and effort required by human operators, allowing food banks to serve more people effectively. “We can process donations a lot faster because a human doesn't have to sit around trying to call people on the phone” ([14:05]).

Furthermore, the model developed by Alex’s team is open-source, enabling easy scalability beyond Indiana. Organizations in other states, such as Washington, can adopt the same system with minimal adjustments. “They didn't have to train anybody. They could just immediately take this on because there's nothing to do, there's nothing they need to learn” ([14:31]).

Ethical Considerations and Fairness in AI

Addressing concerns about ethics in AI, Alex confidently asserts the fairness of his AI models. He explains that by abstracting real-world complexities into manageable models, fairness can be mathematically defined and proven. “The only disagreement can come... whether they disagree with what I mean when I say fair” ([16:17]).

Alex emphasizes the importance of transparency and openness in his research, inviting scrutiny and feedback to ensure ethical standards are maintained. “Because everything is open, you can go read my paper. I'm open to feedback” ([17:24]).

Mentoring and Teaching: Inspiring the Next Generation

Professor Psomas is passionate about mentoring students, encouraging them to pursue what excites them rather than directing them toward his own interests. “I do something that is very difficult for them. So they're all very annoyed, which is tell them everything under the sun that I'm excited about, not one thing. So, you know, 50 things and then let them pick” ([18:15]).

He believes that fostering student interest and passion is crucial for innovation and progress in rapidly evolving fields like AI and computer science.

Purdue’s Leadership in AI and Computer Science

Alex highlights Purdue’s longstanding reputation in computer science, being home to the first computer science program in the U.S. This legacy positions Purdue at the forefront of AI research and innovation. He points out the diverse expertise within Purdue, from computer vision and robotics to AI security and human-computer interaction. “Computer science has a really big place in Purdue as a whole. Purdue is at the forefront of all sorts of things” ([20:56]).

Purdue Computes Initiative: A Strategic Approach to AI

Professor Psomas discusses Purdue President Meng Chang's strategic initiative, Purdue Computes, which focuses on expanding computing-related research and education. This initiative encompasses four key pillars: computing, semiconductor research and development, physical AI, and quantum science and engineering. Alex appreciates the collaborative environment fostered by Purdue Computes, enabling interdisciplinary projects and leveraging collective expertise. “Having all these cool people doing cool stuff makes everything go a lot faster” ([22:21]).

Future Prospects and Continued Innovation

Looking ahead, Alex expresses optimism about the continual advancements in AI and its applications across various sectors, including agriculture, medicine, and robotics. He credits Purdue’s collaborative culture for driving these innovations and maintaining the university’s leadership in the AI landscape. “[...] we're really a part of all the exciting things that are happening in the AI space” ([25:08]).

Conclusion: Embracing AI with Open Minds

In closing, Professor Psomas urges listeners to move beyond AI buzzwords and embrace learning about the technology. “Be less scared and more open to learning how these things work because, you know, it's not going anywhere” ([25:33]). His message emphasizes the inevitability of AI integration into daily life and the importance of understanding its capabilities and limitations.

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Notable Quotes:

• Alex Psomas on AI as a Tool: “AI is a set of tools for all sorts of things. The AI umbrella is so large, so many things fit under it.” ([06:42])

• On the Food Drop Initiative: “Instead of taking it to a landfill, now you're matching the food to food banks.” ([09:53])

• Ethics and Fairness in AI: “The only disagreement can come... whether they disagree with what I mean when I say fair.” ([16:17])

• Mentoring Philosophy: “I do something that is very difficult for them. So they're all very annoyed, which is tell them everything under the sun that I'm excited about, not one thing.” ([18:15])

• Embracing AI: “Be less scared and more open to learning how these things work because, you know, it's not going anywhere.” ([25:33])

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Conclusion

This episode of This Is Purdue offers a comprehensive look into how Purdue University is leveraging AI to address critical societal issues like food insecurity. Through Professor Alex Psomas' insightful discussion, listeners gain an understanding of the practical applications, ethical considerations, and collaborative efforts that drive meaningful change. Purdue’s commitment to innovation and community support shines through, showcasing the university as a leader in the responsible use of AI for the greater good.

Transcript

[Alex Famas]

This is Alex Famas, and you're listening to this Is Purdue.

[Kate Young]

Hi, I'm Kate Young, and you're listening to this Is Purdue, the official podcast for Purdue University. As a Purdue alum and Indiana native, I know firsthand about the family of students and professors who are in it together, persistently pursuing and relentlessly rethinking. Who are the next game changers, difference makers, ceiling breakers? Innovators? Who are these boilermakers? Join me as we feature students, faculty, and alumni taking small steps toward their giant leaps and inspiring others to do the same.

[Alex Famas]

AI is a set of tools for all sorts of things. The AI umbrella is so large, so many things fit under it. In our application, we were trying to solve a very specific task, which is match food donations to food recipients.

[Alex Somas]

The other thing is now you can scale. Now you can just go beyond Indiana. You can include more food banks. You can process donations a lot faster because a human doesn't have to sit around trying to call people on the phone and so on.

[Unnamed Host]

In this episode of this Is Purdue, we're talking to Alex Somas, assistant Professor of Computer science in Purdue University's College of Science. Alex and his team at Purdue are using artificial intelligence to to fight hunger. That's right. This Purdue researcher is helping to solve one of the world's toughest challenges, food insecurity and hunger. Now, we've all heard so much about AI in the news recently, and Alex is going to dig into this technology and how he leverages AI for good to solve societal issues right in his own Indiana community. You'll hear about Alex's AI models, used to outsource decisions to make more informed and fairer choices on how best to distribute food among food pantries in Indianapolis. Plus, he shares how this model can scale to other areas of the country as well. By the way, this interview was extra special for our this Is Purdue team because Alex was the first Boilermaker guest to join us in our new podcast studio. You can check out more on our YouTube page, YouTube.com/atsign. This is Purdue. Okay. First things first, though. We kick things off with Alex's life growing up in Greece and his journey to becoming an assistant professor and researcher more than 5,000 miles away at Purdue University in West Lafayette, Indiana.

[Unnamed Interviewer]

Alex, thank you so much for joining us on this Is Purdue. This is our first interview with our new studio set, so we're really excited to have you and show off our studio, too.

[Alex Somas]

Awesome.

[Alex Famas]

Thank you for having me. It's very exciting.

[Unnamed Interviewer]

So tell us a little bit about your childhood. Have you always been interested in technology? You're a computer science professor and researcher. Is that something, growing up that you've always loved computers and technology, or how did that start for you?

[Alex Famas]

Yeah, that's a difficult question to answer. So my dad was a welder, and later on he had his own business of making stainless steel tanks for, like, wine and oil and these sort of things. So, you know, and even As a kid, 8, 10 years old, I would go and like, sweep floors or whatever. So, you know, I'm kind of used to growing up with like, large machinery around, so to speak. And I was always, you know, he would show me around. I would always, like, be curious about how things work. And, you know, I had a computer at home that I would take apart and put together because I had nothing else to do, I guess. So I was always, you know, into how things work.

[Unnamed Interviewer]

Kind of tinkering around with computers.

[Alex Famas]

Yeah, exactly.

[Unnamed Interviewer]

Give us a little bit of background on what sparked you and your interest in wanting to, like, give back to your community. Was that something that interested you when you were younger as well?

[Alex Famas]

So I'm from Greece. Greece is a small place, and like all small places, community is important. You know, I was part of many communities growing up. My roaming community, my high school community, my college community, family, you know, later on, grad school, the theory community and so on. And, you know, looking back at my life and experiences, every time I'm part of a strong community, that's when I'm the happiest. And of course, part of being part of a community is not just feeling supported, but also supporting others.

[Unnamed Interviewer]

Okay, so you're from Greece. We have to hear about your journey to Purdue and how you wanted to become a boilermaker. How did that happen in Greece?

[Alex Famas]

The paths are somewhat structured. Okay. So up until high school, even if you're in a small village, an island with 10 people, or you're in downtown Athens, everyone does the same books. Okay. Everyone's very uniform education right up until last year of high school, where you have to make humongous choice of where to go to college. And it's very specific. So if you pick to be a math major, you're looking at 40 math courses and nothing else.

[Unnamed Interviewer]

Oh, wow.

[Alex Famas]

Science major. 40 computer science courses and nothing else. You don't really get to pick inside of college.

[Unnamed Interviewer]

Not very well rounded.

[Alex Famas]

Yes, yes, very one street. So it was a very hard choice for me. I was thinking between math, computer science, mechanical engineering, coaching, everything under the sun, you know, I picked computer science because I'm greedy Looking at the curriculum, it looked like it had the most loopholes to learn other things. It had a lot of math. You could do more physics if you wanted. You can do more coding if you wanted. So I thought it was a good way to decide later. And in college I fell in love with algorithms, really theoretical computer science. I really. First course I took was phenomenal, which is a course I'm teaching this semester, by the way. I always tell this to the students. And then I went to grad school. That was my first experience in the United States. I went to Berkeley then. I wanted to just keep learning, keep contributing to research. Then postdoc and then job market for academic jobs. That's the first time I visited Purdue when I was interviewing here. And it really struck me as a special place. It seemed that even though people were doing like super cool stuff and super impactful stuff, it was a sense of calmness and kind of lack of stress. Everyone was very nice and from the people I talked to, it felt like everyone felt supported. My experience as well is the same. So it has lived up to this expectation.

[Unnamed Interviewer]

Yeah, we were talking before this about how California and the Midwest is a bit different vibes, right?

[Alex Famas]

Yeah, yeah, yeah. The stress levels are really.

[Unnamed Interviewer]

I'm glad we've given you the warm Midwest welcome here. Okay, so let's get into AI. Someone who doesn't know AI doesn't know how to use it. What would you tell them, like AI101 for beginners.

[Alex Famas]

That's a great question, because I can answer it with other people's words.

[Unnamed Interviewer]

Okay.

[Alex Famas]

And it says, you know, this topic has shown up since ancient Greece, if you really want to be pedantic about it. But even the beginning of computer science, this conversation has come up again and again. For example, Larry Tesler, famous computer science, said, AI is what has not been done yet, which is somehow accurate. Dijkstra, another very famous computer scientist. My favorite quote of all time, which is regarding artificial intelligence, right? So you have intelligence, you have thinking. So the question of whether computers can think is the same as whether submarines can swim. So people like to have these cute one liners. But what is AI? AI is a set of tools, a big umbrella, all sorts of things faint under it.

[Kate Young]

And.

[Alex Famas]

And it's an evolving agenda. So when I was an undergrad, we talk about spam filtering or things that are now silly spell checking. You know this we fit under. AI was now because my phone does it. It doesn't seem so exciting anymore. Back then my phone didn't do it, so it was more exciting. But what's common, you know, typically when we talk about AI, we mean reasoning, learning, knowledge representation, you know, language processing. All these sort of things are typically under the AI umbrella.

[Unnamed Interviewer]

What would you tell someone, though, who might be confused about it? Is it easy to use?

[Unnamed Host]

Where would they start?

[Alex Famas]

Maybe so the reason I think things are confusing is because the umbrella is so big. The question is so broad when you say about AI. So let's do an analogy. There's this beautiful book by Narayanan Kapoor, okay, AI Snake Oil. And there they describe a world where every machine that can take you from place A to place B is called vehicle. So a bicycle and a car and a ship and an airplane and a rocket. They're all vehicles. So now let's try to have the discussion of if vehicles are environmentally friendly. It's a very hard discussion to have. It's impossible because we're putting all these things under one umbrella. If someone made a faster ship engine, all of a sudden you're like, why is my bicycle not going any faster? Right. Like, it's very, very difficult. So I think the difficulty in the conversation comes from the umbrella being so big so we can talk about, you know, prediction, AI prediction. Is AI any good at predicting the future? Is AI any good at recognizing things like, you know, showing a picture of a cat can tell you it's a cat? These are two very different things. So I would say, where to start, just to answer your question, Start at understanding that it's not one thing. There's categories. That's the first thing to understand. AI is a set of tools for all sorts of things. The AI umbrella is so large, so many things fit under it. In our application, we were trying to solve a very specific task, which is match food donations to food recipients. It's a matching problem, and we were trying to automate a person. So we have very, very concrete task at hand and have very, very concrete tools to solve it.

[Unnamed Host]

So, as Alex just mentioned, he and his team used AI to match food donations to food recipients. Now, if you remember our research series episode with Purdue food science professors Amanda Dearing and Haley Oliver, food insecurity is a massive problem across the world. And boilermaker researchers are continually working on solutions, including Alex. And according to the United States Department of Agriculture, food waste is estimated at between 30 and 40% of the food supply. So how is Purdue helping to solve this? Alex explains more about how his work with the Indie Hunger Network on an initiative known as Food Drop allows shipments of perfectly edible food to be efficiently donated. To food banks instead of being bound for a landfill.

[Alex Somas]

So the Food Drop Initiative is an initiative run by the Indie Hunger Network, a food organization in Indianapolis, and its purpose is to redirect rejected food loads of truck drivers away from landfills and into food banks.

[Alex Famas]

And the idea was very simple. You had these truck drivers, and for one reason or the other, that had little to do with whether they were carrying food that was edible, but they had a bunch of food that they had to get rid of, like whatever they were supposed to deliver it, didn't want it, for whatever reason. You know, you can think of, you're carrying 10 pallets of food and one of them shifts. The food is fine, everything's fine, but the recipient doesn't want it. So now what do you do? The outside option is take it to a landfill, throw it away. So Foodra was trying to not have this happen and match the food to food banks.

[Unnamed Host]

So how and why exactly did Alex and his team use AI to fuel food pantries? He shares why this unique process takes all of the emotion out of the equation, something humans find impossible to do, and replaces this decision making with innovative AI models to weigh all of the possible considerations and then come up with a solution that best benefits the most amount of people. See, by automating food matching decisions, more logical and fair food distribution can occur.

[Alex Somas]

So where does AI and machine learning come in? So in these problems, these are matching problems, you're trying to match a donor with a recipient. There's many things you can think of this way. So as things scale up, when you let a human make these decisions, what you'll start seeing is that 99% of the donations go to only 1% of the possible donors. And this could be all sorts of human aspects, like, oh, you know, this person responds quickly, or this person is.

[Alex Famas]

Very reliable or whatever.

[Alex Somas]

But, like, the effect is the effect. Like the things start concentrating. And that's where machine learning and AI can help. The current system is a person is making these matching decisions, and we just want to automate it. And in doing so, we want to ensure that, of course, we're doing the right decisions. Like, in a sense of, like, people are not driving a lot. The food is going to people who want the food and can use the food. But also at the same time, we are being fair. The way decision making works is we would, for many decisions that need to be made, we would like to have to do things in a democratic process, right, where we all come together and make a decision. And that is sometimes possible for Example, when we want to elect a representative. But often the way we make decisions is we elect a representative and have them make all the decisions, which is like sort of the other extreme, right? And that is because, you know, lack of expertise or because the input output is too much. It's too much to ask everyone for something every single time. The thing with AI and one of the methods that we do suggest, you can often predict what people would do. So if I could predict what you think all the time, then maybe I can simulate the way that direct democracy. So asking everyone a question all the time would work if I just have a good way to predict what you would do. And now this allows me to scale or this allows me to make more fair decisions for a number of things.

[Alex Famas]

So food donation.

[Alex Somas]

That's precisely the approach we took.

[Unnamed Host]

Alex dives further into food insecurity in the US and reiterates the importance of using AI for good when you start.

[Alex Somas]

Working in these food allocation problems. I didn't realize it at least, but food insecurity is embarrassingly bad globally. But also in the US you would think, okay, there's so much food, you know, like, everyone's throwing away their leftovers and, you know, trying to lose weight or whatever. Food insecurity, which is defined as, I'm going to bed hungry. 100% of US counties have food insecurity. Something like 40 million adults, 10 million children face food insecurity in this country in 2022. The numbers are shocking. Like, in our minds, or at least in my mind, I knew it was a problem, but I didn't realize before starting working in the space back in 2017 that it's a huge problem. It's unbelievable how big this problem is.

[Unnamed Host]

Alex says this machine learning program also saves people time and energy. Not only that, but this model can be scaled beyond Indiana to include more.

[Alex Somas]

Food banks beyond people's time. So, again, like, this matching process run by a single person, that's not their only job. So now we can free some of their time into something that, like, in a sense, a machine can do better.

[Alex Famas]

Or an algorithm can do better.

[Alex Somas]

The other thing is now you can scale. Now you can just, like, go beyond Indiana. You can even, you know, include more food banks. You can process donations a lot faster because a human doesn't have to sit around trying to call people on the phone and so on.

[Unnamed Host]

And speaking of scaling, Alex and his team are now working with The Society of St Andrew, a United Methodist hunger relief nonprofit that focuses on food waste and hunger.

[Alex Famas]

So the idea was Once it's automated, it would get passed on to another organization, which we have now. So this is now being supported and it lives under a different organization called Society of St. Andrews. But they didn't have to train anybody. They could just immediately take this on because there's nothing to do, there's nothing they need to learn. You know, you don't need an employee who knows how these decisions are made or you know, how to navigate the process. And in fact, a few months ago, an organization from Washington, the state of Washington, so near, I think they're south of Seattle, want to take this on. They want to run a similar program over there. They don't need to do anything. They could just. Our code is open source. They can just take it, change the locations of the recipients and run it. It can run tomorrow if they decide to run it.

[Unnamed Host]

As Alex just mentioned, this model uses open source code code that's open to the public. It's also funded by the National Science foundation and a Google AI for Social Good award. He discusses his goals for continuing to expand.

[Alex Famas]

We are interested in expanding, so we have seen things getting allocated. We want to do more. I think there's more to do just because we're not limited by human time. So we can expand in a sense for free. So we have some funding underway. There are some changes we want to make in the back end, some technical changes. The goal is to try to grow our set of recipients. So the people on the list, so contact more food banks. So we're working with the Society of St. Andrews for this. We have applied for some funding for this project and we'll see how it goes.

[Unnamed Interviewer]

We talked about there's no human decision making, it's all AI. But ethics in the tech industry has been at the top of the headlines lately. You're nodding. So why are you confident that your solution with this food drop has this fairness and can maintain that fairness?

[Alex Famas]

Well, the short question, because I can prove it. Okay, you see, what's the process here? You see a real problem, you see a real world problem. Now reality is complicated and math cannot deal with that. So you abstract away details and you get to some nice model, a theoretical model where, you know, the actors behave in a simple way, simple enough for you to kind of be able to argue for. And in that model you define what fair means and then you prove some stuff. And hopefully whatever you prove matches something back to the world. So the only disagreement can come, you know, someone can complain is whether they disagree with what I mean when I say fair. They Say, no, no, what you say is fair is actually not fair. And the other is, when I did this abstraction, whether I remove something that shouldn't have, whether I took away something from the real world, that's very important, and I removed it because I didn't think it was important, and I believe that we haven't done this. I can tell you what the fairness notion is if. If I think that's a simple one, so there's no technical things about it. But if there's any arguments, it will be one of those two steps. And because everything is open, you can go read my paper. I'm open to.

[Unnamed Host]

You're open to feedback.

[Alex Famas]

Yeah, of course. That's. That's how science proceeds.

[Unnamed Host]

Will link Alex's research in our podcast show Notes for you. All. Alex's graduate and undergraduate students are involved in this research as well. So what's his approach to mentoring them? Especially when it comes to topics like AI and computer science that are changing day to day on a global scale? Here's a hint. What he wants for these Boilermaker students is actually pretty simple. Find something that excites them.

[Alex Famas]

So what's my teaching style? So I'm a little chaotic. I don't know if this coming across in this conversation, it's not for no reason. Okay. So I really strongly believe that the bottleneck in research, in academic research, many, many times more times than people would think, is not, you know, intelligence or talent. That's what most people think. That's why science is not progressing, because there's not enough talent.

[Alex Somas]

I think that's not true.

[Alex Famas]

I think the real reason is there's not desire. So people like me are excited about problems. And I have found it A very unsuccessful way to mentor people is to tell them to work on the problems that I'm excited about. So instead I do something that is very difficult for them. So they're all very annoyed, which is tell them everything under the sun that I'm excited about, not one thing. So, you know, 50 things and then let them pick.

[Unnamed Interviewer]

So tell us some of those things that you're excited about.

[Alex Famas]

A lot of them are very technical, like very specific technical questions that I don't know how to answer. But others are the opposite. So I'm very excited about AI applications in democracy. What does that mean? What can we do? How do we can make better decisions collectively? Like how do you form committees, citizen assemblies? These sort of things are very exciting for me. Or I've been recently learning a lot about through actually colleagues here at Purdue applications of AI and machine learning in medicine, how some things are done a little naively. So I'm excited to learn more about. Okay, there seems to be some room to put things together more. So, you know, these would be very, you know, open ended examples. And my hope, and this hope has been, you know, materialized, is that someone will get excited about something that I'm. This is in my peripheral vision of excitement and then I get to learn from them. So that's the best way to keep track of things, is have someone who's really excited tell you about it. Right. So hopefully to infect someone and then learn from them.

[Unnamed Host]

I love what Alex just said right there. He knows he can't mentor students by telling them to work on problems that interest and excite him. They need to work on problems that inspire and excite them. That's the difference maker. And speaking of students, in 2022, Alex received a career award from the National Science foundation, which is the most prestigious award given to junior faculty who embody the role of teacher scholars through research. Alex shares what this honor means to him.

[Alex Famas]

First, it was really nice to be able to get the funds to fund students and have them, you know, kind of explore their interests together with mine. Certainly the award helped with that. But more than that, it felt really nice because this was under AF Algorithmic foundations. So which is a very broad kind of umbrella in theoretical computer science. You know, the comparison is a little bit apples to oranges how you compare to people who to give the award to. So it really felt, I would say, very, very nice that people appreciate the work that I do. Like people in my community, in the theoretical computer science community appreciate this work. I feel like that was the most meaningful part of this.

[Unnamed Interviewer]

You talked about your passion for computer science. Purdue had the first computer science program in the US how are researchers like you at Purdue using that experience to be part of this rapid evolution of AI?

[Alex Famas]

Purdue has had the first CS department and that really put us in a good spot today. And you can see that computer science has a really has a big place in Purdue as a whole. Purdue is at the forefront of all sorts of things. There's people like me who focus on more foundational aspects, understanding things that their core. We have very, very strong people doing computer vision, things that have to do with images, video, that sort of thing. Robotics, we have a very, very strong robotics team. We might have Anakin Vera.

[Unnamed Host]

That's exactly.

[Unnamed Interviewer]

I'm thinking of the AI dog.

[Alex Famas]

Security is very strong. So people trying to figure out is AI secure? Is AI trustworthy. So we're, you know, Purdue as a whole is attacking the human computer interaction, all the talent we're recruiting, and all the kind of combined efforts that go into the broader agenda. But I think Purdue as a whole, computer science is really at the forefront of this.

[Unnamed Host]

So not only is Purdue a leader in computer science, but our university is also a leader when it comes to AI. And one of Purdue University President Meng Chang's strategic initiatives includes Purdue Computes, which launched as student interest in computing related majors and the societal impact of artificial intelligence in chips computer continued to rise rapidly. Purdue Computes emphasizes these four key pillars. Computing, semiconductor research and development, physical AI and quantum science and engineering. Alex shares more Purdue Computes is this.

[Alex Famas]

Massive initiative at Purdue that's trying to attack a huge number of problems all at the same time and recruiting the manpower to do so. The whole initiative is facing challenges and providing solutions for things across the board. So AI and applications to agriculture, manufacturing, detecting bias, chip design, robotics, everything across the board. I think there's so many exciting things are happening in that space.

[Unnamed Interviewer]

What's your favorite part about being part of the Purdue Computes initiative?

[Alex Famas]

I think getting to collaborate with people, just having the expertise that you need available. So for example, I was recently in talks with Foot Finders, the company, the NGO here in Lafayette. Again, some AI application in their operations and there was some problem with data collection. It seemed that there was a very expensive solution and the cheaper solution to do the data collection would take a picture. Everyone in the market takes a picture of something.

[Alex Somas]

And I was like, oh, but I.

[Alex Famas]

Know nothing about computer vision. So I don't know. Actually what I'm saying is reasonable. Well, two days later, now I know it's reasonable because I was talking to my colleague Raymond, who is one of the best people in the world in computer vision, and it was right there. I could just ask him. It really makes everything go a lot faster having all these cool people doing cool stuff.

[Unnamed Interviewer]

We've had a lot of researchers on the podcast who have said it's so collaborative. It's this special community within Purdue. So that's cool to hear.

[Alex Famas]

That's how science moves, you know, just by having the expertise. No one can be an expert on everything. It's just too many things going. So just knowing that you have reliable colleagues that are experts in their own domain is really, really important.

[Unnamed Interviewer]

Going back to, you know, you talked about community growing up in Greece. What has it been like acclimating and being involved in this boilermaker community overall for you and your family?

[Alex Famas]

It's been fun. It's been fun. It's just very different. It's very hard to compare. Greece is a very uniform place. Everyone is pretty much looks like me, you know, and acts like me. Exactly. Roughly exaggerating. You know, on the flip side, Purdue is an international community, right? Like, driven from the university. So just so many people come from all sorts of different backgrounds. It's been really cool to be part of that.

[Unnamed Interviewer]

What discoveries or findings can we expect from Purdue, specifically within computer science and AI in 2025?

[Alex Famas]

That's a hard question to answer. So we have some of the best people in the world working on things across the board. So foundational things, theoretical things, vision, robotics, human computer interaction and their applications across the board to agriculture, medicine.

[Unnamed Interviewer]

So last question here you get to see and contribute to so much amazing research at Purdue. Like we talked about earlier, what do you want the world to know about the work that's happening here at Purdue?

[Alex Famas]

We're a leader in so many things. People here are pushing the agenda in all sorts of things. Especially, you know, the College of Engineering and the College of Agriculture are a big part of being world readers. But even at the College of Science, we're really a part of all the exciting things that are happening in the AI space.

[Unnamed Interviewer]

Well, we can't thank you enough for joining us and celebrating our new studio with us. Is there anything else I missed? Anything you want to tell our listeners regarding AI?

[Alex Famas]

I think the most important thing is to really go beyond the buzzwords. I would say that is a concrete message that I think is important to put out there. I think a lot of people are somehow suspicious that AI around them is bogus or exaggerated or just straight up false. At the same time, it seems very useful in other things. And, you know, I think people are having a hard time articulating exactly what they mean and it's difficult to articulate exactly what you mean. And I think that the confusion and the way the confusion gets clarified is by knowing the stuff. So be less scared and more open to learning how these things work because, you know, it's not going anywhere. Like our lives will become more and more algorithmic, so to speak.

[Unnamed Interviewer]

I love that. That's great. Be open minded. Don't be scared to utilize AI in your general life.

[Alex Famas]

It's easier for me to say because it's my job and also it's my job to be wrong. That's most of research, you know, only mistakes. 1% of the time you do something good, 99% of the time you're wrong, that's fine. So I'm very used to being wrong, but that's really the worst thing that can happen. If you could just, you know, be wrong, say something embarrassing, and it's fine, you know, as long as you learn something in the end.

[Unnamed Interviewer]

I love that. No, that's perfect advice. Well, again, we can't thank you enough.

[Alex Famas]

Thank you for having me. It was great.

[Unnamed Host]

It was wonderful to have this special boilermaker in our new podcast studio. We have additional bonus content from Alex's interview on our podcast YouTube channel. Head over to YouTube.com/thisisprdue and click that subscribe button button while you're there. Plus you can check out some videos of our new Purdue Brand studio as well. And of course, be sure to follow this Is Purdue on Apple Podcasts, Spotify, iHeartRadio, or wherever you get your podcasts. This Is Purdue is hosted and written by me, Kate Young. Our podcast videography for this episode was led by Ted Schellenberger in collaboration with John Garcia, Thad Boone, and Zach Mokensen. Our social media market is led by Maria Welch. Our podcast distribution strategy is led by Carly Eastman. Our podcast design is led by Cheryl Glotzba. Our podcast photography is led by John Underwood. Our podcast team Project manager is Rain Goo. Additional writing and research assistance is led by Sophie Ritz and Ashvini Malshi. Our video production assistant is Delaney Young. Our this Is Purdue intern is Caroline Kyne and Purdue Brand Studio Team member Brittany Steph is is the author of a Purdue News article featuring Alex's research, which you can check out in our show notes.

[Kate Young]

Thanks for listening to this Is Purdue. For more information on this episode, visit our website at purdue.edu podcast. There you can head over to your favorite podcast app to subscribe and leave us a review. And as always, boiler up.