How is AI changing the way we solve problems? Dr. Aniruddha Bora

What does it really mean to solve a problem in the age of AI?

In this episode of Stories of Change and Creativity, I sat down with Dr. Aniruddha Bora, assistant professor of computer science at Texas State University, to explore how artificial intelligence is transforming the way we think about problem solving.  I met Dr. Bora when I moderated a panel at South by Southwest (SXSW) at Texas House as part of the Texas State University Global Innovation Roundup.

From his early interest in science in India to his work in applied mathematics, Dr. Bora shares how interdisciplinary thinking can lead to meaningful innovation. I found Bora to be smart, kind and a joy to be around.  His energy is contagious.    

Key Takeaways

• Problem solving starts with the right questions—not just the right tools
• AI is most powerful when combined with mathematics and real-world data
• Innovation often happens at the intersection of disciplines
• Understanding what you don’t know is essential to learning
• The future of education is about guiding curiosity, not just delivering answers

Why This Episode Matters

As AI becomes more integrated into everyday life, the ability to think critically and solve meaningful problems is more important than ever. This episode offers a fresh perspective on how curiosity and creativity can drive real-world impact.

Learn more about Dr. Aniruddha Bora, assistant professor of computer science at Texas State University, and his work in artificial intelligence and real-world applications here.

This podcast episode was recorded at the Live Oak Podcast Studio at Texas State University.  Photo by Texas House. 

🎧 Enjoyed this episode? Please share it with a colleague, student, or friend who’s interested in AI and creative problem solving.

Please follow along for real stories, take away a little wisdom and maybe find something that helps you figure out what's next.  

Transcript

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Better Questions For Better AI

Judy Oskam 0:02

What if the future of artificial intelligence isn't just about better data, but about asking better questions and solving the right problems? I'm Judy Oskam, a professor of mass communication at Texas State University, and this is Stories of Change and Creativity. I met Bora at South by Southwest, where I moderated a panel at Texas House. The Texas State University Global Innovation Roundup brought together leaders to highlight how research, entrepreneurship, and industry are shaping what's next. So no matter what you do or where you are in life, this episode is all about seeing problems differently and building the tools to solve them. I hope you enjoy our conversation.

Dr. Aniruddha Bora 1:00

So uh I am currently an assistant professor at Department of Computer Science in Texas State University. And I joined here in uh fall, like last year fall, so in August uh 2025. Uh prior to this, uh I was doing my postdoctorate at Brown University. So I was there for almost three years, eight months uh from uh January 2021 to uh August uh 2025. And prior to that, I did my PhD in Louisiana Tech University from 2016 to 2021. Right. Yeah.

Judy Oskam 1:34

Well, and you and I had the pleasure of meeting at South by Southwest, and we were on the panel, and it was so fun to hear your story. And let's kind of start a little bit further back. And how did you end up at Louisiana Tech in the first place? Let's kind of start

From Pure Math To Applied Work

Judy Oskam 1:51

there.

Dr. Aniruddha Bora 1:51

All right. Yeah, it's it's pretty interesting because um okay, so I'll I'll start from a little bit even earlier. Yeah, sure. Sure. Uh I always was kind of uh enthusiastic with science and stuff. So uh, you know, like uh working with science projects and and kind of you know trying to figure out why things work the way they do. And naturally physics comes the first uh subject in mind that interests you in in that aspect. And uh I applied for uh physics degree, uh like for for the admission in in undergrad in physics. Uh but in back in India the cutoffs are pretty high for for uh like general science. And my first choice was physics, second choice was maths. Uh physics did not uh make it through, and I I w I was in the list for the maths, I selected that, and that's how I uh started my degree in maths, and that was in pure mathematics. So it was all theory, it was very beautiful. Like, you know, you have the structures that are like, you know, if you can think abstractly, it's kind of like poem, but in terms of science. That's what I think. For you it is.

Judy Oskam 2:52

Yes, it is. For me, not so much.

Dr. Aniruddha Bora 2:56

Yeah, that that's true. Like I and and I would blame the people who teach maths a little bit on that aspect because the way they teach maths and what it actually means, uh, you know, people who don't cannot connect that, you know, student essentially starts fearing maths and you know stay away. So majority of the students everywhere around the world is like, you know, oh maths, it's I don't like it. But that's one of the most fun subjects that you can, you know.

Judy Oskam 3:19

Well, and that's what I heard. And I had a I had an experience in second grade. Uh-huh. And I remember when the new math came and I had a teacher that didn't didn't teach. And so that was where my my beginning of my end. But I love the fact that it truly is it is the language of the world.

Dr. Aniruddha Bora 3:40

Yep. Right? That's the language of the world, which is like constant and unique. Like it's not it's not going to change if you like, you know? It's it's it's carries the same meaning all over the world and hopefully uh out there somewhere else in the universe.

Judy Oskam 3:54

Aaron Powell Yes, yes, and beyond. Yes, yes. Well, and and so your path didn't start where you finally.

Dr. Aniruddha Bora 4:02

Yeah. So uh to to add to that, you know, like I I like the theoretical maths and stuff, but uh slowly with time I started saying like, okay, um this is good, but uh how can I use it, right? So then I started realizing that theory-wise, maths is way ahead compared to what uh practically we are using, right? So there is this big gap. So then I said, like, okay, now I I understand what math means and and and what it means kind of from fundamentally and structure-wise, and and what is it in a you know theoretical uh context where everything is you know well-defined and you have beautiful structures, but in real life, you know, as is in every other field, it's messed up, right? So then I started going into the direction of applied mathematics, so where you use kind of like uh the theories uh and and build on numerical methods that can solve real-world problems. So then I did my master's in applied mathematics back in India. And uh during that time, uh I was working with uh one of the professors, uh Dr. Ranjan Kumar Mohanty in South Asian University. So he was a very brilliant mind, and uh I was very interested in in the method that he was developing, and I learned, I did my thesis on that, and then he said that if you want to learn more, you need to contact this person who was a professor in Louisiana Tech University. So that's how I I I had no plan of like, you know, where should I apply or something. I just applied one college. It was uh like not applied to college, actually. I just wrote an email to the professor that uh I am working with uh this professor and he mentioned your name and I'm working on this field and and uh I would like to continue uh learning this field, and would you uh be like willing to accept me as a PhD student? So that's how I ended up in the Trevor Burrus.

Judy Oskam 5:45

I love that. I love it. It's all about the people, right? Yes and the connections.

Dr. Aniruddha Bora 5:48

Yes, exactly.

Judy Oskam 5:49

I love that. Yeah. So so you got on a plane and you left sight unseen. Yeah. You'd never been to Louisiana before.

Dr. Aniruddha Bora 5:56

No. Never been outside of India.

Judy Oskam 5:58

Tell me about that. Oh my gosh. Oh, I love that.

Dr. Aniruddha Bora 6:01

There's a there's a funny story to that, actually. So um you know, like uh I I used to take uh a lot of like this uh movies which I used to watch, you know, where uh they showed this PhD students, right? And mostly these movies used to picture uh in Cambridge and stuff, you know, where people are very kind of formal and stuff. Yes. And you won't believe I I I wore a suit from India to US when I when I came in the flight because you know I was all in that mindset.

Judy Oskam 6:28

Yes.

Dr. Aniruddha Bora 6:30

It was like when when I came here, uh the people who used to pick uh came to pick me up from the sto uh from the college, they thought I'm uh joining as a professor because I was of course, because you look like one.

Judy Oskam 6:40

Yes. Yes. Oh my gosh.

Dr. Aniruddha Bora 6:42

So yeah, I I ended uh and and there is a funny story on that aspect also. So the thing is my sim did not work here, right? So uh so Louisiana Tech uh is is in a college town. So the nearest airport is kind of like a domestic airport, shuts down at 10. My flight reached at 9.50 uh p.m. at night. Oh yeah. So I got down, took my bag. Uh I had no internet there now because this airport shut down at 10 and they asked me to stay outside. Because it was a very, very small local airport.

Judy Oskam 7:10

Yes.

Dr. Aniruddha Bora 7:10

I was outside, I have no connection, and and it was pretty dark, and I was thinking, I I hope they will come. They came late, like around 45 minutes late. I was waiting, but when they saw that I was Oh my gosh. Did you fly into Shreveport or where did you fly? Oh Monroe, Louisiana. Oh my gosh. Yes. Yes. And then you drove to the Trevor Burrus, yeah. Then some students from there came, like I contacted the ISO beforehand and they came to pick up and said, yeah, they were stuck in some work and and all good.

Judy Oskam 7:40

Oh yeah. Well, I think and that was the beginning then of your next phase of your of your education then.

Dr. Aniruddha Bora 7:47

Yeah. Yeah. That was kind of like yeah, when I came in, and you know, the first thing I noticed was it was too silent.

Judy Oskam 7:53

Yeah.

Dr. Aniruddha Bora 7:54

Like in India, you you are used to, you know, hearing people lot around you and and because you were in the rural area. And then honking and stuff. Yeah. So that was new.

Judy Oskam 8:04

Yeah.

Dr. Aniruddha Bora 8:05

Yeah. And and then I met my advisor and and started working on the stuff, and uh it was interesting. It w it was very different from uh how um teaching is approached back in in India. So I saw that's that's a good thing. I saw like, you know, it's kind of more open-ended. Rather than the advisor telling you do this, do that, you are more open to, you know, like uh discuss your ideas and and and you know focus on something that I can decide. Yeah. So that's how I started with uh uh the applied uh numerical methods, which was the core part of my dissertation in PhD. But uh then these numerical methods also has some limitations.

Bridging Equations And Data With AI

Dr. Aniruddha Bora 8:45

Right.

Judy Oskam 8:45

So there's limitations to everything, right? Yeah.

Dr. Aniruddha Bora 8:48

Uh the problem is uh you know, most of the things that we model in the real world are not complete. Although it works, but it's not fundamentally complete in that sense that you can find like an exact solution, right? Okay, this is the equation that's determining, let's say, the weather events, right? If it was well defined, you could predict the weather exactly, right? So it will say that it will rain hundred percent. Not not there is no kind of probability aspect to it, right? So there is some parts which is still missing, right? That's that comes from the basic science, right? You are uh researching, developing, and then you figure out some more things that you add to whatever is there. So that's where I felt that numerical methods suffer because now when you are coming to like more kind of real world problems, so there is kind of like uh uh gap in what the model predicts and and what it is actually, right, in the real world. So that's kind of like a bias.

Judy Oskam 9:43

Yeah.

Dr. Aniruddha Bora 9:43

So but in the in out there, there's a lot of data right uh on on these real world problems, right?

Judy Oskam 9:50

So so the real world problem like like weather. Let's say weather.

Dr. Aniruddha Bora 9:55

So you have this all these measurements of weather stations, right?

Judy Oskam 9:58

Right.

Dr. Aniruddha Bora 9:59

Now uh the idea was okay, now there are patterns in this, hidden patterns in it, right? Now you have these equations and you have this hidden pattern. They are trying to describe the same thing, right? So there has to be some kind of bridge between these two that connects together to a more complete picture, right? And that's how I came into like machine learning and AI and stuff, because those are pretty good at uh finding patterns, right? So that's how in my last chapter of my dissertation I started working with physics information learning.

Judy Oskam 10:27

Ah okay. And that's the physics coming back.

Dr. Aniruddha Bora 10:29

Yes. Okay. So now you are using the physics of the system. Plus you have some data and and you combine them together to get the more complete picture, right? So now you are doing better than what data alone can do. You are doing better than what equations alone can do. So it's it's kind of like a hybrid. And that's how uh I switched to kind of like more AI stuff towards the end of the thing. And then uh there is a very uh uh important uh aspect of of of a paper regarding this is on physics and form neural network that came in during around the same time. And uh believe me or not, I was working on the exact similar idea.

Judy Oskam 11:06

Yes.

Dr. Aniruddha Bora 11:07

At the same time, you know? Yes. And and uh, you know, like they they published the paper. I saw, oh my god, this is the exact same thing I'm working on, right? So uh but I was solving a different problem and they were working more on the kind of broader methodology. So I was probab I was solving a nonlinear uh heat equation in nanoscale, which is highly nonlinear. So I wrote this paper, uh this method, and then I sent an email uh to the professor who who was kind of like in that paper, the corresponding author, that what do you think of this paper? And he was very impressed seeing the results because at that time uh it was just uh in invented that method.

Judy Oskam 11:41

Yeah. Yeah.

Dr. Aniruddha Bora 11:42

And they saw like a very real world problem that I was solving, and he said, uh why don't you present it in my group? And he was the professor in Brown University.

Judy Oskam 11:50

Oh, and that's how you became a good idea.

Dr. Aniruddha Bora 11:51

Yes, and then then I I went there and and uh I I sorry, I I presented online and then he said uh I'd be happy to offer you a postdoc after your PhD. So would you be happy? I said definitely I'd be very interested. Yeah. Yes. And that's how I ended up in Brown doing my postdoc in the like intersection of applied maths and and uh scientific

Let The Problem Choose The Tools

Dr. Aniruddha Bora 12:10

machine learning.

Judy Oskam 12:10

Okay. Yeah. So is is the is the challenge of the method driving you or is the problem to solve driving you?

Dr. Aniruddha Bora 12:18

Problem to solve. So I'm always kind of like uh Q because Okay, in academia, I you would see that people are very uh protective of their methods. Which is good and bad. Uh I see like you know, methods can change, so you should be more that's my opinion. I'm not saying that Trevor Burrus, So be more free with the method. Yes, it should be more towards problem solving, right? Whatever works in there, right? So you can create a new method that works for that but may not work for another problem, right? So you should not be trying to use the same method to you know force, to fix, to solve the like the different problem, right? You should be open. So that's one reason I was always open in in learning things, is because you know you never know what problem you will encounter. So that's why I I learned the theory, I learned the numerical methods, and then the pattern recognition or the machine learning aspects of of of data science. So that now I have a problem.

Judy Oskam 13:08

You have a tool.

Dr. Aniruddha Bora 13:09

Yeah. I have different tools and I can get a complete picture to address the solution like that. Okay. Yeah. So that's that's how I see myself.

Judy Oskam 13:16

Okay. Okay. And then what problem are you most interested in solving?

Dr. Aniruddha Bora 13:21

Huh. Most interested. Okay.

Judy Oskam 13:23

So right now you might change, right?

Dr. Aniruddha Bora 13:25

Okay. So most interested in solving is kind of uh uh designing true AI algorithms. Although AI is kind of like a big thing right now and in every aspect you will see, right? Yeah. AI is being used. But uh there is a difference between people who develop AI and people who use AI, right? So people who develop AI, we know like what exists are.

Judy Oskam 13:47

I'm a user. I'm not a developer. Okay.

Dr. Aniruddha Bora 13:49

Yeah. So mostly people are users, right? Even in different science domains, let's say engineering or or other science domain, they are using these AI tools that is being developed, right? So even though we call it artificial intelligence, it's more, I would say, kind of pattern recognition. Right? You feed in the data, you try to mix the you you try to find patterns and use that patterns for your predictions, right? It's still not autonomously, you know, having intelligence. So if you ask me, what's the like, you know, what's the problem, yes. So that's where I would like to like, you know, think and and try to discover something that is kind of truly intelligent, right? Whether it's good or bad, I don't know. But in terms of science, that's kind of very exciting. I love that. Yeah. Okay.

Judy Oskam 14:32

Okay. Well, and here at Texas State, then you just started at Texas State in the fall.

Dr. Aniruddha Bora 14:37

Yeah.

Judy Oskam 14:37

So how are you working with students and other faculty to help make that happen?

Dr. Aniruddha Bora 14:42

Oh, okay. So uh I work mostly since I I am interested in solving real-world problems, I work with interdisciplinary people, right? So I reach out to people uh and and working in different fields and I say that, okay, I I am working in this kind of problem. I have this experience on developing methods for solving problems, and uh what are the interesting problems that you have to solve, right? Because uh, if you think of uh these AI methods or numerical methods, algorithmically they are similar. Application can change. But the core way of uh taking all those information and and you know predicting something and and making decisions based on this is similar. The core principle is similar.

Judy Oskam 15:21

Sure.

Dr. Aniruddha Bora 15:21

So that's how I I start uh working with like you know different faculties and and uh I work with a different lot of different domain experts. It takes some time to be on the same page because the same word may mean very different things between uh a domain expert and the one who is like say developing the computational stuff. So uh that's that's the way I uh collaborate with faculties and

Teaching Curiosity In The LLM Age

Dr. Aniruddha Bora 15:44

with students. So um right now you know that uh the atmosphere of teaching or the atmosphere of education is changing.

Judy Oskam 15:51

Yes, a lot. A lot.

Dr. Aniruddha Bora 15:53

So as a uh person in academia, what I see is nowadays uh the role of you know professors or or teachers is to you know point and guide the student. You have unlimited resources that you can get today just sitting at home, right? Right. But how to direct that to a constructive thing or direct that to uh uh way, direct it in a way that helps that student develop, right? Because you have all this uh let's say you have this kind of big LLM models. You can get most of the answers, not not all, but most of the answers you can get them. But what is the right question to ask?

Judy Oskam 16:31

That's right. That's right.

Dr. Aniruddha Bora 16:32

You you you can give you can give, let's say you you uh there is a student in your class, you give them a set of you know answers. Like here is the answers, all the questions. Now figure out what you want to do in life. You'll that student will be clueless because you have so many things, they don't see what can be important, what cannot be important, right? So I feel that's kind of the role that is more predominantly should be played by a professor or or or or or an educator because you know that uh these tools are so powerful, right? So you can get all the information, all the knowledge you want, but what exactly you want and how do you pipeline it to make something meaningful for your career, right? So I I see that's kind of like the thing. And that's how I discuss with students, and when they come with interest that, okay, I want to work with this this kind of stuff, right? And then I give them, okay, there are this two, three papers. Just have a read through and see what doesn't interest you. And if they come back to me, and then I'll give them a small problem, like which I already know how what the solution is. And I want to see how they handle the problem. Not the solution. I don't care if they give the wrong solution, but I want to see what are their attempts or or how they think to solve that problem. And then I can have like a rough idea on what that student's thinking look like, right? And then I try to build on that and connect it to a problem that I'm solving where where he comes in or she comes in as kind of like a complementary part and develops their own part. And that's how I see students uh kind of, you know.

Judy Oskam 18:04

A I think that's fascinating. And I think too, if you think about uh uh how people think and so you're actually and you know, I I thought about this as you were talking. Sometimes students don't know what they don't know.

Dr. Aniruddha Bora 18:18

Exactly. They don't know what they don't know. So I think your idea of giving them an article or two to read and think about. So how do you get them to really open up their thinking so that you can have a first a front row seat to that? Trevor Burrus, Jr. Oh, okay. So I think that refers uh to the person I'm talking to. So usually what I do, okay, when they say they are done, right? I'll say, okay, can you present like what you understood? And then I'll start questioning them. And you will see kind of like a uh there is a level after which the student becomes uncomfortable. That happens, I think, commonly with every student. I said, yeah, so that's part that's the part that you know I want you to break. Tell me clearly what you know, what you don't know, right? Not knowing is also kind of a knowledge I see. So I always tell them that's right, that's true. People talk about failures, but failures is an important part of learning, right? What you don't know is equally important as what you know.

Judy Oskam 19:16

Yes.

Dr. Aniruddha Bora 19:16

So I said like it's not about I'm not trying to test you. Like there is no point of, you know, these answers are already there. The point is I want to know where what is your understanding. I want to see that picture. So the first thing is you should tell me what you know, what you don't know. Okay, there's no no basis of judging. Yeah, yeah. Because what you don't know now, even I didn't know at that time. Right. Now you see after put putting so much efforts of hard work. So that is a different picture. Even today, there are people whom who knows much more, right? So it's it's the same process. But unless you are open to it, unless you are vulnerable, you'll never learn.

Judy Oskam 19:53

Right.

Dr. Aniruddha Bora 19:54

So I try to put that perspective and with time, you know, I see that they get that, and you know, I say, like, whatever you don't understand, that's okay. It's there is nothing like something uh silly question or a small question, right? Everything is a question, and you should keep that you know in mind. And that's how you see like when people are like kids and babies, they try to like you know, let's say touch and and touch a hot stuff they learn, right? So that inquisitiveness should be there when people are old also. But I see that kind of because of I I would say uh uh competitiveness or social pressure or whatever, that goes away.

Judy Oskam 20:29

Or the systems that they're working in. Yeah.

Dr. Aniruddha Bora 20:32

So that goes away and that kills kind of like the whole innovative.

Judy Oskam 20:35

I totally agree with you. Yeah, I totally agree.

Dr. Aniruddha Bora 20:37

Because every s I think every student has very good potential. I don't see anyone is more intelligent or less intelligent. It's only the the only difference is there because of how they have handled or how they have learned uh in till that point of time. I feel if you nurture the similar kind of condition to students, I think they will equally do good.

Judy Oskam 21:00

Yeah. Yeah.

Dr. Aniruddha Bora 21:00

That's how I see it.

Judy Oskam 21:01

I may be wrong, but you're but you're having to build a lot of trust with that student who might never have had a teacher like you before.

Dr. Aniruddha Bora 21:09

Yeah.

Judy Oskam 21:09

And who might think, well, I I want to make sure I get the right answer. I want to make sure I give you the right answer. And it's building that trust. Yes. Yeah, building that. That takes time. Yeah, yeah, yeah. So did you always were you always uh interested and curious about solving things?

Dr. Aniruddha Bora 21:27

Yeah.

Judy Oskam 21:28

Even as a kid.

Dr. Aniruddha Bora 21:29

Yes.

Judy Oskam 21:29

Yeah.

Dr. Aniruddha Bora 21:29

Yeah. So I used to take part in all the science projects and stuff. Yeah. Just for the fun of uh kind of you know, trying to make something which is works after you know understanding the basic stuff.

Judy Oskam 21:39

Yeah.

Dr. Aniruddha Bora 21:40

That was always kind of part of my uh interest. Yeah.

Judy Oskam 21:43

Yeah. And so now here at Texas State, you're mentoring uh graduate students as well as undergrads. Yes. Okay. Yes. Great.

Building A Laser Tool For Skin Cancer

Dr. Aniruddha Bora 21:51

So there is an entrepreneurship program where I am working with two undergrad students uh for creating kind of like a self-autonomous laser for uh skin cancer. treatment. So we are now trying to develop the prototype. We have all the simulations and all the codes. Now we are 3D printing stuff, you know, like a phantom skin and trying to make the laser to you know deploy that algorithm there and test like benchmark the kind of lab results and see where that goes.

Judy Oskam 22:16

And so that that will maybe take over from where like a Mohs surgery or something? Or is that what you're thinking? I'm very familiar with all that unfortunately. Yeah. So I could be one of your patients in the future. But so so that's a real world application there. Oh my gosh. Yeah.

Dr. Aniruddha Bora 22:34

So that's something like we are developing and and kind of uh it's it's very interesting. You know like it it has a lot of challenges but uh if it comes to play, I think that has a lot of potential in in medical kind of yes.

Judy Oskam 22:49

Yeah. Wow. And so so would that would that replace the um the freezing or would you would you laser that the skin off or what's the what's the idea there?

Dr. Aniruddha Bora 23:00

I'm curious about this personally it's kind of like we take a temperature measurement in the surface of the skin and it can be a sensor or it can be an infrared camera camera, right? And we have the physics equation that governs like how the heat uh diffuses in the in the human body, right? Plus we have the data from the sensor. So again the two aspects of the same problem right and combine them together with something called reinforcement learning which is kind of like it learns from mistakes. So if you have heard of uh AlphaGo or the uh computer program that defeated the chess uh world oh the chess oh yes yes yes so it learns from mistake you know and it keeps learning and it reiterating yes yes so we have a lot of uh like now that is only with data where it makes mistakes learn learn now we have a physics system that governs it perfectly right now the data so it's learning from modeling from the physics and learning towards the more kind of corrected part from the data so it's not making any vital mistakes in the beginning because it already is governing is it's it's uh kind of following the physics law but now your s your skin structure and your tissue will be different from mine right but the physics cannot exactly govern that difference that comes from the measurement part. So now you have a measurement that grounds okay technically let's say at at at this depth the temperature should be 10 degrees but because the person's skin and morphology is different that is 12 degree.

Judy Oskam 24:27

Right.

Dr. Aniruddha Bora 24:27

Right? So that gives you more precision control and uh you know kind of uh you don't have to come for treatments often sure it'll be very precise and the excessive damage to other cells will be kind of limited. Yeah because it would be targeted yes very targeted wow so that's that's kind of the target yes well that's exciting.

Judy Oskam 24:45

So so coming from the lab into industry is really what you're what you're working on.

Dr. Aniruddha Bora 24:51

So I I want to work in both the domain you know like develop the theory as well as kind of the product that I can see in my lifetime being used and you know help the society. So that's that's yeah yeah oh wow. So I I'm working on other different entrepreneurship ideas. So I'll still be in academia. Sure. So one of the students who is working and in developing this I'll make them kind of run the company and you know there you go. That's that's the idea yeah that's the plan.

Judy Oskam 25:16

I love it. Well and that's what I love about academic careers is there is such an application element that if you have the right faculty and the right people and the right resources and here at Texas State we have Star Park we have the entrepreneur centers we have all kinds of opportunities there.

Dr. Aniruddha Bora 25:33

Aaron Ross Powell Yeah that's a like a the the um Star Park is is really good like and and all the office of you know commercialization they have helped me a lot to understand that aspect of of the research because till now I never been in that field right or or in in that kind of developing a product. So they are helping me in those a lot. Otherwise it it would have been it would just take you longer.

Judy Oskam 25:56

It would take you longer and you already have all the resources here. So well look ahead five years. You're gonna have some products out there in the world what's in five years what's happening in five years with you.

Dr. Aniruddha Bora 26:09

So there is one uh uh I just submitted a paper uh kind of like uh two days back and I already filed a patent for it kind of two two weeks back. So that is kind of like a new navigation uh software uh that is for shipping companies to cut fuel waste and give a more optimized path uh which takes into account the climatic conditions and stuff. So uh that has a big potential and and there is a big push in international maritime organization for all those uh uh uh optimizing those shipping routes so I think that kind of something I want to see like in five years being deployed in in uh I love that yes so that's one of the things already the laser is there so I hope I can you know develop it into a full fledged uh kind of system where it's being used to help people so that's another uh uh product that I'm looking into there's one more that that's kind of like very very uh small thing but uh it's it's kind of very uh useful thing. So that is that came from one of the discussion that we were having kind of like an industry and university uh meeting in Star Park that was in last year uh kind of November last week or December first to get around that time. And there was a person from uh Austin Energy he said like you guys work on so many complic complicated problems I have some good small problems you can solve they'll be very helpful I said yes why not? They said okay you know garbage overfill is a big problem for us. Yeah I said yes there is something we can do very readily about it. Because I never thought of those problems right these are very simple problems which are out there but you know there is no kind of structured uh uh work that has been done for that and uh I developed kind of like a uh again similar idea on what I developed the laser for but now instead of you know like having a weekly schedule the garbage can will have kind of a sensor on top on on top of it that will you know uh give you kind of like uh what's the field level and will give an instruction to the the garbage company or the government who's pickup yeah for pickup whenever it's kind of like you know near a threshold. Yes.

Judy Oskam 28:19

And instead of coming weekly because when I have wondered that you know I have wondered that too is we don't have it's not full.

Dr. Aniruddha Bora 28:27

Yes. So yeah so so it's kind of like so so many places they make a wasteful trip because the it's it's not filled at all. And in some places it's overfilled and and you know people have nowhere to go. And then uh right now we we show by simulation that saves a lot of money, a lot of fuel and and uh lot of less overfill uh problems. So right now I submitted an application for kind of doing a pilot study here at Texas State itself. So we are now creating those sensors and we are planning to deploy in Texas State University as a pilot site.

Judy Oskam 28:58

Oh I love that and then Austin Austin's ready for you. Yeah yeah oh my gosh so you are the problem solver and you're using math and data and AI to do that. Yes. Yeah I love that I love that well I want to thank you for sharing all of this and again I'm so excited about what's coming next for you.

Dr. Aniruddha Bora 29:20

Thank you thank you for the opportunity

Key Takeaways And Share Request

Judy Oskam 29:24

As you heard from Dr. Bora solving meaningful problems is all about staying curious, being open to failure and building the right tools. Well if this conversation sparked a new way of thinking about problem solving or the role of creativity in your own work I hope you'll share this episode with a colleague, a student or a friend. And you can find more episodes wherever you listen to podcasts and learn more about our work at Texas State University. Well thanks for listening to Stories of Change and Creativity. I'm Judy Oskam