Sudha Ram: Big Data, Dogs and the Dos & Don'ts of Online Privacy

Announcer 00:00

*music* Welcome to Wildcat Wonder, where we take you inside Arizona research. Breakthroughs, discoveries and advances happen so often at the University of Arizona, it can be a challenge just to keep up. We'll handle that for you. Join us as we explore the research innovation and awe inspiring work making a societal impact that maintains the U of A's reputation as a global leader. This episode meets Sudha Ram, Professor of Management Information Systems and director of insight center for business intelligence and analytics. Her research focus areas include big data analytics, machine learning, network science and business intelligence. She's an editor for multiple professional journals, a member of the 2023 Women of Impact cohort, and just a big fan of spending time outdoors. Our hosts are Lisa Romero, Associate Vice President of research communications and marketing, and Laine Kowalski, student writer and content specialist. Now let's turn it over to Lisa and Laine.

Lisa  00:59

Hello everybody, and welcome to our Wildcat wonder podcast I am excited to have. This is our inaugural podcast, and our first one lane. And I and our team have been working really hard on this, and I if I could have had my dream first guest, which I actually did. I did suggest my dream first guest, and you are sitting here next to us. So Sudha, thank you for being here, and we are very excited to dive in to your work and kind of your your dreams and your what you know, your experiences getting here, and just talk a lot, but let's start with a dog. Because really, I mean, there's really nothing more important than that. Tell us about your dog for a minute. I mean, let's be honest.

Sudha  01:52

Well, first of all, thank you for having me here. I'm just super happy and thrilled. Yeah, I love talking about my dog. He's my baby. My kids feels jealous of him. I think, no, they love him too. He's a rescue. He's a Ridgeback mix. He's about 80 pounds. He's a big boy, and he's very loving. I swear he can talk different languages. When I say he seems to understand. How old is he? He's almost five now. We we got him during COVID, okay? And he was about a year old, I think, then a year, year and a half, and this family was gonna give him back to the shelter where they had got him from in the first place. And at the time, I was looking for a dog, and I saw his picture, and I said, this is the one I want. And he came home, and he started doing zoomies. He was home, he came and licked me all over. And I told my husband, this is the dog I want. My husband says, Don't you want to look at other dogs? They said, No dogs. And so I knew, and I love dogs. Yes, there's not a dog that I met that I didn't like. So I fell in love with him. He fell in love with us, and he's with me now.

Lisa  03:18

Best story ever. And yeah, we've talked about it, but dogs save our lives and our community and our health and many things.

Sudha  03:27

He tries to sit on my lap.

Lisa  03:31

I have one of those, also 90 pound lap dog. They're great. Well, thank you for sharing that, and we're excited to dive into a little bit about your work and your history here at the University of Arizona, one thing I wanted to start with, because I think maybe you are one of the only people who can relate to me in this regard. I just gave a talk the other day about my origin story, and it was about the fact that when I started my first communications leadership position, I didn't have a computer, because computers didn't exist then...

Sudha  04:12

You're dating yourself

Lisa  04:13

I know, maybe you can relate to sort of coming into your career when that was still sort of a pipe dream, almost, and I had a typewriter and, you know, a corded phone on my desk, and that's how I started. And so, I mean, so much of your work again, which we'll dive more into today, is about what can be generated on technology and data and computers. So talk a little bit about those early years and how even without that, you kind of started to imagine the direction you might take in your in your work.

Sudha  04:50

So when I first came to this country in 1982 that's when the PC had first come out, and I didn't have one, but I did have access to a big computer. Through a terminal, big mainframe. Yeah, big mainframe. I had a terminal, and you had to dial up, and you had to use your phone and you put it inside that modem. I'm acting like I know, right? No cell phones, no no social media, World Wide Web. I did do my dissertation on the computer, because we had latex, but we didn't have, you know, word processors, Microsoft Office, you know, all that stuff. And so I would have to go to my lab. So I have a joint degree between computer science and the business school. So one foot in very technical stuff, and one foot in a very practical sort of way to think about businesses and so on. And so when I got recruited here, that's when we started setting up a lab, and we got a lot of PCs, no laptops, but PCs were great too, right? You could have one in your office, and you no longer, I mean, you used to connect through a local area network. You could send email, but you always had to go to the library to read papers, like, actual books, yeah, reading actual books, actual paper, yeah, actual journals. But even at that time, I had this vision of a world where everything would be connected eventually, and so,

Laine  06:30

yeah, what was it like to be ahead of the game so early?

Sudha  06:34

People thought I was crazy.

Lisa  06:37

As visionaries are!

Sudha  06:40

So we had local, you know, Local Area Networks, which were connecting PCs together, and we could send email through BitNet and ARPANET. And so my vision was, you know, about data, because I was always fascinated with databases. And when I was going through my degree, my dissertation was on distributed databases, which is, you no longer have a centralized system like what American Airlines had, but you could split that data into different parts, and they would be all over geographically different parts of the world you could connect with them. And so my dissertation was all about, how do you design a model to optimize the performance of these distributed databases? And people said, oh, you know, how would that ever happen? My advisor didn't think I was crazy. You know, she was very appreciative that I was thinking like that, and she encouraged me, but she also said, you have to be very rigorous and build these models and then show people the value. And so when I came and presented that here, Jane Anna maker, who was the department head at that time, he said he loved it because he could see where the world was moving. And so he always encouraged me to think wildly, and that's how I got started. So

Laine  08:06

even though there was a lot of people around you that maybe didn't believe you or thought you were crazy, you still had people who supported you, right?

Sudha  08:12

And my passion was I saw this technology coming, and I said, I, you know, I didn't know about the I didn't even think about World Wide Web and these smartphones and apps, you know, obviously never thought of that, but to me, it opened up a world of possibilities, and I was fascinated by technology. I wanted to see where it would go and how I could harness the power of technology and data eventually to do like things that would eventually help the world, but also kind of push the frontiers of knowledge, and how I could make other people appreciate how technology can be really useful. You don't have to be afraid of it in any way.

Lisa  09:05

One thing I've always really appreciated about your work too, is you have taken, you say, this, this business, this business background, this, you know, technological innovation vision, and you haven't even turned that into maybe the most logical thing. A lot of what you do, as you just described, is about help, using that technology, using data, using technology to help people, to help situations, which you know isn't necessarily really business focused, it is, it's humanity focused. So I really love that about your work. I think one of the first projects I knew about yours was, you know, again, early when I started at the University, was you were using data, I think, to in social media to predict asthma attacks. Yeah. And I have asthma. My daughter has severe asthma. So I that immediately struck me. And, I mean, you know, talk us through how, again, that even comes into your mind, and how you start thinking about that.

Sudha  10:12

So, um, so when I saw, so the world wide web came into existence, the internet around the 90s, you know, just before that, right? So all the bit net, ARPANET, all this network connectivity, kind of gave evolution to the web, the internet, and then social media, things like, that's around 90s. It started.

Lisa  10:35

It was like a domino once, yea...

Sudha  10:39

And so when the first version of the web came out, and I started thinking about, you know, I need to go beyond these distributed databases. We have data that's going to be coming from other places. It's all going to be mixed up in different forms, you know, not just text and numbers, but also voice and video, and I saw that happening. And so that's when I set up my so early 2000s is when I set up my research center. Till then, I had this group called the advanced database research group, where we did a lot of work on connectivity, on the, you know, on a network with data. But data has always been my focus. And so in the 90s, when the web started coming out, I could see a lot more types of data and sources. And also I started to get very interested in machine learning. Around that time, the beginnings of machine learning, and I said, you know, I the technology is moving in that direction. And so I set up this research center called Insight. And tagline for the center was to challenge, solve grand challenges, to address grand challenges that have social implications. So and and health care was a big deal because health care has a lot of social implications, right? Everybody wants to be healthy, everybody wants to be proactive in anticipating any health problems. There were also other challenges, like within the public university, which I'll talk about. But one of the I was in a panel with the CEO of a foundation, of a Healthcare Foundation, and I was talking about how we can harness the power of data machine learning technology to support healthcare. And I was laying out some kind of really crazy ideas. And he said, I'm interested, can you come to my hospital and talk about this? And I said, Yeah. And can you help us solve problems? So my, I guess, so my, you know, so my business hat and my technical hat sort of sort of started merging, and I said I would have to talk to your physicians, your caregivers, to figure what exactly your problems are, because this is not a silver bullet, you know. You need to sort of figure out what the challenges are, and then design solutions. And so that's when, when I talked to the hospital, it's a very large hospital, and we started looking at what their problems are, and one of their big challenges was the emergency room, like, who would show up in the emergency room on a given day, so that they have the right physicians, the right equipment being prepared for it. And that's how it all got started. And then we narrowed it down to chronic conditions. And respiratory conditions are a big challenge, because you can never predict who's going to show up in an ER with asthma related complications. And often people think it's because of the, you know, pollution in the air, the weather, but there's so many determinants stress and what you eat, how much you exercise.

Laine  14:06

What was it like during COVID?

Sudha  14:09

COVID was challenging, you know, that was the other kind of situation. I did a lot of work, you know, because of what I did with harnessing different types of data. So during COVID, again, the university reached out to me and said, How can you help us? It was one of the projects people, yeah, people help them with the wastewater, you know, things like that. And so I said, you know, contact tracing that's going to be so important if we want to bring people back safely to campus. So let me sort of think about the digital traces people leave behind when they use their phone they connect to the internet, and let's see if we can use that to. Figure out, you know, where crowds are anticipated. Where should we should put the COVID test kits, because we want to see the traffic pattern, right? Yeah, what rooms in buildings should require? You know, more cleaning than others. Where can we? How can we help people figure out, you know where they were in the last 14 days without violating their privacy? So it will help them recall where they went, and that would help in contact tracing. So that's how we started thinking about it.

Lisa  15:36

That's incredible, and, I mean that largely informed a lot of the decision making of the university during that time.

Sudha  15:43

Right? The policy making like, you know, there were curfew policies, and so are they working or not? And if you can use these digital traces to look at people's movement and behavior without pinpointing individuals, so privacy preserving way, yet being able to give some insights into people's movement and habits, that would be very helpful. And it was the same thing I did with, you know, with one of the biggest challenges for public university like us, on the freshman retention. Would you like me to talk a little bit? Yeah, sure. Yeah. So that was another project. When I set up the Research Center, the University said, how can you help the university? Can you help us get more money? And I said, I don't think so. I don't think so, but I could probably help with others, but sort of from that idea, you know, we're a large public university, and for me, students have always been front and center, and I've been very passionate about public education, yes. And so if you look at public universities over the years on an average, not just ours, but all public universities, the retention rate is anywhere between 50 to 70% that means one in two or one in you know, two out of three people drop out, right? Which is but our mission as a public universities, bring students in, serve them, make sure they graduate. But rather than sort of thinking about graduation at the end of four years, you want to see are they going to finish even their first year? You can't retain them in their first year. You're never going to have them graduate, right? And a lot of students drop out in the first year, not because of grades, not because of academic performance, not because of demographics, either. I mean, those might be some reasons, but it's there's other reasons. There's social science theories that inform us, and the social science theories say that people who don't establish a regular routine on campus, those who don't sort of build good social connections, they tend to get isolated and they drop out. So how do you find out when that's happening? And usually that happens within the first 10 to 12 weeks of the semester, before they even see their grades, right? And so how do we actually predict who's going to drop out, but in a timely manner? So you can do interventions, yeah, yeah. And that was the challenge, and so we did not need their grades. So we looked at again, the Wi Fi records, but anonymized, and we developed some algorithms, which is my computer science hat, my technical hat, but at the same time using social science theory, which is coming from the other side, you know, the business side, and we were able to determine if People were establishing a routine on campus on a regular basis, if they were starting to form social connections without violating their privacy, and we could use that to predict who's at danger of dropping out. And then we could give that information to the student retention office, who could then de anonymize and decide what sort of interventions to do.

Lisa  19:21

Wow! Well, social science theory, I'm gonna, I'm gonna talk about that for a minute, in general, because everything you've just described tells me your brain doesn't shut off very easily. Social Science, sciences, mental health, what are some ways that you, you know, shut that down and and take care of your own mental health. And, you know, think about that, work life balance. Yeah, work life balance, because this is, this takes a lot.

Sudha  19:59

So I enjoy what I do, but it's not always all about work. It's also about spending time with people, but outdoors. I mean, the reason I came to Tucson was because, you know, it's an outdoorsy place. It's you can be outdoors most of the year, except maybe the summer. But, you know, you change your lifestyle in summer, you get up very early, or you do it in the night. And so hiking, I love I love the mountains. I love biking. We have the River Park, but hiking in Sabino Canyon, Catalina National Park, taking my dog for a walk, spending time with friends and family outdoors. That's sort of my big thing, you know, doing meditation. So I love the Chiricahuas. Oh yeah, it's a little far, but it's worth it. It's so beautiful. It's so peaceful. The first time I went there, I couldn't take my dog. I went there with my husband.

Lisa  21:11

*laughter* totally relate

Sudha  21:15

So we walked, and we were the only two people on this trail, the Echo Canyon loop. It's, it is so peaceful. I could just sit there, and I could hear the whirring of a hummingbird's wings. I could hear a little bird kind of trilling. I could hear the bee flying. And I felt like, oh, and I closed my eyes, you know, my ears start working better, and I could just imagine that without having to open my eyes. So that's my go to place. But I do, I mean, I spend a lot of outdoorsy time. I love gardening. That's another way for me, I sometimes kill a lot of plants, but I'm learning which ones. I'm learning which ones grow better here.

Lisa  22:10

Yeah, that's a trick for sure, especially in the weird seasons we have. Yeah, I totally agree. I mean, nature is yeah. I mean, nature for sure. Dogs. It keeps you pets in nature.

Sudha  22:24

Yeah and I could just sit there and meditate and not realize the time is passing by. You know, I just blank out my mind and then it helps me focus better when I get back to reality.

Lisa  22:39

Yeah.

Announcer 22:42

This podcast is brought to you by a growing partnership at the University of Arizona. The Office of Research innovation and impact oversees more than a billion dollars in research activity. Its communications and marketing team is committed to sharing all the incredible breakthroughs and discoveries, especially the impacts they have on our society. The School of Journalism in the College of Social and Behavioral Sciences is an accredited program with a strong focus on global journalism as well as audio and visual reporting. Students are trained in the latest technology they need to succeed in an ever changing digital media environment. One example is the internship offered that supports the production and promotion of this podcast. Now back to our hosts.

Laine  23:21

We're here with Sudha. Can you tell us more about, especially in the digital age today, and especially as someone who's younger, grew up with technology basically in my hands. Can you tell us more about, how are we supposed to protect people's privacy?

Sudha  23:34

So privacy is very precious. It's very important to think about it as a user of technology. You know, we take technology for granted. We take our cell phones. So every time we use our cell phone and it connects to the internet, we're leaving a digital trace. Every time we buy something from Amazon, we're leaving a digital trace. Everything we use and every time we use an app, we're leaving a digital trace. If you use the Wikipedia, you post reviews, you use Reddit, use Instagram, you check into some place, you are leaving digital footprints. And these footprints can come from many different apps, from many different pieces of technology, and when you join them together, you get a very complete picture of the person. So as a young as I always tell my students and people that we're beyond the point where we can shut down the technology this. This happened even with credit cards. Every time you use a credit card, you're doing a digital trace. Can we live without credit cards now?

Laine  24:47

Probably not.

Sudha  24:48

We can't. Can we live without our phones? Now? We can, right? So it's part of our life now, and so you have to be careful about what you leave on social media. Yeah, you don't want to post things unnecessarily about what time you leave home every day. You know when you're going for a vacation. I wouldn't even do check ins to places because you're leaving a location and a time. And if you do that regularly, enough people will know what that is, right? And they can misuse it. They can use it for good. They can misuse it. Yeah, on YouTube, you know, they're tracking what videos you're watching, right? So there's a good side and bad side. And so as an individual, you have to decide how much of a digital trace I want to leave, and what I want to leave in that. So you have to think about that. It's sort of like I give an analogy that you wouldn't walk into a public road without wearing clothes, right? So when you use social media, essentially, you're exposing yourself. And it's okay to use social media, but you don't want to share over share too much about yourself.

Lisa  26:08

That's a fine line. That is a battle. I mean, yeah. I mean, how many times have I feel like we say things out loud, and then I look down at my phone, it's like, okay,

Sudha  26:22

And it's there permanently. Once it is there permanently, you can't erase it.

Lisa  26:28

Yea, it's interesting. I feel like, I don't know about you Laine, but my you're about the age of my daughters and they it's kind of an interesting shift. I think we were all we've all just been so fascinated, and we all are learning that maybe the hard way that we kind of have to place some boundaries with our technology. But because they don't use it like, like I did at first, they they have a lot more, especially my youngest daughter, like she just does not she she watches it, but she does not post a lot. She does not, I don't know, maybe that's a good trend for me.

Laine  27:05

Like I I tend to stay away from commenting, liking certain things. I do just kind of scroll. I tend to avoid posting on, like, Tiktok or certain things. So yeah, I do think it's important, but I think it's difficult, because my generation grew up with it, like when technology just started to come out, and so we never got to see what it was like before. And the dark age, we were so young we didn't know how to manage that privacy aspect. So a lot of stuff is coming back to haunt people later, you know, I mean, but,

Sudha  27:43

But, you know, there is a flip side to that, which is we, we have wearables, right? I do a lot of interdisciplinary research, so I think about health, you know, medicine and health. And you know, wearables help you track your sleep patterns, you know, your sugar levels, your heart rate, exercise and exercise. And so if I could save all that and share it with my doctor, they could see so much more information about my lifestyle than you know, the episodic information that they get when I go to see them, right? And so when they ask me, like, what have you been doing for them? You know, did you, you know, my records will tell me, Oh, I actually sat and binge watch, this big piece of binge watchingand I only ran like a mile in the last three days. I'm very active. Looking at No, maybe not. So there are things like, and then they can see irregular heartbeats, yeah, they can see irregular sleep patterns, you know. So getting serious about it, it's like there's a lot of insights you can get, and if you can share it with the right people, they can make a better, proactive diagnosis, which is what we did with our asthma project right harnessing people's eating living habits, where they're going to see pollution levels, and then using it to predict when there'll be a surge in the ER. And so that's a good use of it. And you want people to share information, but you have to share to the general public. Probably not right. You have to be careful about who you share.

Laine  29:41

Exactly.

Lisa  29:43

You just mentioned you working across disciplines, and we've talked a little bit about that through your projects, but maybe this is, this is kind of like picking your favorite child or pet. But do you have any project that you've done you're here at the university? Arizona, or, more broadly, that kind of is your heart project, your favorite, something that you feel has just really profoundly changed the path of your work.

Sudha  30:12

I think probably my freshman retention project is my favorite. That's because I became a professor because I was very interested in research and teaching and pushing the frontiers of knowledge, and my passion has always been to not just teach out of a textbook, but teach out of my own research and experiences. And I don't want to train students to just learn what the textbook says. It's to think critical thinking, and to be able to apply all the concepts they learn. And so students are like, really front and center, you know the whether it's doctoral students who help me learn a lot, because I'm always learning from them. Of course, my graduate students, whether it's in the online or on ground program, and my undergrad students, I just love them, because they teach me so much about all the latest and they come with with this fresh perspective, right? You know, enthusiasm students from the Honors College or elsewhere, and so to me that the having those students sort of gives me energy, and then, you know, they come from different disciplines, and I've always wanted to do something interdisciplinary, so the freshman retention project, the fact that I Teach a network science class that students from different disciplines take. You know, networks are essentially seeing connections between things, and that's applicable whether it's public health or medicine or biology or evolutionary biology social sciences. So I really enjoy that part of it.

Lisa  32:02

One of the best things for being at a university for me, and again, I don't get to teach directly, but I can't imagine, you know, not interacting with students. And I think I don't know, talk about, maybe for you, that idea of how, when you started in this role, you know, as a student coming in and you have had all these classes, but maybe how real life and a real job has, you know, kind of the same thing, right?

Laine  32:30

Yea, no, it really is. I think it is very difficult to manage all of these things, but you you kind of get it at some point. It just kind of clicks at some point, but you really have to work at it.

Sudha  32:41

So yeah, and that's great, because I see students who come tell me, I want to do a project with your research center. Yeah. And I'll say, Well, what are you interested in?

Laine  32:53

Sometimes we need guidance.

32:57

So it's like, what do you enjoy doing? What are you planning to major in and they'll tell me, oh, you know, like you, astrophysics and journalism. People who are doing evolutionary biology and Portuguese. Music and business.

Lisa  33:14

So cool!

Laine  33:15

People have like two different paths that they...

Sudha  33:18

So we try to work something out where, you know, I can harness their interests and have them try something new, but also my experiences with, you know, leveraging technology in different ways. So we, we've done this fabulous, very interesting project on computational art, where I used to teach these students network science and how to visualize large networks, you know, in different fields, like looking at transfers between clubs that play soccer, looking at purchase patterns on Amazon. You know, so many different kinds of networks, and you visualize them, and you can visualize them in different ways. And so I happen to be talking to this professor in the music school, and he said, I'm going to have a computational art exhibition. And I saw some of these pictures that you know, your students had posted, can you create artwork out of it? So, you know, that generated an idea. I could do some fundraising for these students. So we printed these out, and they, I think at some point the university even said, let's create an NFT, a non fungible token for it. And then we had an exhibition, and then we had our board members come, and they bid on these pieces of art, and the students had done a wonderful job with different in different disciplines. And that was my first foray into computational art.

Lisa  34:56

That is so cool.

Laine  34:57

That's incredible.

Lisa  34:58

Before I came to the university, I worked at another science based organization, and similarly, there was, I walked into my office the first day and there was beautiful visuals, I mean, art pieces up on the wall, but I couldn't really tell either very abstract, and I couldn't really tell what it was. And it ends up being that they were images of data from, like oncology cells, cells, you know, with oncology patients, etc, blown, I mean, in this striking visual imagery. So, I mean, it's really amazing. Yeah, you would never think that like that would ever be, but I was like what?

Sudha  35:39

That kind of triggers the thought, you know, something near and dear to your heart. Laine was, I was very interested in looking at how news articles spread through social media...

Laine  35:51

Right, yeah.

Sudha  35:51

And so at that time, it was called Twitter. Now it's called X, and data was public. You know, I worked with the student to collect data from Twitter's public pipeline, and we looked at how news from New York Times diffuses globally, how it's different, how the diffusion patterns are different from BBC versus CNN versus Fox News versus some blogs and things like that. And we visualized these really huge networks where you could see people reading articles, sharing them, and then people re sharing them, commenting them. So you see this connected, yeah, and you see this connection, and that became art, but then you could look at it and see, I mean, you could mathematically analyze them, which was important, because visuals only tell a certain part of the story. But the visuals became, you know, sort of the start of the story. To say, this is how New York Times is very different from BBC, and this is the life span of a story on New York Times versus BBC, etc. So it's just fascinating what you can do with networks visualization, and then you know the data itself. Have you guys looked into like tracking misinformation?

Laine  37:18

Yes! Yeah, curious about that as a journalism student.

Sudha  37:22

I am very... so that's another area. I actually have, one of my latest papers is on identifying fake information, and so you look at so what I've done is developed some methods to identify and do what's called fact checking, but in an automated way, where you take a piece of information, use different sources to check the facts, and you use networks for that to say whether the information is correct or incorrect, and then you can predict and then pop up a message to say, this might be fake. This might be real, because for a human being to go through is very difficult, so yes, very interested in that area.

Lisa  38:08

Well, that's a little relevant right now, isn't it? I mean, that's yes, it's yes, it's scary. I mean, it is scary because it's, it's so sophisticated now too. Yeah, there's no way anybody

Sudha  38:20

Yes, there's generative AI now, and it can generative means that you can produce new information by training it with old information. So you can produce videos, images, text, based on some training you provide. And it's often hard to tell the difference between a generated video or a generated piece of art and but there's also a lot of IP issues associated because AI machine learning, you need a lot of training, and a lot of the training comes from data that people post on the internet, you know, images they share pictures of themselves

Lisa  39:02

on social media

Sudha  39:04

again and so again, you know, so that there's still a lot going on in terms of regulations, policies for AI and how do we not over regulate so You don't stifle innovation. But at the same time, you know, where is things like GDPR, you know, it says things like, you know, if you collect data, you must make sure that you're not violating someone's privacy, and you have to say what you're going to use it for. But there's a thin line between, well, sometimes when you harvest data, you don't know exactly what you're going to use it for, and there's some IP issues associated with the algorithm that you produce. And so you want to be able to do that, but in a way that doesn't violate privacy and also accounts for. Or intellectual property?

Lisa  40:03

security people and yeah, yeah. Well, you've been at the University of Arizona for a very long time. Yes, 40, almost four years. That's incredible. What? What about? Maybe something, maybe not obvious. You know about the U of A but what makes it so special to you, or what has made you stay? Yeah, what? What has made you want to stay?

Sudha  40:32

I think what makes me stay here is the fact that I can collaborate with anybody, work with anybody, whether it's another lab, another department, with any student, pretty much, you know, there's no boundaries and and so, you know the I think the most interesting work happens at the boundaries of disciplines. And so I've always been sort of unconventional in, you know, going and collaborating with different people, whether it was for the I plant project RII, or with people in the medical school or people in archeology. You know, I've had grants with people in archeology, with people in renewable natural resources. So to me, I want to push the frontiers of knowledge. I want to produce students who can think like that, and students are very curious, and they want to do that. I want to make it fun. So I feel like, U of A fosters that kind of environment. It's it's very open. And I've never felt like I've owned, I've never felt like that. I've needed to publish, you know, a small, incremental paper, or

Lisa  41:48

stay in one lane.

Sudha  41:49

Yeah, stay in one lane. I've sort of always gone in different places and and I've, I feel like this is the community, you know, I want to be in. And when I first came into the profession, my discipline was sort of, it was, it was in one lane, you know, people were doing a lot of behavioral work, but I had a passion for technical work and combining it with business, and so I didn't want to let that go. And for the sake of making tenure sort of follow these so I decided I'm going to make this community.

Lisa  42:27

You started your own path, yeah,

Sudha  42:29

And it was difficult. There weren't very many women doing that sort of thing either. And it was hard. But when I made tenure, I started this workshop on information technology and systems, which was a combination of technical work, but with, you know, sort of the business aspect to it. And then that led to a paper that I wrote with some friends in the field called Design Science in Information Systems, which was how to create artifacts that are rigorous, but they have some models, methods, but at the same time, how do you show their value? You know, in real life, and that became like that's become a dominant paradigm in the Information Systems field now, and now I'm trying to move people into doing more interdisciplinary work. And I came into this department which kind of fostered that, you know. So I'm lucky, you know. So I was in an environment. So I'm very grateful to the U of A and to the Department of the college. They actually appreciated it, as opposed to sort of pigeon holing me into one, you know, slot, yeah, and that's what I want to do, is to train people to think in critically, in different directions, outside the box, and not feel like, you know, It's just about publishing. It's about enjoying the process. It's about working with students and integrating research and teaching and service and all that together.

Laine  44:10

You said that originally you were one of the only women who were kind of involved in this, in the technical world. So yeah. How did you kind of go about that? Especially, I'm curious as someone in STEM as well.

Sudha  44:20

Yeah, so I think, you know, I was lucky that in my field, I mean, I persisted, to persist, you get a lot of people say for sure, oh, you're just building systems. No, I'm actually understanding technology and producing things which will be useful. And so why don't you just do this kind of research? This is what I'm interested in doing. I think that's where the future is. And people actually appreciated that. We had conversations about it. So there were a few people. We formed a community. We expanded the. Unity. So you have to, sort of, you know those ideas, you have to bring everybody on board, and a lot of it is about helping them understand the value, and you have to be patient about it.

Laine  45:15

This is why you're one of our former women of impact

Lisa  45:17

That's right! And an ongoing one... well, thank you. This has been really, really great, and we've gotten to know a little bit more about you and your work and your life, and you are a treasure. Here I was thinking the whole time, like, maybe she I could go back and be a student in her lab.

Laine  45:43

I would love to be your student.

Lisa  45:45

Laine, you can't do anymore. *laughter* I was thinking that the whole time, how cool that would be, but what a gift that is to our students and to all of us here, that you are here and you share all this with us. So thank you so much, and we will tune in next time to another episode of Wildcat wonder.

Announcer 46:09

Thank you so much for joining us for this episode of Wildcat wonder, the podcast where we take you inside Arizona research. If you enjoyed what you just heard, just send it to a friend colleague, or maybe share it on social media, remember to follow the office of research innovation and impact on your favorite platforms, and if you have a good suggestion for a person or project to feature on an upcoming episode, email research@arizona.edu. Bear down.