Patty Vest:
Welcome to Sagecast the podcast of Pomona College, I'm Patty vest in these extraordinary times, we're coming to you from our various homes. As we all shelter in place.

Mark Wood:
This season on Sagecast, we're talking to Pomona faculty and alumni about the personal professional and intellectual journeys that have brought them to where they are today.

Patty Vest:
Today, we're talking with professor of mathematics, Jo Hardin, class of 95, a statistician whose work has applications in the study of the human genome.

Mark Wood:
Welcome, Jo. Good to have you, yeah. Glad you could join us here in zoom land. Yes. So how are you doing in these really strange and, and challenging times?

Jo Hardin:
You know, I I'm, I'm hanging in there. I have told multiple people that this experience has been, you know, or my life rather has been just incredibly mediocre. It's, you know, some, some really hard things along the way and some things that are really, you know, long days and, and whatnot, but, you know, my students have risen to the challenge and I'm doing some fun research projects and there's been some silver lining with being with family and all that. So you know, hanging in there

Mark Wood:
As are we all,

Jo Hardin:
We try to have fun when we can see my background, my background from the other movie. But tell us tell us a little bit about your childhood actually. You moved around a lot as a child. What was that like and how did that affect you as you grew up? So I was attending the high school where my parents had met when we started moving. You know, so if my parents had met at this high school, you know, you can imagine that we really it was in Portland, Oregon, so it wasn't a huge city, but it wasn't a tiny little city either, but certainly the community you know, we, we were ingrained in the community. People knew who we were and we knew people and, you know, it was just a small neighborhood in Portland, Oregon and whatnot.

Jo Hardin:
And my dad sort of felt like his, his position, his company was a little bit of a dead end. He just felt like he wasn't really gonna have opportunities to move up in it. And so he got a job offer right outside of Detroit, Michigan. And so we moved and it was really enough people. And then that position turned out to be a complete disaster. There were a bunch of, of scandals happening at the company at the time. And it was just like, I'm really not a good fit and all these things. And so that's why we moved sort of immediately after that. You know, he put in his one year and we moved to right outside of Cleveland, Ohio. So, so I ended up going to three high schools and and also that put me from Portland, Oregon into the Midwest where, you know, it was, it's hard in high school.

Jo Hardin:
So I probably didn't give the Midwest the chance that it deserves, but I was sort of done with the Midwest and really wanting to come back to the West coast. And my sister had gone to Swarthmore, older sister had gone to Swarthmore and I was thinking, you know, I want to go to a small, small liberal arts college like she did, and, you know, I want to go to the West coast. And so Pomona was very much on my radar. In fact, Pomona is the only college I applied to. And but really interesting this story that you're asking me about moving is a story that I actually tell reasonably often because people ask me about my name. So so my name is actually Johanna and the, the HANNA part is, is like you would call someone Hannah, right?

Jo Hardin:
Like that, with that sort of American "aa". But for some reason that most people with my name aren't Johanna they're Johanna, or they're Jo Anna, or they're Jo Ann or something like that. So so like I said, we were growing up in this community and and my name had never been an issue because we knew people. I ha you know, my friends, I had gone to school with since I was in preschool. So everybody had always known my name. I had never corrected anyone ever with my name. And then we moved twice. And and so for anyone who's, you know, who doesn't have a top 25 name, they they might resonate with this, that when people don't know your name or they don't know how to pronounce your name, they don't use your name. So I spent three years of my high school, like the last three years of my high school with people really struggling to say my name or to use my name.

Jo Hardin:
And then I would correct them sometimes. Or then if I didn't correct them, it was like, that's not my name, you know? And and you know, so the story I'm telling you is all on reflection. I don't think I really knew that this was happening, you know, in high school at the time. But then as you know, I came to Pomona and I have no idea why this happened. I think I even asked my sponsor at the time and she didn't really even have a good answer, but she put Jo on my door and I had never gone by Jo.

Jo Hardin:
I had never liked it. It was not a name

Jo Hardin:
Ever for me. And she put Jo in my door and then she did

Jo Hardin:
Totally makes sense. Your Pomona name. Yeah. Yeah.

Jo Hardin:
And so immediately I have this, you know, 1500 new friends and they all can say my name, nobody even ever questioned what my name was or who, or, or whatnot. And it was just like this, this like relief of, of, you know, all of a sudden, everybody knows my name and they can tell me, you know, they can address me appropriately and they're not anxious and I'm not anxious. And it was just a done deal from literally the day I walked onto Pomona's campus.

Mark Wood:
So that day that you walked onto Pomona's campus, did you already know what you wanted to do or did you know you wanted to do math, did you know, already wanting to do stats, you know, where were you?

Jo Hardin:
So so I did start as a math major and my dad's an actuary or he's retired now. He was an actuary. So I, I thought that that's what I wanted to do. So particularly like, you know, 25 years ago, which isn't really that different from today. If you look at if you look at, you know, Forbes or whatever, the top hundred jobs out there actuary is, is pretty high up there. It's not like a, you're never going to be a bazillionaire being an actuary, but you're going to make a good living without the crazy hours. And the stress of being like a partner at a firm or a, you know, a neurosurgeon or something like that. So it's, it's one of those good trade off a work life balance and a reasonable salary. So, so I sorta thought that that's the type of thing I would want to do. So, and in fact, while I was at Pomona, I took a couple of the first the first two actuarial exams. And I did some I did one summer I spent doing a internship at an insurance company kind of thinking I was going to do actuarial stuff. But what happened is that that I fell in love with Pomona college and I thought to myself what can I do to never leave Pomona college?

Jo Hardin:
And so you did, right. And so you know, Mark will know my predecessor Patty, probably not, but my predecessor's name is Don Bentley. And he was my professor, he was my thesis advisor and whatnot. And I knew how old he was like, I could do the math,

Mark Wood:
You're taking your actuarial design test. Right. Math major.

Jo Hardin:
Really. It didn't really seem like you know, like the stars would align in exactly the right way. But I thought, you know, this is a pretty good gig. I love the fact that you go into ferry and you're sitting with your friends and you're having this like super intellectual discussion about superheroes, you know, and it just like, like I loved the way people talked and and, and just sort of had really interesting things to say all the time. And I was just, you know, this is the sort of environment that I want to, that I want to live my life in. And so, so, yeah, so when I went to graduate school I tried to do things along the way that would you know, bump up my chops for going back to a teaching place. Tried to take on teaching opportunities.

Jo Hardin:
And I worked at the, at my graduate, I went to UC Davis and I worked at the I don't even have any idea what they call it, but like the teaching learning center or whatever, whatever it was called. And I would go into like other graduate student classes, like in chemistry and English and whatever. And I would sit in on their TA sessions and provide feedback for them and stuff like that. So, right. So then when it came time for me to apply to jobs, this job happened to be opening. Yeah. Stars aligned. 

Patty Vest:
Before you came to Pomona, you also worked at the Fred Hutchinson cancer research center for, for a bit.

Jo Hardin:
Oh, the middle, the middle of when I was, ah, when you were well, before I came to Pomona as a professor. Yes. That's what you mean. Yes. Sorry. After I was at Pomona.

Patty Vest:
So for your second life at Pomona,

Jo Hardin:
Right.

Patty Vest:
You worked at a cancer research center. What was it like working there as a mathematician and how did this experience impact your research interests?

Jo Hardin:
It was a great position. It was a really good group of people. And we were working on clinical trials that were being done in multi centers, so all across the United States. So we were working with medical doctors and other researchers molecular biologists and whatnot who were running cancer trials at cancer centers and hospitals all over the United States. So I really got to meet quite a few people and and I got to learn sort of how it works, you know, the nuts and bolts and the different sort of politics of everything and the different personalities of everything. So it was really an amazing experience for me. One of the, the best parts of the job was that the the, so this was in 2000. So I was, I had just graduated from my PhD in 2000 and the person who was hiring me had had just sort of embarked not him personally, but sort of like him as the PI, but he was the PI again, over this much larger conglomerate.

Jo Hardin:
I don't know if that's the right word, if that means for profit, you know, medical, medical conglomerate, if that's a thing with what was called, what is still called microarray data. So microarrays are these these little chips that allow molecular biologists to measure thousands of genes simultaneously. And and it was the first of what we call high throughput. So high throughput, that word just means lots of measurements at exactly the same time. And and now we measure proteins like that and we measure you know we measure so this was measuring RNA and we measure RNA in a slightly different way now. But, but this was this like game changing technology. But when you, when you end up with you know, thousands of measurements on a couple of dozen, or, or if you're lucky, a couple of hundred patients well, the, the measurements sort of swamp, the variability of the patients, and it's really hard to get good quality sort of measurements or, or models out of this data and, and standard techniques fall short in many different ways.

Jo Hardin:
So a good, a good, yeah, part of the reason I was hired was because everybody had a day job, right. Everybody right. Was doing their thing and they had lots of work and, you know, these clinical trials were moving forward and data needs to be analyzed and protocols needed to be written and all this stuff. And so he said, I have this data and nobody knows what to do with it. And if you want to come and just spend all day every day working with this data, then I will pay you. I was very cheap also at the time. And so I was like, sure. And so it allowed me to literally read every paper that was being written on this data, which is a bonkers idea because now there's probably, you know, a hundred papers a day, but come out speaking to, you know, genetic high throughput data that, and, or you know, statistical methods applied to high throughput data.

Jo Hardin:
And so you know, we, we had these journal clubs of a couple people, you know, the, the Fred Hutchinson cancer research center is a big place. So there were a couple of different stat groups in, you know, doing different working in different groups and whatnot. And so we'd come together and, and, you know, have like a journal club and read papers that were the Seminole papers. And so it was just a matter of being at the right place at the right time. And it's really informed all of my research or, or much of it. And when I got to Pomona Laura hoops, who's a Marita in biology. She she had some microwave data, she was working on with yeast. And so because of the work I had done previously she and I started working together collaborating and we even did some work writing sort of course, modules for how to get students you know involved in, in both collecting the data and analyzing the data.

Jo Hardin:
And we did some, some classes, her in biology and me and math, I think I only taught that class one time though. And it's led to a collaboration with now Dan Stovall, who who works on Ecolab at Harvey Mudd. And and now has data that's very similar, but it's second generation, which is RNAC data. And RNA sequencing is the full name, I guess. And you know, as the technology changes, the statistical questions change. And so so it's been great having, you know, that collaboration going and having my students able to think about what the statistical problems are and, and to be able to, you know, have that context of solving real problems and helping the biologists you know, move their research forward.

Speaker 4:
So, yeah, big data sort of seems to be in some ways, the defining element of our times the, the data that just is, is so huge that people don't even know how to start. But at the same time data literacy, I guess, in America is a very high you talk about teaching your students to think with data. What does, what does that mean? And is that a skill we all need these days?

Jo Hardin:
Boy, it is a skill we all need. Yeah, I mean, I there's so many examples of the, we'd love to have data for making decisions. You know, that's how we run in the world, but you know, you take some of the COVID data and it's, it's sort of fundamentally flawed it really basic levels. So you might want to know what is the death rate. So it feels like that's a pretty straightforward question. If you get the disease, how, how likely is it that you will die of this disease? And, and we sure that's a straightforward mathematical operation. It says, you know, out of all the people who have the disease, how many of them die but, but the problem is that we, we can't, we have no idea what that number is because we don't know the numerator and we don't know the denominator, which is to say, we have no idea how many people are actually dying of this disease.

Jo Hardin:
And we have even less of an idea of how many people have the disease. So it's really hard to get any information right now. That's gonna, you know, help model or help think about what's, what's going on you know, with the disease as we try to make a lot of decisions right now. And, and so, you know, is that I really try to get my thinking about is if you see a number, if you see any kind of results, you know, the first question you should ask yourself is how would they have measured that, like how, how could that data have been collected? And in fact, one of my favorite questions and I gave it this, this spring on my final is that I just give them like three New York times science article science headlines. So you know, there's one, one which was said that putting more dollars into mental health treatment reduces crime rate, something like that.

Jo Hardin:
Right. So, so some kind of connection that was the headline, some kind of connection between dollars spent in mental health and then crime rate. And then you have to think of yourself like what would that data set look like? This particular problem and hard for my students, I gave them like three or four headlines and they were fine with the rest of the headlines, but this particular one because you have to think about what were the data collected. So if the data were collected on a per person level, right, then you've got to measure something like, was the person in a care facility or not, or did they go through some kind of programming, mental, mental health programming or whatnot. And then, then you've got to measure, did that person commit a crime or not? But if you're measuring the data at a citywide level, then you're measuring something totally different.

Jo Hardin:
You're measuring the crime rate at the citywide level, but you're measuring, I don't know, you know, the number of dollars or something that goes into mental health facilities or something like that. And so really kind of forcing yourself to think about how data are collected you know, is, is a hard thing to do, right. Is it collected on the individual level? Is it collected on the city level? What are the measurements taken? But I think it goes a long way towards being able to interpret, you know, what the, what the results are, right. When you're reading about, you know, what, what that study says. I think it's really different if you're talking about cities versus if you're talking about individuals.

Patty Vest:
Yeah. I really liked how you explained the, the application of quantitative science and, and, and their relationship, the close relationship with applied science. And now math has become one of the most popular majors Pomona as a former alumni of the department, and now a professor in it. What do you, what are some of the changes that you've seen and, and why do you think it's three, what's the reason behind that popularity?

Jo Hardin:
So I'm really proud of some of the things we've done in math. And you know, I really appreciate your question. And the first thing I'd like to say is that if there's not a single answer, so you know, math is sort of surprisingly when compared to other institutions in the country, math is surprisingly one of the most popular majors. And so people will often say like, what's in the water. What did you do as if you just have one thing that you do, and, or as if it's just easy. And I think that my colleagues and I have worked extraordinarily hard to to make this happen. And, and that's not to say that other disciplines haven't and whatnot you know, but but there's lots of things. So for example math has always been on the on the forefront, pushing the facilities to let our students be in Milikin.

Jo Hardin:
We we are one of the only buildings that lets in every single student. So a lot of the buildings letting in their majors, right. With their swipe cards. And, and it was way early on before buildings had swipe cards that that students had actual keys, like, like I, as a student, I have a physical key to Milliken. You know, so we're talking in the early nineties, I had a physical key to the building because the math community, the math faculty and the students wanted to be working together and that my colleagues and I really believe that math is a collaborative endeavor and that the more we can be working together you know, the more creative we can be, the more we can be solving problems and whatnot. So I think that's one of the, one of the big things.

Jo Hardin:
You know, we've, we've tried lots of different ways to level playing fields. So you know, sometimes leveling the playing field just means creating an environment where people feel like they can be successful. And because it's, it's remarkable that two people with the same exact skillset will be on different playing fields. If one of them feels confident about that skill set and the other doesn't feel confident about that skills. And so you know we we've done this, I'm going to scholars with math based on the Pomona science scholars, just to be creating communities of, of students who are in math classes together, so that they're seeing people who have the same backgrounds or who look like them, or you know, just, just who can, who can sympathize. And and really, again, back to this collaborative endeavor, just really working together to to build community and to you know, to ask each other, what are you going to, what kind of are you, are you going to go to, you know, a summer research program, those types of things, you know, sometimes you feel like, Oh, everybody already knew that you were supposed to apply to summer research.

Jo Hardin:
And in January, I didn't know that. Right. So, so really changing the environment, changing that playing field so that everybody has the same opportunities and and, and feels a part of the community. I mean, we're, of course not always successful you know, on every front with respect to every student. But I think that I think that we, we get credit for trying and I think that's, that's deserved it, it doesn't hurt that you know, you can get jobs. So we have, you know, a good number of of students who are in thesis that get a extended STEM visa gets extended longer you know, or undocumented students who feel like this is their way to getting pieces. And, and that's, that's real, you know that's just the world we live in. So you know, we'll take that as well, especially now. Yeah. Especially now, for sure, for sure.

Mark Wood:
Well, you also take pride in the fact that I know that it's a very diverse and inclusive community in math. Can you tell us a little bit about that?

Jo Hardin:
I mean you know, we hear over and over from our students that they resonate with people who have been who have had experiences like they do. So you know, in that, that could be all the, all the ways. Right. So it could be you know, the, the gender, right. So, so here's a woman who I'm going to look up to because, you know, I've had students talk to me about how do you balance family? How do you balance being a mom, you know? And, and we've had lots of students who you know Edray Goins, who has been here a couple of years and and has so many students who ask him about being a black male in mathematics. And what does that look like? And, you know, what does that mean? But it, you know, it's all the things, right?

Jo Hardin:
So it's, it's another faculty who maybe went to a math camp and it's like, okay, I've always been sort of that nerdy, you know, I don't know if they recognize themselves as that, but like having those conversations. So we really value you know, when we're looking to hire our, both faculty and student, and I'm sorry, when we're looking to hire both permanent and temporary, really, really like, okay. We think of part of our educational mission in our department is supporting the whole breadth of our students. And so then the more you know, that's really a part of, of what we're trying to diversify in terms of our of our, you know, leadership in the department is making sure that there's people in the, in, in those faculty positions who can speak to you know, all the, all the students that, that come through our department. So it's, it's one part of the piece, the package, you know, when we're, when we're trying to hire, so, yeah.

Patty Vest:
Jo, can you tell us a bit about your students and what kind of senior thesis have you recently advised?

Jo Hardin:
I have fantastic students.

Jo Hardin:
I love my students so much. I have a student right now who is who's a rising senior Ethan Ashby who is working on the, this collaborative project with Dan Stobel. And Ethan was part of a Harvey Mudd. Dan is at Harvey Mudd. Ethan was part of a Harvey Mudd research program last summer, where we worked collaboratively on Dan's data. And it was really, you know, it's, it's so nice to have a student who works for an extended period of time because he spent all that time last summer, and then part of last year really getting to know the problem and, you know, understanding what different aspects of the problem we could, we could work on. And, and also which aspects were sort of more important to, to Dan's research and sort of understanding the science of it.

Jo Hardin:
And so now he's working on it this summer and and he sort of able to simultaneously, you know, as a rising senior understand the mathematical modeling that's going on under it and think about, you know, different ways. So for example, we're trying to, we're trying to fit a model. And and one of the pieces in the, in the software that is giving us some problems are what is, what's the initial settings of the model. So you sort of like iterate on some initial settings. And so there's all sorts of sophisticated ways that we can you know, create some initial settings that we think will improve the model drastically. And so, because he really understands the problem and because he's, you know, now got a couple of years of math under his belt that it's, it's such a pleasure of his you know, he's the one bringing these ideas to me.

Jo Hardin:
And and, and, and then simultaneously we're working with Dan and talking about you know, which of the parameters are most interesting to Dan and what does that say about, about what the you know, what the stress mechanism on the Cola is saying is doing and whatnot. But that's a, that's a project that's really, you know, close to my research and it's been super fun for me. And you know like I said, this whole collaborative thing is, is really a big part of what I enjoy. But I also you know, I I've worked with all sorts of students doing all sorts of things. So for example I'm I'm also in the process of working with some teaching collaborators, pedagogical collaborators. So I have a group of, of there's four of us who have done some projects together.

Jo Hardin:
And they're at they're at Reed and Smith. And then one when we started, she was at Duke and now she's at the university of Edinburgh, which is, which would be fantastic to visit her if we could travel, but now it just makes the time zones complicated. But but we're, we're building some online tutorials and what not with interest stats. And so I've developed a relationship through, you know, wonderful ways Pomona, you can develop relationships with students with with a student who I think you guys interviewed her, name's Marie Tanoe, and she's a double major in African studies and cognitive science. And she is somebody who is, I'm doing a raise with her this summer to work through the the, the modules that we're building. And so it's a, it's a win, win project where she can learn some introductory statistics and I can have a student, you know, actually going through the modules and telling us, you know, if they're, you know, in good shape, if they're confusing, if there's things missing links that don't work all of that. So and I, and I love that type of research, just as much, you know, thinking about my classes and, and trying to improve them and trying to make them, you know, these modules that we're building, we're making them open source really kind of open access for, for introductory material, for, you know, anyone out there who's trying to learn a little bit of interest.

Jo Hardin:
So 

Mark Wood:
Tell us what your students go on to do after promoting. You said that there are jobs out there, what kinds of things do they do?

Jo Hardin:
They, they do all the things. So a good number of them go to grad school. And then from grad school, they you know, they go on to be researchers at pharmaceutical companies or at academic institutions or, or I have a we have a recent student who who did a PhD at Duke and now is at the Dodgers. But we have quite a few as I'm sure, you know, from various other Pomona publications, we have quite a few who just go to a baseball straight out of Pomona even without doing any graduate school. So, so quite a few of my students do that, but they go to, to tech you know, to, I have one going to Facebook and, and have students at like JPL and sort of think tank type places a good number of them go on to finance type jobs. You know, they, if they decide they're not doing math or statistics after Pomona, they have a leg up almost always in whatever it is they want to do. So for example, if you're, if they're deciding they're wanting to go into finance most people look generously on the fact that they're also majors, right. Or if you're in biology and you've done statistics, right. That that it speaks volumes to your sort of quantitative abilities generally. And, and right. So, so our students tend to do quite well.

Patty Vest:
Jo, can you tell us a bit about research projects that you're working on that you may have not told us about yet? Sorry.

Jo Hardin:
I have a student who his name's Benji Lu, and he is at Berkeley doing a science PhD and he his senior thesis was was working on a on a method called random forests and random forests are a, it's a machine learning technique to sort of predict and in our case to predict a continuous variable. So what do I mean by that? Like, you're trying to predict how much income someone is gonna make that's opposed to a categorical prediction, which might be like, is your email spam, or is it not spam? Right. So like that, that binary or classifying some other kind of things. So, so, so the random forest that we're using are predicting you know, a numerical that variable that that is continuous. So one of the things that you know, one of the wedges in sort of the broad area of things that I like to think about is that machine learning sort of computer science tends to be really focused on good predictions.

Jo Hardin:
And so if you, you know, you hear words like neural networks or support vector machines, or deep learning, those are all methods that are very focused on really good predictions, and they do that well, they've been quite optimized and random forest fall into this as well. But, and, and statistics on the other side is really focused on what is the model, right? So it's all about parameter estimation. So you can estimate the parameters in the model then yes, as new values come in, you'll have better predictions, but not only will I be able to predict new data, I can actually tell you about the relationships between all the things I'm measuring, because I actually have sort of a model. I understand what it is. That's, you know, predicting someone's income. I understand if it's years of education or if it's parent's income, right.

Jo Hardin:
I understand which one of those is more important. And, and that's sort of been a little bit of a crossroads for statistics, as opposed to machine learning, computer science, whatnot, and data science is, is bridging that a little bit. So data science is something that is really trying to take these different areas and think about how they can come together and how they can work together to, you know, to do even better things. Right. so what we were able to do with Benji and and this project was his senior thesis, but it's I think he's 17. I think he's class of 17. And it's like just now in the final stages of being revised at a, at a journal. So it should be, it should be published, I hope within the next year, six months here. Anyway.

Jo Hardin:
So it's been a while, right? It's been a couple of years that we've been working on this paper. What we've been able to do is, is not so much think about the model and the perimeters, but think about when we do a prediction to give what's called a prediction interval. So when I'm talking about, you know, a random forest and I say, okay, what is the the income that's going to come out of this prediction? Well, is it, you know, 60,000 plus or minus 3000? Or is it 60,000 plus or minus 20,000, right. When you're talking about like, how confident am I about this prediction and how, how much information am I providing you about my prediction? It's not just about having a prediction, that's as good as can be, but it's also about assessing the variability of that prediction and understanding the variability of the population. And so so that's a project that I'm still working on, and that's fascinating to me because, like I said, it bridges these worlds that that I think is sort of the wave of the future of all of these disciplines. Right. Trying to think about, you know, not only can we have these good models, but can we understand them and can we understand the variability that's coming out of the models.

Mark Wood:
So you started off here at Pomona, you came back, you want a wig award. What, what has it been like being back on campus? How, how is it different from, from when you were a student and were there surprises when you came back? I did a little something at the same time. My first one of my first jobs was on, on my campus after I graduated. And it was, it was a few years after graduation coming back and there were some surprises.

Jo Hardin:
You know, the, the thing that I was sort of worried most about was my relationship with my colleagues. And as I said, originally that my advisor retired and so he wasn't around. And I think that was to my benefit. I didn't have anyone looking over my shoulder and in fact, you know, my colleagues, we haven't really spoken about this much, but my PhD is in statistics. So I don't consider myself a mathematician at all. And we, we have there two statisticians in the department now, but when I was hired, it was just me and, you know, nine mathematicians. And they were so thankful that I was there to teach these classes, that they had no interest teaching that they said, you know, you can do whatever you want, and we are so grateful to you. So I never had to broach that, are you a student or are you a peer relationship?

Jo Hardin:
I had taken classes from a few of them, but really you know, Bentley had been the person who I'd taken the vast majority of my classes with, and like I sent on my senior thesis and, and whatnot. So I was pleasantly surprised at how how much they treated me like a peer and how much autonomy they gave me and all of my classes and research and whatnot. So, so my colleagues in my department, I really appreciate that. And you know, genuinely you know appreciative in terms of like my sort of experience on campus a couple of things. One is that for a couple of years, every time I went somewhere, I had a memory or sort of like a visceral reaction of being in that space in sort of a different life. And you know, you'd go somewhere, you'd go to the wash for the first time. And you were like, wow, I remember the time

Mark Wood:
You don't have to tell us about that one.

Jo Hardin:
And and that has as expected dissipated, right? So now I go to the wash and I have all these memories of various other things that have happened in the, in the last 18 years. So, so, right. So that was sort of, that, that transition was just kind of my own you know, relationship with the college and the physical spaces. Mostly I mean, far and away, the, the main thing that is different from from now and then is cell phones, because it changes everything about how students interact. So when I was a student, you, you hung out because that was the only place, or that was the only way to find your friends. So you would go to the coop and you would just sit there for hours and you would hope that people came and you would, you would go to dinner and you would just hang out because you would hope that people would show up at dinner and you had no way of figuring out who was going to go to dinner. And you sort of built up routines where, okay. After class, you know, at two 45, we're going to be at the coop. And but but you know, just this, this sense of knowing where people are, or even parties right in the evenings, like you would just go and hope people were there is really, really different. There's none of that, like hanging out, waiting for people to just kind of be around.

Mark Wood:
So on that note, we're going to wrap this up. We've been talking with Jo Hardin, professor of mathematics. Thanks, Jo.

Patty Vest:
You're welcome. My pleasure. Thank you, Jo. Go down memory lane a little bit and to all who stuck with us this far. Thanks for listening to Sagecast. The podcast of Pomona college stay safe, and until next time.