Professor Melissa Lynn of the Department of Mathematics, Computer Science, and Statistics at Gustavus on hating and then loving math, researching sums-of-squares formulas, teaching math and programing, machine learning, and gender inequities and discrimination in mathematics and the sciences.
Season 6, Episode 3: “Like Solving Puzzles”
Greg Kaster:
Learning for Life at Gustavus is produced by JJ Akin and Matthew Dobosenski of Gustavus Office of Marketing. Will Clark, senior communications studies major and videographer at Gustavus, who also provides technical expertise to the podcast, and me, your host, Greg Kaster. The views expressed in this podcast are not necessarily those of Gustavus Adolphus College.
For some people, just hearing the word mathematics causes anxiety and even dread. For others, of course, it’s the basis of a fulfilling career. Among the latter group is my colleague and guest today, Dr. Melissa Lynn of the Department of Math, Computer Science, and Statistics and Gustavus. Dr. Lynn earned her BA in mathematics with honors from the University of Chicago and went on to earn her PhD in mathematics at the University of California Los Angeles in 2016. She joined the Gustavus faculty in 2019 following a three year teaching postdoc in mathematics at the University of Minnesota.
At Gustavus, Melissa teaches courses in computer science, machine learning, computation, and the analysis and design of algorithms. An active researcher as well, her work focuses on the sums-of-squares formulas and, don’t panic, explanation forthcoming. Work that has yielded not only her PhD dissertation but more recently a publication in the Journal of Algebra and other publications in progress. Melissa’s strong commitment to mathematics education is reflected in the math and machine learning summer camps for middle and high school students for which she has written curriculum and taught as well as in the grant she received in 2019 from the National Science Foundation for improving undergraduate STEM education.
Equally strong and impressive is her commitment to equity and diversity. Evidenced most recently in her participation with other Gustavus women and faculty in a panel discussion of their experiences as women in STEM fields. She exemplifies Gustavus’s twin commitments to academic excellence and social justice. And now I’ll stand in my own bit of math anxiety and I am delighted she can join me on the podcast to talk about her story and work as a math professor at Gustavus. Welcome, Melissa.
Melissa Lynn:
Thank you. Thank you for having me.
Greg Kaster:
My pleasure, it’s great to talk. And as we were saying before we started recording, we actually haven’t had the pleasure of meeting one another in person so this is that occasion as well. You’ve been at Gustavus… you started in 2019, as I said. So you haven’t been there that long and also much of that time has been the pandemic, how is it going, first of all? Are you teaching in person, hybrid, online only?
Melissa Lynn:
So right now I’m teaching all online which is very different from how I expected my teaching at Gustavus to go and really how I’m interested in teaching. So it’s going pretty well, I think. We have had some students doing on campus courses but we started out all online and part-way through the semester when I asked my students, “How’s it going? What do you want to do going forward?” They said, “Hey, this is working. We want to be responsible and stay safe with COVID,” and so we decided to continue those courses as online courses. But I certainly miss the energy of having students in the hallways, getting to see them in class, and just miss normal interactions as part of being at Gustavus.
Greg Kaster:
Yeah, same here. Especially at a place like Gustavus, a liberal arts college. I attended Northern Illinois University for my BA and MA in history and then I went to Boston University which is also huge. But my wife Kate went to Bard College in New York, upstate New York. I don’t know where exactly in New York. Anyway, small, liberal arts college. And yeah, I do really miss that about Gustavus. I’m doing all online from Minneapolis. But one thing you just said which I strongly agree with, I have found the students are really… I mean, for the most part, the students have adapted well, they’re taking the work seriously, real learning is occurring, they’re being responsible, et cetera. So it’s not the end of the world, far from it. Had you done any online teaching before? I had not.
Melissa Lynn:
No, I’ve done some video recording as part of a flipped class where students watch videos recorded outside of class and then instead of spending time on lecture and introducing material in class, we do a lot of exercises in group work. So that experience helped a lot with online teaching but no real online teaching and so, yeah, that’s been a new experience and I’ve been really impressed with how our students have handled it.
Greg Kaster:
Yeah, same here, I agree. I was only half joking in the intro when I introduced you about my own math anxiety. These are my memories. I remember, it’s either seventh or eighth grade junior high and I can picture the teacher. I won’t say his name, I can picture him perfectly handing back work. He would call out the grades as he handed back the work. And in my memory, calls out that I received an A and I’m excited, I go up and I look at the work he’s handing to me, had to go up to his desk and it’s a D. And then the other memory I have is being in high school, I can picture this teacher as well and something similar, I know I passed both courses. And then I have no memory of taking math at Northern Illinois University where I did my BA. I must have but I have no memory of it.
And then I’m a PhD student, I need a second language, I’m at Boston University and they allow statistics. And I’m petrified but I take the course, love it, do well enough that maybe it was a B or whatever and pass. So I like to say I have a complicated relationship with math but how did you come to math as major, were you already interested in it in high school, what brought you to it?
Melissa Lynn:
So I would say kind of when I think about my relationship with math, it sort of starts in second grade where I remember telling my mom, “Oh, I hate math. It’s the worst.” Because we would do these drills where they’d ask two plus five and you’d have to answer right away. And the way I would figure things out, I would piece numbers together and think it through every single time reasoning it out whereas everyone else was just trying to memorize these and so I felt like I was really bad at it because I was thinking it through slowly like if you’re adding eight and seven I would say, “Okay, if you take two from the seven and make a 10, then you’ve got 10 and five so you’ve got 15.” But not actually memorizing the answers there.
So that’s my first memory of math is not liking that aspect of it. And I think that that’s an issue that people have with math that they’re thinking about it as the memorization or following a procedure but really how working with math is is the reasoning and solving things and figuring out ways to do things which makes it tons of fun. It’s like solving puzzles all of the time.
Greg Kaster:
I can… Oh, sorry. Go ahead, go ahead.
Melissa Lynn:
Yeah. So I think my relationship in the next couple of years with math started to change a little bit as we started to get into a little bit more past basic arithmetic and we’re able to do more things that required some more of that thought and so I just got to really like it, I would always think about things really hard and puzzle them out and so from there, really kind of took off.
Greg Kaster:
What kind of math were you studying, let’s say, in well grade school even or junior high, high school? I mean, was it algebra, geometry, statistics, all of the above?
Melissa Lynn:
So I was really lucky that in middle school and high school, I was able to be a part of a program at the University of Minnesota called [inaudible 00:07:40]. Where middle school and high school students can take their math classes at the University of Minnesota and so then I was able to get through the algebra one, algebra two, geometry, and pre-calc while I was in middle school at an accelerated pace. And then when I was in high school, I was starting calculus so that was a really great program for me, it was really challenging which was what I loved about it the most and that’s my favorite thing about math is that it’s always hard. Once you figure out one thing, there’s something else for you to try to figure out and so it’s always challenging, always interesting and there’s always something new to learn.
Greg Kaster:
Yeah, that’s great. And I meant to ask where you grew up and in Minneapolis. And you went to… was it South High School? Is that where you went?
Melissa Lynn:
Southwest.
Greg Kaster:
Southwest.
Melissa Lynn:
My brother went to South.
Greg Kaster:
Okay, oh sorry, Southwest. I don’t want to start a sibling feud here. Everything you said a minute ago about math, I think about this often, how math profs and history profs have this problem in common where the students think it’s about memorization, right? That’s true of history too. And of course it’s not. I always tell my students, “You can have every fact memorized, if you can’t tell me why something happened, how it happened, or how it might have happened differently, it’s not historical thinking.” So I love that, the idea of thinking and wrestling with puzzles and every time you sort of solve something or think you’ve solved something, a new question or puzzle arises. You were doing deep learning, it sounds to me, in second grade already. So from that program, you graduated high school, and then you go on to UCLA, what was it about that program that attracted you or was it the state of California or both?
Melissa Lynn:
So I went to undergraduate at the University of Chicago-
Greg Kaster:
Oh that’s right. We didn’t talk about that first. Yeah, thank you.
Melissa Lynn:
Yeah, yeah. So when I was looking at colleges, what really appealed to me about U Chicago is its reputation as a nerd school where they have all these mottos like, “Where fun goes to die,” and things along those lines. And so just the idea of going somewhere where everyone was really excited about learning. And it’s a really great math program. So I came into undergrad with the idea of I wanted to get a PhD in math and be a math professor, that’s what I had in mind that I wanted to do.
Greg Kaster:
Wow, almost from the start it sounds like.
Melissa Lynn:
Yep, yep. It was pretty early on.
Greg Kaster:
In that case you’re somewhat unusual. I was like you, I mean I knew I wanted to be a history professor or teacher from the start, late in high school even. But a lot of our colleagues in interviewing them or speaking with them, they start out in one place and wind up doing what they’re doing now, kind of surprisingly or unexpectedly so I’m thinking of Chaplain Siri Erickson at our college, for example, who was a chem major at Carleton and winds up becoming a chaplain. Not exactly a straight line. Anyway, so Chicago, yeah, great place. And I know some of those mottos, I have good friends whose daughter is attending. I grew up in the south suburbs, I grew up south of there in Park Forest. Did you have much time to enjoy Chicago at all while you were there or was it all sort of nose to the grindstone?
Melissa Lynn:
It was a lot of nose to the grindstone. That’s definitely something that I regret that I didn’t feel like I took advantage of being in Chicago as much as I could have. All though maybe it was good to be focused on school but I definitely remember going to Chinatown with friends and lots of late night meals that were lots of fun. Went downtown not super frequently but a few times a year. And yeah, it’s a great city. I’ve gone back a few more times to try to make up for that lack of Chicago experience and I love it.
Greg Kaster:
Yeah, same here. I really do love it. I love Minneapolis too and St. Paul, I really do. And I do love Chicago and generally I love cities, I guess, for the most part. But growing up my dad was Greek-American and so his parents had come from Greece but we were oriented toward Greektown which is still there in Halsted and in that area. And anyway, went to the city often usually to eat. Or occasionally for museums and theater and that kind of thing. But great school, University of Chicago. And so why UCLA, what was it about that program?
Melissa Lynn:
So from coming out of undergrad I knew that the area of math that I wanted to be focused on was Algebra which is different from the high school algebra. It’s much more abstract and I guess disconnected from reality all though it kind of loops back in cool ways. And so knowing that that’s what I wanted to do, I was kind of focused on programs that were good in that. And then from meeting with professors there, was excited about working with a professor that I didn’t end up working with but that was my decision there and certainly visiting there in March, didn’t mind the weather.
Greg Kaster:
Yeah, my brother, he’s lived in Los Angeles for a long, long time. A lot of people are down on LA, I don’t know if I’d want to live there but I love visiting. I had a great time there and I enjoy the UCLA campus quite a bit. You just mentioned, it’s funny, about graduate school and mentors. The same thing happened to me, I went to Boston University to work with, I thought, a particular professor and then wound up working with someone else that I didn’t even really know about. And, again, by choice. But I was going to ask you, both at University of Chicago and UCLA, were there particular professors who had… whether they were math professors or not, had a particularly important impact on your learning and career?
Melissa Lynn:
Yeah. So I would say that in general I’ve been really lucky with the professors that I had and had some really awesome ones. I would mention Peter May, one at Chicago, that had a tremendous impact on me. He helped me think about grad school with the area that I was interested in and was really a great mentor to me at U Chicago. At UCLA, I would certainly say that my advisor, Christian [Hazelmyer 00:14:23], he’s no longer at UCLA but while he was there. So he was my eventual advisor and he was really great and supportive. So I’m very grateful for the professors that I’ve had along the way and that has inspired me in my teaching as well.
Greg Kaster:
Yeah. I mean, I agree. When I first came to Gustavus as a professor, I really hadn’t thought about mentoring that much and how important it is to the job, especially at a liberal arts college, but anywhere. I just hadn’t given it a whole lot of thought but then thinking about the mentoring that I had had, like you, I mean some great mentoring both at the undergraduate level and in high school as well. In fact, the other night I was on a Zoom with a bunch of people from my high school, different class years with one of our common history teachers at the high school, he was from Minnesota, from Minneapolis actually, who’s 90 years old and still going strong and has inspired so many [inaudible 00:15:23] and really an important part of teaching that I think sometimes is overlooked or underappreciated.
The other thing I wanted to ask you is a backtrack a little bit to you’re back at Chicago, was it a liberal arts kind of curriculum you were doing? Was it mostly STEM? What was it like, you mix of courses?
Melissa Lynn:
Yeah. So it’s a liberal arts undergrad even though it’s in a school that’s more graduate students than undergrads and so that’s something that I think that I didn’t really totally understand until I went to UCLA and saw that the undergrads there weren’t doing this. So I loved the courses that I took. I took sociology classes, history classes, art and music classes, linguistics, all of this different, cool stuff that I did in addition to packing in lots and lots of math classes. And so I think back to those a lot and kind of think about things I learned or things I took away from them. So there’s a lot of value in those courses that weren’t my major.
Greg Kaster:
Yeah. I mean, gosh, the liberal arts, right? Say, I still remember taking some of these like an art… I can remember some art historians whose courses I loved from my undergrad. Just two courses I never followed up on but still have an impact on me. That sounds like an ideal program. So we’re going to take a deep breath and then dive into your dissertation. So everyone out there who’s listening who suffers from math phobia or anxiety, just breathe. Here we go. So I know what each of those words means individually and it’s sums-of-squares formulas. As best you can for those of us who have no idea what that means, it sounds interesting I have to say, what is it? What does it involve?
Melissa Lynn:
So it’s really about looking at different classes of numbers. So at a more basic level we have our whole numbers, we’ve got zero, one, two, three, our counting numbers there. And then we can add on to that group by adding in the negative numbers so minus one, minus two and so on there. So that can give us one group of numbers we call the integers. Then next up if we throw fractions into the mix that gives us more numbers on top of that and so that’s what we would call the rational numbers. After that, you can add in decimal numbers. So once you’ve added in decimal numbers, we call that the real numbers and so then you can think about it as being the line completely filled in, once we’ve got all those decimal numbers.
Then when you go on from the real numbers, the next step up is usually adding in the special number i which is the square root of minus one and so there we get into imaginary numbers or complex numbers. And so what I’m looking at with sums-of-squares formulas is essentially number systems that have the same kind of properties as complex numbers. But even more so, so the first example would be if we add in instead of just I, throw in a J and a K there. And so the joke that I like to make is that it’s complexer numbers. And so I studied those and kind of looking at when they exist and trying to figure out how we can find those types of number systems.
Greg Kaster:
I read a tiny bit about imaginary and complex numbers and I find it fascinating, I even struggle to get my head around it but so, I mean, an imaginary number, can you say a little bit more about that? How can that be, right? Numbers are numbers, right?
Melissa Lynn:
Yeah. So it ends up being important when you’re looking at things like ways things can be configured that it turns out that looking at the real numbers isn’t really enough to describe what’s going there. And so by adding in this square root of minus one which is sort of like a theoretical thing. It’s not sort of grounded in like we can count things out with some [crosstalk 00:19:50] portion of an item but that that can describe kind of how things are positioned more in three dimensions or how we can rotate things around. And so it they’re imaginary but they’re also very grounded in reality that these things come up in physics and electrical engineering and are important for reasons other than just the things us mathematicians care about.
Greg Kaster:
Okay. Well you’re kind of touching on what I wanted to ask next which is what are the practical applications or implications of this research?
Melissa Lynn:
Yeah. So a lot of it is in the realm of pure math research where it’s just like, “Hey, this thing exists, let’s study it, let’s find out more of it.” And this kind of approach to mathematics is something where people have studied these things and all of a sudden they have these weird applications that you never would have thought of 100 years ago but they end up being really important there. So things like cryptology that is really important for how computers work and how we can send emails securely and things like that are coming out of number theory which is just studying numbers and so it’s sort of in the realm of that where it’s pure math, maybe it will eventually be useful, maybe not. But we think it’s interesting to study. Some of the past results from sums-of-squares formulas have had to do with what I mentioned with physics and how configurations of molecules, what’s possible and what’s impossible. So some of the past work has been applied to that.
Greg Kaster:
Fascinating stuff. And also the idea of… I’m trying to think what’s the equivalent in history, pure history? Pure mathematics and just sort of the fun of it, the challenge of it but also the fun of it. And I noticed in preparing for our conversation you just mentioned cryptology and that you… I don’t know if this is a course you taught on secrets or something, but that caught my eye. So it’s kind of neat to hear about how this relates to that. Let’s talk more about your teaching. I know it’s mostly been online thus far, I guess except for your first semester. At least your first semester was in person at Gustavus.
Melissa Lynn:
Yeah, that was a good one.
Greg Kaster:
Yeah, that’s the way to do it, right? At least have that start. You teach gen ed, right? I assume you’re teaching at least some courses where most of the students aren’t math majors, is that accurate?
Melissa Lynn:
So I’m actually teaching all computer science now which is a whole nother story.
Greg Kaster:
That’s a whole [inaudible 00:22:38].
Melissa Lynn:
Yeah. So computer science one is, actually, this year is the first time it’s counting as a gen ed requirement. So it’s meeting the requirement now but previously was not a gen ed course. But we do get people who want to be CS majors and even before it became a quant course I got lots of students from other majors who recognized the importance of computers and so want to learn some programming in order to have that experience and then of course it’s really useful, directly, for lots of fields like physics, math, statistics. And so it’s a good mix of majors and non majors there.
Greg Kaster:
Well if you’re teaching, let’s imagine you’re teaching a gen ed quantitative… meets the quantitative requirement gen ed mathematics course, what are your strategies as a professor to make sure I’m not terrified or to bring the students along?
Melissa Lynn:
Yeah. So I think one thing about computer science that’s really nice is that when we’re looking at the CS1 course, there’s no real background required for that, you come in with no previous programming experience and you’re going to learn everything you need to know in the course. And so that’s one way where it’s a little bit distinct from something like calculus where you’ve got quite a bit of background that you need to be solid in before getting into calculus and that can be an issue for students. But I think it’s really fun with computer science that’s sort of this clean slate and when we talk about the math pieces that we have to do as part of the course, I like to tell students, “You don’t need to do any arithmetic, we’re just going to tell the computer to do it.” And so figuring out how to write the program to get the computer to do the math for you, that’s pretty much and saves you from computations that you might not want to do.
Greg Kaster:
Right. How did you get into comp sci? Is that just a sort of natural outgrowth of what you were doing in graduate school?
Melissa Lynn:
Not quite. So what I was doing in graduate school was very much pure math, very disconnected from reality and I would say that actually my sums-of-squares formulas was actually a move towards reality from what I was originally trying to do which was category theory. Which is a much more abstract type math. But the way that kind of came about was halfway through grad school I had a little bit of an identity crisis where I had come into grad school thinking I wanted to be a research professor. So I was thinking I want a job at an R1 school, being really focused on research, and I wasn’t really thinking about more teaching focused professor positions. But then a few years into grad school I realized that I just didn’t like spending all of my time on research, that I wasn’t enjoying it that much.
And so then kind of thinking about the experience I had, I didn’t really think I was super employable at that point because all of my experience was in pure math and so I wasn’t sure what I was going to do. And so I started taking programming courses at that point to try to eventually get a job in industry. A lot of my colleagues from UCLA have gone on to work at Google or Facebook or the big tech companies. And so that’s kind of what I had in mind as what I was going to do. Then once I continued on there, I learned more programming and kind of as I was shifting my focus away from research, I was noticing that I really liked the teaching part of my job because I was working as a TA.
And so then I also found that as I was focusing less on research and more on teaching and learning programming that I was really enjoying my research more as well. So I realized that, “Hey, maybe I don’t want to be in a research focused position but if I’m in a teaching focused position where I’m also doing research, I’ll still hae the freedom to learn about new things that I am interested in. That could be really great for me. So I kind of shifted my direction there and did the teaching postdoc at the University of Minnesota. And that was still in math. And then gradually more and more computer science things fell into my lap where I had a lot of experience teaching in summer camps and someone at the University of Minnesota wanted to start up a machine learning summer camp.
And so I was brought in not as a machine learning person but just as someone who had experience working with high school students. And so I helped develop the programming curriculum that they needed for that and then as part of that I started learning the machine learning part of the camp and contributed more and more to that. So this past summer was our third year of that camp as an online camp this year. So that’s been a really cool experience.
Greg Kaster:
That’s just great. It comes through in your CV, your resume, how much you care about teaching and education, your work with middle school kids, with high school kids. You’ve written curriculum, you’ve done some work on… are they textbooks, essentially? Is that what you’re also working on?
Melissa Lynn:
Yeah. So that’s another programming project that fell into my lap there, is working on online, interactive open sourced textbooks. So that’s a big passion of mine because we’ve got these paper textbooks which a lot of the times students kind of buy it at the beginning of the semester, spend 100 or 200 dollars on it. And then just use it to look up their problems or they just put it on their bookshelf and never look at it again. And so this idea that, first of all, maybe they don’t need to be paying that hidden cost of classes to have to buy that textbook and then also how can we make it so that textbooks are leveraging the technology that we have? And so kind of work on having these textbooks be online, be free for students, and then also have interaction where they can get feedback as they’re learning from the textbook. I think it’s a really, really cool project and it’s something that I’m really excited about.
Greg Kaster:
Yeah, and the key thing there, obviously, is interactive not just that it’s online but interaction. And, I mean, we historians are trying to do something similar. I don’t know about the interactive online part but certainly it’s much more about, I would say, the process, the work of historical thinking. I call it we’re going to have a workout, we’re going to work out, that’s the language I use, hoping to tap into some of their love of athletics among our students. Think of me as you would your coach.
But, seriously, it’s not just about coverage, right? It’s about the doing, the thinking. Man, I think for us it’s the interacting with primary sources and other things as well. But yeah, you are an educator, you’re clearly not just a mathematician. And I want to come back to… I think you might have already answered this but just to clarify it for me and our listeners, so what is machine learning? You teach a course in machine learning and then you teach a course in analysis and design of algorithms. Are those related, what are they about?
Melissa Lynn:
Yeah. So I would say that the algorithms course is sort of the kind of core standard algorithms course that every computer science major needs to take. So when we’re talking about algorithms. An algorithm is a set of instructions that you follow to do something and so when we’re writing a program, essentially what we’re doing is writing the steps for an algorithm that a computer can follow. And so designing algorithms is a huge part of computer science so that we can figure out what are the instructions we need to give to a computer so that it can do the stuff that we want it to do. And so machine learning algorithms are a particular kind of algorithm. And so this gets into the realm of artificial intelligence and this cool stuff there.
So the way machine learning algorithms typically work is that what the algorithm is doing is learning some sort of input that you give it. So often there’s a large dataset so an example that I’ve had students work with is fatal police shootings data as an extreme example. Or we can look at Pokemon, all the statistics for Pokemon. So then the machine learning algorithm essentially processes that data a bunch of times and kind of builds what we call a model. And so then that model can kind of take the data and make some sort of classification based on the data or some sort of prediction.
So one of the standard examples is looking at measurements for iris flowers and so once we train a machine learning algorithm on the measurements of these iris flowers, the model that it produces tells us what species the flower is. So the idea is that this algorithm is kind of doing the learning and so that it’s kind of building this model without us having to directly program it telling it what the model should be doing.
Greg Kaster:
I need you to come speak to the irises that we just planted in our community garden. I hope they show up next year. So a couple of things and I assume machine learning in your answer had something to do with artificial intelligence. So do machines learn the way human beings learn or not?
Melissa Lynn:
So I would say that it depends but basically no. So there are some machine learning algorithms that are modeled after the human brain so these are called neural networks. And are a really hot, really commonly used machine learning algorithms. But the way that they work is they’re built up of this big network of artificial neurons so when we’re thinking about the human brain, it’s got these neurons that take in electrochemical input and then if they get enough input, they fire and so then send their signal onto other neurons. And once you’ve put that all together, you get the human brain and all of the thinking that we’re able to do.
So when we’re looking at the artificial neurons, those are essentially just functions where if they get a large enough number as input, then they produce an output that can be sent on to artificial neurons. So it’s really remarkably similar to what the actual physical neurons in our brains are doing. And so then once you put all of these artificial neurons together, you get a neural network that can then learn in a kind of similar way to how the human brain learns where the human brain gets feedback based off of what you do. You eat something delicious and you get hormones or… I’m going to get out of my depth here, but [crosstalk 00:34:35]-
Greg Kaster:
It feels good-
Melissa Lynn:
Get that positive feedback that it’s good. And so kind of similarly when we’re looking at an artificial neural network, if it makes a mistake it kind of passes blame backwards and so adjusts its signal there. And so it’s kind of similar to how the human brain is working. That said, the actual results are not anywhere comparable to a human brain. Even just looking at number of neurons, I think I’ve heard that the largest, most powerful neural networks in existence right now have the same number of neurons as a worm or a bumblebee. Those are the ones I have heard and so just plain in number of neurons, we’re way ahead there.
And as humans, we spend years and years and years taking in input and learning from things around us, even when we’re just looking around, not doing much, we’re taking in inputs. So we’ve spent decades training whereas most people aren’t going to sit around for decades waiting for their neural network to train.
Greg Kaster:
I’m feeling better as a human being. Maybe even slightly superior. I think the common thought is, “Oh, these machines are so brilliant.” And of course they are amazing. I don’t know how much you’ve thought about this, and if not at all that’s fine too, but is artificial something we should be worrying about, as some do?
Melissa Lynn:
So I would say that the worry is people using artificial intelligence to do bad things. It’s not that we need to worry about a robot uprising or anything like that. It’s the people using it that are going to be the problem. So one of the common examples that comes up here is algorithms that discriminate. So one of the big tech companies, I’m not going to say which because I can’t remember which one and I don’t want to say the wrong one, but was trying to train an application filtering model-
Greg Kaster:
Oh yeah, I’ve read about this. Yes, for people interviewing or something?
Melissa Lynn:
Yes. And so the issue here is that it’s training off of what they’ve done for past application filtering and so essentially even if they leave… gender was the issue here, that even if they leave gender out of it, the algorithm can find shortcuts and recognize that playing women’s tennis is an indication that someone is a woman. And so the algorithm, the model that the machine learning algorithm builds can discriminate based off of the past decisions that people have made that were discriminatory. And so without realizing what it’s doing because it doesn’t have that ability, you train a model that is discriminatory. And so if you don’t realize that and just apply it, there are big problems there.
Greg Kaster:
Yeah. It’s both a little creepy and fascinating and as you’re speaking I’m sort of imagining my department, maybe you too, but my department’s in the middle of a tenure track search and I’m just trying to imagine using such algorithms to make the hire to a liberal arts college. But this is also, I mean, just a perfect segue to where I wanted to go next which is to talk about your really clearly strong commitment to equity and diversity. And some people might think, maybe some people even in your field too, “I’m a mathematician or I’m this, what does that have to do with me or my discipline?” But you really have a strong commitment to it. You’ve participated in workshops, I think, around this, dialogues and service at the University of Minnesota. Was it in October when you were a part of this panel of women faculty at Gustavus in STEM fields? Was that this past-
Melissa Lynn:
Yes.
Greg Kaster:
Yeah. And so I’m going to ask a lot, which is to reflect a bit on where that commitment of yours comes from, what it’s about, and tell us a little bit also what that panel was about and what you shared as a member of that panel?
Melissa Lynn:
Yeah. So I think that this is a big problem in science in general and I think especially in math, mathematicians have a tendency to view themselves kind of above the issues of society where mathematicians are pure and logical. So obviously we’re just above any issues of discrimination because we’re just about the math. But the trouble with that is that math has developed through history and when you have people involved, you can’t take it outside of that context of society and so we might like to think we’re above all of that but in reality, we’re just not. There’s still culture and there’s still society that’s interacting with the field there.
So I would say that thinking back to when I was growing up, one thing that I would hear all of the time was, “You’re so good at math for a girl.” And it came a little bit of a joke in my mind where, “Oh, that’s another one. Oh, that’s another one.” But it’s irritating and I just want to tell people… and it’s good intentions. People are trying to be nice, trying to be supportive. I just want to say, “I’m so good at math, period. You can stop there.” So that was something that kind of dug at me a little bit growing up but I would say that from starting in middle school and high school and kind of in to college, I definitely took the attitude that I thought the issues of discrimination or gender imbalance in math, I thought that that was a thing of the past and that people made too much of a big deal of it.
And so I think it was, as I started to feel the weight of the experiences that I had, that my opinion shifted there and my perspective really changed. So in undergrad, the experiences that stand out there for me, coming in I was really, really excited about being at the University of Chicago. I was getting to hang out with all these new friends who were so excited about math and excited to talk about this stuff and so that was really cool but I was hired by a professor to do some work. And there’s a pretty clear pattern in how he hired students, he hired a lot of students and he would hire female students to do administrative or secretary work and he would hire male students to help with him research projects or book writing.
And so I was hired on the secretary/administrative side and so it was frustrating to be working for him and some parts of that were good experience that I got good experience working with some programs. But also seeing students who were younger than me working on projects that I would have been really excited to work on and I was certainly qualified to work on but wasn’t the opportunity that I was given.
Greg Kaster:
And this isn’t the 1950s. I mean, this is not that long ago, right? What-
Melissa Lynn:
Yeah, so I graduated high school in 2007. So yeah, this is-
Greg Kaster:
It’s amazing.
Melissa Lynn:
Not that long ago.
Greg Kaster:
Right. And then did you endure, suffer, similar experiences at UCLA?
Melissa Lynn:
So at UCLA, I’d say that kind of starting out, I again had the enthusiasm like I get to hang out with all these other math people, talk about math, this is cool. There I started to notice like having to prove myself all the time where other students asking each other questions about things like homework and things like that, realizing that I wasn’t the one they were asking even if I had more expertise in that. Other students were getting the presumption that they knew or mastered that content while I felt like I was having to prove myself constantly and show that I had that knowledge.
Also about halfway through grad school, I did have some incidents of sexual harassment. There were also a couple from undergrad. And so I would say it was sort of a lot of things that really built up and it’s the thousand cuts thing where if you take one of these things in isolation, it wouldn’t have been that big of a thing but it just built up so much over time that it eroded my confidence. And that was a big part in my middle of grad school identity crisis where I ended up shifting tracks there, that that played into that quite a bit.
Greg Kaster:
Yeah. I think about it’s analogous to what black scholars have talked about as well, what they feel and experience. But that erosion of confidence, right? It’s just awful. You’re also reminding me, I was in graduate school, this is different, I’m a guy but I’m in a woman professor’s office and I thought she… I remember this so vividly and being kind of devastated by it, I felt suddenly she was flirting with me. I don’t know if it was true or not but just feeling that and thinking, “Oh, she’s not as interested in my ideas as I thought she might have been.” That’s the only time I felt something akin to, it’s not the same obviously, but akin to what you’ve been through and other people as well. You were on that panel and I guess preceding the panel is the movie, which I haven’t seen, I want to see… is it called Picture a Scientist? Is that what it’s called, a documentary?
Melissa Lynn:
Yes.
Greg Kaster:
Yeah. Tell us a little bit about that, I’ve read about it just in preparation for our conversation. But it just sounds like a fantastic movie.
Melissa Lynn:
Yeah. So it was a bunch of women scientists talking about their experiences as women scientists, the experiences with sexual harassment or discrimination that they’ve had and how that affected them. So that was such a powerful movie and I think watching it myself and what I heard from the other panelists, so much of that resonates with us and I think that often when we have those experiences, it’s really easy as yourself to think, “Oh, it’s not that big of a deal. It’s just me. I’m making too much of this or I brought this upon myself in some ways.” But so many people have these experiences and when you hear someone else talking about their experience, they say, “I thought it was my fault.” And you’re thinking, “This is absurd. This was terrible. That was not your fault.” And so I think it’s really helpful for people to share those experiences for upcoming women scientists or people from other minority groups in science to be able to reflect on their own experiences and recognize when things that happen aren’t okay.
Greg Kaster:
Right.
Melissa Lynn:
And know that it’s not their fault.
Greg Kaster:
That’s right. A couple of things, one, that there’s a systemic problem. President Trump aside, there is systemic racism, there is systemic sexism, there’s no question about that. And one realizes that, right, when you start talking to other people and find you’re sharing a common, really bad experience. This reminds me, the second point is, Betty Friedan’s Feminine Mystique, the problem that has no name, right? Where women are realizing, “Oh, it’s not just me. I don’t need to adjust,” right?
Melissa Lynn:
Yes.
Greg Kaster:
The problem isn’t mine, it’s a social problem, a systemic problem. And that’s, I think, a powerful… it doesn’t solve the problem, right? But that’s a powerful step toward addressing the problem.
Melissa Lynn:
Yeah. And I [crosstalk 00:48:04]-
Greg Kaster:
Go ahead.
Melissa Lynn:
Think that having people to talk to about it, when I was going through this in grad school, I talked with some of my fellow grad students about it and being able to talk to male students and have them say, “Whoa, that’s really messed up, that’s not okay. You should report that,” having that confirmation and being able to talk to someone and get some perspective on it, that’s really helpful.
Greg Kaster:
Yeah. And I have to say, I came to Gustavus in ’86, so 34th year or something, you do the math I should say, but I just have such vivid memories of there being… there were certainly women and respected women, tenured women, full professor, but far fewer women on the faculty at the time. This is 1986, coming from Boston University. And both Kate and I were sort of stunned by that. We loved Gustavus, we enjoyed being there, but Kate, for example, my wife Kate was the only woman at that point in the history department. I don’t know in the history of the department but at that point, we’re all guys and one woman. And just having more women on the faculty begins to change the culture a bit, I think in positive ways for the faculty and for students alike.
I wanted to give you a chance to talk about your impressions of the place, Gustavus, and the students and recognize, again, that you only had one semester in person. But thoughts about that, what strikes you about Gustavus? And I confess, I think I said before in some of these episodes, I had not heard of Gustavus growing up in the ‘burbs of Chicago, I learned a bit when I saw an ad for it at a big history convention in New York City. And I asked my advisor who began raving about the place and some would say the most famous living civil war historian is James M. McPherson who is a Gustavus graduate. And so when I heard that, I thought, “Oh, okay, yeah.” But just thoughts about the students, about Gustavus thus far?
Melissa Lynn:
Yeah, so I think that I was aware of Gustavus growing up but I was sort of aware of it in a way that I was aware of all of the colleges and didn’t really understand any distinctions or anything like that. And then coming through grad school and my postdoc, I had this concept that I wanted to be at a liberal arts school with small classes and have a lot of interaction with students and so I remember on my interview, so I had driven down from Minneapolis and was driving back after the interview and just thinking like, “Oh, I don’t want to leave,” it just felt like home. And just having that community within the department and within the school and interacting with students in these small classes where I think it’s so cool that as faculty we’re able to devote so much time to helping one student.
I think that’s really cool and really powerful and a great way to make an impact in people’s lives. And so that interaction, I think, is just wonderful. And it made me feel like event after a kind of stressful interview that I just wanted to stay here and I didn’t want to leave.
Greg Kaster:
Yeah, it’s so funny, I mean, the historian in me, the concept of community, the practice of community can be/has been oppressive. So I just think south Boston, for example, keep out black people, right? In the name of community. But I have to say I had the exact same reaction and I interviewed at least one other college… anyway, one other college on campus I interviewed, another liberal arts college not in Minnesota. And I just remember so powerfully feeling… I don’t know how to put it. Just feeling positive vibes about the place, just the way the faculty were interacting, I was in the guest house and the then associate dean Tom [Emhert 00:52:16] who’s a dear friend, history prof for a long time, now emeritus, he was hosting a party with wine and cheese for faculty. I thought, “Oh, this seems great to me.”
It sounds like we’re making it up where it’s a cliché and anyone who’s gone to Gustavus and fallen in love with it says the same thing but there is truth to it. To backtrack a bit before we conclude, I meant to ask you about the movie Hidden Figures when we were talking about women. Have you seen that and thoughts about that?
Melissa Lynn:
Yeah. So I’ve seen it a couple of times where we’ve hosted movie viewing parties for math students. Not at Gustavus but previous ones. Maybe we should have one at Gustavus. And so I think that that is a great movie. I would say looking at compared to Life of a Scientist, it’s certainly a gentler exposure to that type of discrimination. But it’s a great movie and those are incredible women that haven’t gotten as much credit as they should.
Greg Kaster:
Yeah, I agree. I loved the movie, I’ve just seen it once and a friend, a colleague, at Macalester, Duchess Harris, who’s written young adult books, I think it was her grandmother or great grandma, again I can’t remember, was a hidden figure. And she’s written about that. As we conclude here, I’d like to give the chance, I try to give everybody I interview, at least faculty colleagues, to make your pitch for your discipline. That is to make the pitch for the math major at Gustavus, what would you say?
Melissa Lynn:
So I think I have to make the pitch for the computer science major but the math major is great as well. I think that it’s so fun working on things and solving a problem. And I think with computer science and programming, when working on trying to figure something out there’s this process where you’re so engrossed in it and at the same time, it’s frustrating but also exciting and then once you finally figure it out, it’s just this feeling of like, “I got it,” that’s just exhilarating. And to me that’s what both math and computer science are all about is kind of chasing that feeling and struggling with things and then having them click. So I would say embrace the struggle, enjoy the struggle, it’s a lot of fun.
Greg Kaster:
Oh, I like the embrace the struggle. That should be our tagline and what you just said is really… I mean, I think for anyone who thinks about learning and loves learning, that’s learning, right? To embrace the struggle whether it’s in math or history or geography or physics, health and exercise, it doesn’t matter. Whatever you’re doing, embrace it. And you said it well, exhilaration, that’s just such a great feeling. And that’s frankly, a little bit of the feeling I always at the end of these podcasts, I feel so inspired and excited. Not sure I’m going to go do calculus tonight but I do feel inspired and excited about math. I can hear it in your voice and how you describe it. So thank you so much for doing this and hope we are all back in person soon. And I hope I get to meet you finally in person. Maybe when you come up to… what were you near in south Minneapolis where you grew up, where were you, what were you close to?
Melissa Lynn:
I was near Lake Harriet.
Greg Kaster:
Oh sure, yeah, okay. As I was telling you, I think before we started recording, Kate and I are right downtown at Grand Park. So we’ll have to get together for coffee or lunch or something.
Melissa Lynn:
Sounds good.
Greg Kaster:
But take good care. That does sound good, take good care and thanks so much. It’s been fun.
Melissa Lynn:
Yep, thank you for having me.
Greg Kaster:
You’re welcome. Take care, bye-bye.
Melissa Lynn:
Bye.
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Media Contact: Director of Media Relations and Internal Communication Luc Hatlestad
luch@gustavus.edu
507-933-7510