## Episode Transcript

My guest today, Ken Ono, is a mathematics professor at the University of Virginia. But what makes him unusual is how he applies math outside of academia. His math knowledge got him the job of associate producer for a major Hollywood motion picture. And more recently, he’s been helping swimmers to make the Olympic podium.

ONO: How do you optimally streamline your body so that the force you generate propels you through the water as efficiently as possible? That’s a math problem. And it’s a math problem with many different parts.

Welcome to *People I (Mostly) Admire*, with Steve Levitt.

On a day-to-day basis, Ken studies highly abstract mathematical topics you’ve probably never heard of, like integer partitions and modular forms. I want to start the conversation by seeing if he can explain some of his crazy theorems in an accessible way. I’m also curious how someone who thinks in such an abstract way defines math.

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LEVITT: So Ken, one result that you’ve proven — and to an outsider, I have to say, it’s a strange result — you’ve proven that every odd number greater than 2,719 can be written in the form x squared plus y squared, plus 10z squared, for some integers x, y, z.

ONO: It is a very weird theorem. It actually is a theorem that is dependent on what’s called the Riemann hypothesis, which is one of the million-dollar Clay Millennium Prize Problems. It’s a polynomial in X, Y, Z. You know how to plug in numbers for X, Y’s and Z’s, and you can get larger and larger numbers. What our theorem says, if for any reason you are unable to find an expression of a number, any odd number bigger than 2,719, then the Riemann Hypothesis would be false. And you’d get a million dollars. It’s a big thing. So, as strange as that equation may sound, one of the reasons we proved that theorem was to make this very deep, difficult problem in mathematics into a statement that’s a little bit more tangible.

LEVITT: Now, did you start tackling this question because you were looking for the million bucks and you were hoping to find the opposite result?

ONO: Oh, no, not at all. I’m still looking for that million bucks. I am a big fan of an Indian mathematician by the name of Srinivasa Ramanujan, who lived over a hundred years ago. And this mathematician was an autodidact. He was untrained. He was an amateur. But Ramanujan discovered this crazy equation, x squared plus y squared plus 10z squared. And he thought that there should be a special rule that determines what odd numbers you can get and those that you cannot get by just plugging in for x, y, and z. So that was a mystery. And I’m delighted that in the work that we did, we figured out what that rule is. And it happens to be the Riemann hypothesis. There are only four or five problems that I think are this level of difficulty in mathematics, and what Ramanujan discovered is that the simple equation sort of embodies, magically, invisibly, the guts of the problem.

LEVITT: So I don’t know much about math, but I think your description there of Ramanujan — I’m probably not saying his name right, but—

ONO: No, that’s fine. That’s good.

LEVITT: You totally sold him short! He is the most amazing phenomenon intellectually that we’ve maybe ever observed. Wouldn’t you say? I mean, it doesn’t even make any sense — this guy had no formal education and he would write down these theorems — hundreds, thousands of theorems. He didn’t give proofs though, right? Which was what was so odd about it because every mathematician always writes theorem and proof. But I mean, I’ve heard it said that you could stack up him against the entire knowledge of Europe in math and it’s a coin toss.

ONO: Right, the accumulated wisdom of the best mathematicians of his day — maybe that pales in comparison to Ramanujan. He believed, growing up in South India, that his Hindu goddess by the name of Namagiri would give him mathematical formulas in his sleep, and he recorded these formulas in his notebooks. These notebooks survive to this day. And a lot of the work that I’ve done is inspired by trying to figure out the scrawl in these notebooks. In fact, we made a film about him calledThe Man Who Knew Infinitythat starred Dev Patel and Jeremy Irons.

LEVITT: So you, as a modern mathematician, you were spending time going through these old notebooks, and he’s got some equation written down there with some note saying, “This seems like it should be relevant.”

ONO: That’s exactly how he says it.

LEVITT: And you took this and eventually proved this theorem that really matters to mathematicians.

ONO: Right. I proved it with Soundararajan. He’s a very famous mathematician at Stanford. It’s not just me. It’s work that we did together almost 30 years ago.

LEVITT: Let me ask you about another theorem you’ve tackled.

ONO: Okay.

LEVITT: It comes with quite an unusual name. It’s known as the Umbral Moonshine theorem.

ONO: Oh, yeah. Umbral Moonshine, yeah.

LEVITT: Does moonshine in this context refer to homemade alcohol, light reflected off the moon, or something completely different?

ONO: So in a poetic way, yes and yes, but strictly speaking, no and no. The word moonshine historically has meant, “You’re talking nonsense.” So the umbral moonshine conjecture is this idea that certain symmetries in 24-dimensional space, as measured by what are called characters could be reformulated in terms of objects called modular forms and modular functions. So the moonshine is the mere thought that there are these functions that control objects in mathematics that at first glance should be unrelated. The moonshine — that term was used earlier in this area of mathematics in connection with the group called “the monster.” It’s the largest of what are called the finite simple groups. Its discovery was probably one of the four or five most important achievements in the latter half of the 20th century. And people discovered that this monster group could also be tamed if you could find modular functions. So our goal — and Jeff Harvey at Chicago, and his collaborator Miranda Cheng, and John Duncan, they theorized that a similar phenomenon could be true. But how do you find the functions? Their guess was that we had seen glimpses of these functions, which are kind of like shadows of the function. So they theorized that the functions that we needed to find, their shadows could be viewed in this world that’s called harmonic Maass forms. So our job was to not just work with the shadows; literally find the functions that cast those shadows. So hence the umbral moonshine conjecture — the shadow of moonshine.

LEVITT: As I thought ahead of time about our conversation and the likelihood that you would be able to describe your math theorems in simple terms that non-mathematicians could understand, I guessed there was only a 10 or 20 percent chance you’d be able to do it. Am I right in thinking that your area of math more or less defies simple explanation?

ONO: Oh no, no, no, no. Are you saying that I failed?

LEVITT: I’m saying you said a lot of words that sounded great, like monster and moonshine, but I have no idea what you’re talking about. It’s not easy stuff what you’re doing.

ONO: All right, Steve. That’s two strikes against me. Can I have a third chance? I don’t want to strike out here.

LEVITT: Okay.

ONO: Okay, I have a paper coming out with colleagues in theProceedings of the National Academy of Sciences. And our theorem is very simple. It’s about prime numbers. A number is prime, like 5, if its only divisors are 1 and itself, right? Five is only divisible by 1 and 5. A number like 6 isn’t prime because it’s divisible by 1 and 6, but also 2 and 3. And one of the fundamental problems in mathematics — which has significant implications to the modern world that’s awash in data, cybersecurity, internet security — is how does one detect whether a given number, like a 20-digit number, is or is not prime without going through the steps of trying to factor it by division. So this theorem, we give infinitely many new conditions that automatically list the prime numbers in order without you ever having to stop and factor them one at a time. Did I strike out?

LEVITT: No, no.

ONO: Oh, good, okay.

LEVITT: Is that a threat to — the kind of data encryption we use these days is called RSA, and that all has to do with the difficulty of factoring primes.

ONO: You are right, this is in the right kind of mathematics, but no, I’m not taking that apart. All of these algorithms begin with a process by which some entity has to start picking large numbers at random that have to be prime, which then have properties that anyone trying to break a code can’t figure out because they don’t know which prime numbers we started with. And theoretically, the theorem I just described detects and lists them in order and plays a role in that little step.

LEVITT: In my lifetime, the biggest moment for math was // when Andrew Wiles proved Fermat’s Last Theorem. That was in the 1990s. And It was all over the front page headlines of major newspapers. If I’m not mistaken, I think he used a lot of the same techniques in his proof that you use in your own math. Is that right?

ONO: So let’s rewind the clock a little bit. Fermat’s last theorem actually was never his theorem. He wrote, in the margin of his book,Disquisitiones Arithmeticae. he wrote that a sum of two nth powers can never be an nth power again if the exponent n is bigger than or equal to 3 without one of the numbers being zero. So that flies in the face of the Pythagorean theorem, where you know that a squared plus b squared equals c squared. There is no a cubed plus b cubed equals c cubed — unless it’s true for a dumb reason, like b is zero or something like that. And for the longest time, for over 300 years, mathematicians have been mesmerized by that bold claim. That little remark that he made was a challenge to the mathematicians of his future. The Andrew Wiles announcement came as a major surprise. I was in my last year of graduate school at the time, and I didn’t have a lot of confidence as a near Ph.D. That’s honestly the understatement of the universe. I just hoped three or four people in the world would care about the thesis I was writing on these objects called modular forms and elliptic curves. I applied for maybe 200 jobs, I wasn’t getting any jobs, and I was about to work for a community college thinking that I had to make a living. And then on June 23rd, 1993 — I’ll never forget this day, it was my third wedding anniversary — I got out of the shower with a towel around my waist, I logged on to the computer, I opened my email, and there must have been 20 or 30 forwarded messages with the same subject line: “Andrew Wiles proves Fermat’s Last Theorem.” I couldn’t believe it. And as I read it, it said, “He uses modular forms and Galois representations,” and my life almost changed overnight. I went from hoping that four people in the world would care about the subject to, oh my god, the whole universe in mathematics is now going to care about this subject, So talk about luck.

LEVITT: I could imagine it could go either way, right? You’re working in this mathematical area, and Fermat’s Last Theorem gets solved, and you could imagine everyone saying, “Oh, that’s solved. Let’s pack up our suitcase and go look for the next hard project.” But it sounds like just the opposite happened — that because he used techniques that people hadn’t expected to be the way into this proof that suddenly that area of mathematics got elevated, and the hope was you could use it for all sorts of things.

ONO: Oh, yeah. People who had been thinking about problems like Fermat’s last theorem, they might as well have been working in the era before electricity. So the tools that were now developed by Wiles and so many others, we’ve now got full power computers compared to the prehistory, which was just the early 1990s, where the ideas were just labor with nail, hammer, and chisel. We’ve got computers now. That’s how dramatic these new ideas are.

LEVITT: Now, I’m curious. Most non-mathematicians, when they think of what math is, they have in mind answering questions like 2x squared plus 3x equals fourteen. So solve for x. But I suspect to you, math is something very different. Could you talk about what math is?

ONO: Yeah, I don’t want people to think that mathematicians think about just solving equations that remind them of sets from high school or maybe a calculus problem set from their years in college. That’s not what mathematics is. It’s really the language, I think, of science and engineering and medicine. Mathematics is the language of measurement. We count things. We approximate phenomena in the real world using mathematical equations. And so the tools that we devise to study them, namely the mathematics, or now the data science or the statistics, they’re relevant. My father was a mathematician. He desperately wanted me to grow up to be in his image. And in many ways, maybe I did. I’m a number theorist. I prove theorems about subjects that he would have thought about, perhaps, in his working days. But make no mistake — unlike my father, I view mathematics almost as if it was like poetry or art. I like beautiful things. I think the umbral moonshine conjecture is beautiful. I like finding patterns in prime numbers. I like looking for patterns in athletes like swimmers or triathletes, thinking that maybe the combination of the mathematics and the physics could somehow matter.

LEVITT: Now, I’m surprised, first you described math as the language of medicine and engineering, and then you said it was the language of measurement. What’s funny is because when I describe math, I say math is the language of the universe.

ONO: Oh, okay.

LEVITT: I hold math to a much higher standard than you, I think. Because really, to me, it’s just — math is the way in which the universe expresses itself. Don’t you think that’s a better way of defining math?

ONO: Yeah, I do. I think it’s a rather bold statement. Please allow me to revise. So I declare on this show that I agree with Steve that math is the language of the universe.

LEVITT: There’s a long history of math and mysticism coming together. I get the sense you’re a little bit in that camp, right?

ONO: Oh, yeah. When I was 10 years old, I would have said that math is nothing but taking tests, memorizing facts, and trying to do well on standardized tests.

LEVITT: And just to interrupt you, you were getting roughly 800 on the math SAT when you were 10-years-old, right?

ONO: Yeah, that’s right

LEVITT: Okay.

ONO: But I didn’t like it, Steve. I didn’t like that. Now at 56, I could care less about any of that. As I just described, and you quite eloquently said, math is the stuff of the universe. It’s, for me, an art form. When I mentioned earlier that I’ve spent a huge proportion of my professional life studying these notebooks of this autodidact Ramanujan, whose ideas came to him as visions from a goddess, I go back to those notebooks many times a year, and I’ve been doing this for decades. And every time I go through, I learn something that I didn’t understand before. If you need me to give you evidence of something spiritual in mathematics, it is that. Where did Ramanujan really come up with these ideas? Now, I’m not a Hindu, and I don’t really know what to make of this Hindu goddess, but I can tell you that when I finally see a glimpse of what Ramanujan probably had in mind when he wrote down some formulas and recognized it as so much more profound than what the symbols on the page seem to suggest, well, that is a spiritual reckoning.

We’ll be right back with more of my conversation with mathematician Ken Ono after this short break.

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LEVITT: The movie about Ramanujan, here’s this unknown Indian mathematician and suddenly somebody decides to make a major motion picture starring Dev Patel and Jeremy Irons. What was your role in the movie? What were you doing with it?

ONO: Steve, you have no idea how lucky I have been in my life. Ten years ago, I was a professor at Emory University. And one morning, I got an email from Matt Brown, who was directing a film calledThe Man Who Knew Infinity, and the email just said, “I’m making this film. Could we Skype tomorrow? We’d like to know about maybe some equations we can put on the chalkboard.” And so the next morning I got on Skype, and maybe just a 15-minute Skype turned into hours because Matt, who’s now a good friend, discovered that I really knew the story of Ramanujan and he thought that I could be helpful. That very next day, he emails me and said, “I just met with Ed Pressman, who was the executive director of the film. We’d like to invite you out to Pinewood Studios in London for pre-production, which starts next week. Can you come?” It was summertime. I had nothing else better to do, certainly, than make a Hollywood film. That’s a surprise. So I got on that plane. I had no experience in film, so I was mesmerized by what Hollywood can do. The art department was amazing. They even had an artist whose main job was to practice and learn how to replicate Ramanujan’s handwriting. And she wrote out multiple copies of all of his notebooks — not just a few pages, the whole notebooks. Before I knew it, I had made a number of friends on the set, and by the end of the week, the casting director comes up to me, said, “We’d like you to sit in and participate in some of the casting. And then a few days later, Matt comes up to me, said, “The two main actors, Jeremy Irons and Dev Patel, are coming tomorrow. I want you to be with the three of us to go through the math scenes.” So I went from just helping out with math formulas, ten days later, I’m in a room with Jeremy Irons and Dev Patel, and we’re working through the script. There was just a little coffee table, some sandwiches, and the script. And I’ll tell you, when I first walked in, I was petrified. I couldn’t even pull my chair up to the coffee table because, what was I doing there? Did I really belong? And Jeremy Irons, within four or five minutes, said, “Look, I don’t know any mathematics, and in fact, I find the mathematics to be quite dull. But you are here with a job to do. And your job is to tell us and show us how to speak mathematics.” They pulled me up to the coffee table and we got to work. When we had our grand opening, we were the gala film for the Zurich Film Festival, which is the opening night film. It’s this major honor. I remember at the post-event party chatting with Matt saying, “Thank you. Thank you, Matt, for inviting me into all of this. You have absolutely no idea what these few years have meant to me. This is the richest experience in my life.” The one thing he said is, “I wrote three people and you just happened to be the first person to answer an email.” So talk about luck. Had I been second or third, maybe we wouldn’t be talking.

LEVITT: Now, you say luck, but it’s obviously not luck, right? They wanted you to do something very specific. They wanted to have a 15-minute Zoom call. They didn’t expect anything of you, and you actually made your luck. It’s interesting because most people have the opposite of you. They would look at that situation and say, “Hey, I got a little break and then because of talent or hard work, I became a big part of this movie.” It’s interesting that you view it the opposite. The talent and the hard work are obviously front and center for why you got to do all this, but in your mind, it was all luck.

ONO: Well, like I just said, I was first of three to respond to an email. The other two, maybe, they probably, well — thank you, Steve.

LEVITT: Most people I know who do that kind of math — highly abstract math that you do, they have far too little common sense to solve practical, real-world problems. But your work on swimming is such an obvious exception to that. It seems to me that the work you’ve been doing with the Olympic swimmers, that’s just miles and miles away from your abstract math, right?

ONO: Yes and no, and this circles back to something I said earlier about the fact that we live now in a world that’s awash in data. The numbers measure the universe. And that certainly applies to athletics. I had a student show up in my class in Emory about 10 years ago, in my number theory class, who was a walk-on for Emory’s Division III swim and dive team. His name was Andrew Wilson. But he was also a brilliant math student, and together we just started to think about how we could mathematically model swimming based on the IMU accelerometer work that some Norwegian scientists had implemented with the Norwegian cross country ski team.

LEVITT: And what’s IMU mean? What’s that stand for?

ONO: It’s inertial measurement unit. It measures the acceleration and deceleration of that unit relative to its position in space. Your Fitbit, your cell phone, your smartwatch — they all have IMUs in them. So if you’re wondering how a Fitbit might calculate how many steps you take in a day, it’s because this internal device is collecting that information. So the way the scientists in Norway were using these IMU units is that they placed them on the skier’s body is in different places, and they also place them on a cross country ski pole, so they could literally replicate not only the movements throughout an execution, but also the acceleration in the various directions and deceleration throughout an event. 65 The IMUs I use collect that kind of information that we import into our computer system, which allows us to assemble what’s called a digital twin. That digital twin will allow us to visualize on screen the execution of the athlete with pretty remarkable accuracy. Had it not been for Andrew Wilson in my class, I probably wouldn’t have carried this research any further. It would have just been something like a lark. Andrew ended up winning multiple national championships and he won a gold medal in the Tokyo Olympics. It became something that has defined maybe the last seven or eight years of my life.

LEVITT: Was it obvious to you right away how to think about using math to improve swimmers?

ONO: If you swim, you know that you’re in a constant battle with the water. How do you optimally streamline your body so that the force you generate propels you through the water as efficiently as possible? That’s a math problem. And it’s a math problem with many different parts. The other problem is exactly what is going on in a swim? You probably have to breathe. You have to take kicks of different types. Where and when do you take those kicks? Swimming the fastest possible race is highly individualized, and is a beautiful example of a difficult mathematical optimization problem. I love that.

LEVITT: So you’re talking — clearly things like Newton’s laws, right, about force equals mass times acceleration, and the particular thing that I understand to be really front and center in swimming is what’s called the drag formula. You have to somehow minimize what’s called the frontal surface area. Could you talk about that?

ONO: If you swim through the water, unlike a fish, almost every movement you execute is likely to work against you. We have fish that can swim 50 miles an hour. A human being swimming their all-out as an Olympian is doing their very best to swim maybe six miles an hour for 20 seconds. The drag that the human body generates, it comes from everywhere. It could come from the top of your head. It could come from what’s called the catch, when you insert your arm into the water. There’s drag there. When you kick with your legs, your legs are probably dangling a few inches below the surface, introducing more surface area for your drag. And what we do in the mathematics is we calculate the surface areas that introduce drag dynamically through a swim using data that we collect, and we calculate how much drag could be minimized by changing execution, changing head position, changing the placement of the hands in streamline. Every little thing that might contribute to drag, we look at, we measure, and we make recommendations in these reports that we send to the coaches.

LEVITT: How many IMUs do you have on a swimmer to be able to make this picture?

ONO: I can put three on at a given time, but you’d be surprised how much information we can get from one IMU placed on an athlete’s sacrum.

LEVITT: So I’m surprised. I would have thought that the arms and the legs would be so critical to this.

ONO: I also have hand paddles, force paddles, which are about the size of a quarter that you can place in the middle of your palm. And athletes, as they swim, this force paddle measures the force that their arms are generating in a swim. It can count how many strokes, but it can do much better than that. It can tell you, for example, how much of the force your right arm generates — say in freestyle, that’s propelling you straight down the middle of the pool — how much of the force is actually steering you accidentally to the right? Or how much of it is actually steering you accidentally to the left throughout a swim, because it’s very dynamic. How much of it is actually driving your upper body downwards when it shouldn’t be? In mathematics, there’s a subject called linear algebra, which is perfectly suited for analyzing this kind of data. It’s called the theory of inner products in three dimensional space. And for students of linear algebra that have studied something called the Gram-Schmidt process, they’ll know exactly what I’m talking about. And they’ll know that you can calculate the inner products of these force vectors throughout a swim, you can identify places in execution where the athlete isn’t as efficient as they could possibly be. But then there’s other things. I measure how much force an athlete puts into the wall when they execute a flip turn. Someone who can execute a good flip turn and then follow that up with outstanding streamline might be much faster than the athlete next to them. If you’re not good at that, how do you become better? The timing of the execution of the first dolphin kick, which is the fastest kind of underwater kick that elite athletes use — when do you begin to execute that dolphin kick coming out of a wall? In fact, what is your angle of rotation coming off the wall? Do you wait ‘till you’re either flat on your back or flat on your stomach before you execute? That’s highly individualized.

LEVITT: So you sound like such an expert talking about it now, but when you started, you were making it up from scratch. Tell me about the first time you tried to measure these forces. What was it like?

ONO: Oh God, it was a nightmare. It wasn’t that long ago, accelerometers weren’t very dependable. They could be called “waterproof” or “water resistant,” but I’ll tell you, those words are close to meaningless, at least 10 years ago. So finding suitable manufacturers that could make these accelerometers that could withstand what we need for elite level swimmers was non-trivial. Even figuring out how to fasten and where to fasten these units on swimmers was difficult. I had athletes that were so strong that they destroyed the belts that we used to attach these IMUs to their bodies. And in terms of the hand paddles, I didn’t even know how to attach these devices to a swimmer’s hands. I still have in my closet probably three or 400 nitrile gloves, neurosurgeon quality gloves, but then I discovered that elite swimmers need to feel the water. So despite the fact nitrile gloves allow a neurosurgeon to have a feel for their tools, they’re not good enough for elite-level swimming.

LEVITT: We heard in the first part of our conversation you were this abstract mathematician talking about monsters and stuff. But you are a legitimate, boots-on-the-ground data scientist now. And it seems to me that there’s a whole different set of skills that you need when doing data science work than you do with abstract math. Do you feel like you’re using different skills or do you feel like those are in some ways closely aligned?

ONO: Oh, no, no, no. I’m using the same skills, but I’m having a different outlook on a particular project, why it matters. In my primary day job, I’m still a number theorist, mathematician. And the goal there is to uncover phenomena that haven’t yet been discovered in the world of numbers. But I use these same numbers, just like I would have as a kid studying baseball cards, and I want to look for properties in them that make swimmers faster. The biggest difference between how I switch my mindset from the parts of the day I’m a pure mathematician and the parts of the day I’m working with swimmers, it’s this: in my work with the swimmers, I’m not looking for a grant. I’m not looking for multiple publications. It is satisfying to see the results in the competitions, and that is what drives me. I started at the University of Virginia shortly before my good friend Todd DeSorbo was hired as the head coach of the U.V.A. swim team, and in just a few years we’ve won four consecutive national team titles for the women. Many of the athletes just returned from Paris a few weeks ago, winning a gaggle of medals. I’ll tell you, the Olympic Games — wow, being there in the finals, watching these really committed, amazing athletes succeed and have their dreams come true. I am so in awe of athletes like Kate Douglass, Paige Madden, and the athletes I’ve worked with because that looks, honestly, superhuman to me. And the opportunity to work as an advisor to the swimmers, I don’t have the words to explain how amazing and extraordinary that’s been.

You’re listening to *People I (Mostly) Admire* with Steve Levitt and his conversation with mathematician Ken Ono. After this short break, they’ll return to talk about his upbringing, and something that may surprise you — why Ken dropped out of high school.

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Ken Ono is on top of the world these days, but his path to get there was surprisingly difficult. He went from child prodigy to high school dropout.

LEVITT: So as I talk to you today, everything seems so easy for you. You’re hugely successful academically. You’re hanging out in movie sets. You’re getting world renowned for your swimming work. But life hasn’t always been so easy for you. I think listeners will be stunned to hear that you actually never graduated from high school because things got so tough. Would you be willing to talk about the pressures and the anxiety that haunted you growing up?

ONO: Yeah, thank you for asking that, Steve. In my life I’ve been very blessed, very privileged, very lucky. And for people who have only known me in my adult years, say 40 onwards, wouldn’t believe anything that I’m about to say. I was a terrible student. I was lost for many years. I had been identified as a young child as being good in mathematics. My parents and Johns Hopkins psychologist, Julian Stanley, even had me take the SAT test in second grade. I was one of those kids. But I hated that. All through middle school and for the first few years of high school, I was about as miserable as you could possibly imagine. I would even later — much later in my early twenties, I almost took my own life. I was a miserable person.

LEVITT: And you were under tremendous pressure from your parents, right? Your parents expected you to be perfect.

ONO: Right. I love my parents. They’re still alive. And if they’re listening to this, we’ve already worked through all of this. You can laugh about it, but I’ll tell you it was really — there was a lot to work through, and we wouldn’t successfully work through it probably until my 40s. My parents grew up in Japan. They came of age in World War II. And, like most Japanese at the time, they were brainwashed. And I’m not exaggerating. The Japanese citizens were brainwashed. They were taught to believe that the Japanese race was superior. They were taught to believe that the emperor was a god. You all know about the Kamikazes, right? Japanese men who joined the military viewed themselves as servants to the Japanese Imperial Army and the Emperor. And so imagine what your outlook on life would be when Japan suffers a humiliating defeat at the end of World War II. You might even remember that the United States had to drop two atomic bombs on Japan before the Japanese military commanders agreed to surrender because they didn’t want to humiliate the emperor, Emperor Hirohito. So what does that have to do with anything I just said? Well, the United States took it upon themselves to help rebuild Japan post World War II. And among these investments, the United States sent and supported and rebuilt the Japanese universities. And my father at the time was quite literally a starving math graduate student. Several distinguished American scientists went to Japan, held conferences, and rescued a whole generation of Japanese math graduate students. And my father was one of them. So coming out of World War II, my dad and my mom were brought to the United States, where my dad embarked and launched a very distinguished career as a scientist. He ended up becoming a member of the faculty at the University of Pennsylvania. Later, he was on the faculty at Johns Hopkins. Try to imagine what the lives were like for my parents because they came from the country that bombed Pearl Harbor. Maybe my dad was a brilliant mathematician. Sure, he got these great jobs, but outside the office, my mom and my dad had to live among neighbors who were brought up to hate them. People shot out the windows of the kitchen, they left burning bags of dog excrement on the patio. It was rough for my parents to carve out a life in the United States. So how did they respond? They responded out of fear. My dad’s in his 90s. Even to this day, out of fear, they lock themselves in their little apartment in their community. So this was the mindset of my parents when they were raising the three Ono boys, me and my two brothers. And they quickly came to the opinion that the only way any of the three Ono boys would amount to anything in the country that hated them would be if we became something like the spitting image of my dad. So my oldest brother, Mamoro, he went to Juilliard on full scholarship. He’s now a professor of performing arts. My brother, also under extreme pressure — well, he’s president of the University of Michigan in Ann Arbor. And you’re talking to me as a mathematician. The formula worked. But as the youngest of three, I wanted to be playing baseball in middle school. I wanted to be like all the other kids in school, rather than being one of the three Ono boys. And for the kids that remember us back then, we were strange, right? We were expected to be, like, perfect students. And I hated it.

LEVITT: It’s interesting to hear you describe the pressure that your parents were under because I actually experienced something really similar. My father put tremendous pressure on me. An A was never enough. Why didn’t I get a perfect score? He would say, “You’re special. You can’t waste your talent.” I suspect I was following the same path as you. One where the pressure overwhelmed me But something really odd happened that changed everything for me. I was a tiny child growing up. I was the smallest in every class at school. I had thick glasses. I always felt small and powerless. And probably like every kid, I thought adults knew everything. I was terrified of my dad. He would scream at me when I wasn’t good enough. And then something changed. I was a golfer as a kid, a serious golfer. I would spend every day practicing alone at the golf course. And at night after dinner, I’d play nine holes with my father, who was also an avid golfer, although not a very good one. Around the age of ten or eleven, I beat my dad at golf for the first time. And by the age of 12, I beat him every single time. And psychologically, that changed everything for me. Somehow, I couldn’t take my dad seriously once I was better than him at golf. And I wasn’t afraid of him anymore. It somehow made me feel like I was the boss. He would still say, “Oh, an A wasn’t good enough.” But I would just be able to laugh it off. It was more like a running joke where I would bait him. I’d come home from school and I’d say, “Hey Dad, I got a 96 on the math test today. Tory Herman got a 100 again.” And he would yell, he’d say, “Jesus Christ, I’m not paying $6,000 a year to send you to private school to get 96s!” But if my dad hadn’t been such a terrible golfer, I think my life would have turned out very differently and much worse.

ONO: I never had that golfing moment with my dad. It wasn’t until my 40s that I’d come to peace with all of this with my parents. It’s no longer a problem, you know, but — this is a difficult thing to talk about.

LEVITT: So you never graduated from high school because the pressure was too much.

ONO: Well, I had to escape. So I was very lucky. My brother, Santa, who, as I mentioned, is now the president of the University of Michigan, he helped me out. I went to live with him and work in Montreal. I did a lot of growing up. I cleaned a lot of test tubes at McGill, where he was a graduate student. And fortunately, University of Chicago, at least back in the day, was a place where people like me, without high school degrees, could readily go.

LEVITT: That’s amazing. I would’ve said that was impossible. That’s awesome that the University of Chicago was willing to take a chance on the on kids who clearly had some talent, but but were struggling.

ONO: I know many others. So it wasn’t unusual. Had that not been the case, I’m not sure where I would be today.

LEVITT: Were there professors you had along the way who encouraged you?

ONO: The first was a professor at the University of Chicago by the name of Paul Sally. Steve, maybe you knew Paul?

LEVITT: Everyone knew Paul.

ONO: Everyone knew Paul, the pirate professor. He rescued me. At the University of Chicago, I wasn’t a great student. It’s interesting, when I go back and visit the math department, they have this legend. They think I was someone that I wasn’t when I was a student in the ‘80s. I was, at best, an average math student, and Paul Sally helped me discover my passion for mathematics. He got me into UCLA for graduate school, where I met another great man by the name of Basil Gordon. Basil Gordon came from the family of British distillers that makes Gordon’s Gin.

LEVITT: Okay. Uh huh.

ONO: And he was also an autodidact. Honestly, the smartest man I’ve ever met. He made his way to UCLA as a professor after completing two Ph.D.s at Caltech — one in mathematics, that doesn’t come as a surprise, but he also wrote a Ph.D. in physics under Richard Feynman, because he just loved learning. We would meet in his house on Saturday afternoons for hours, but usually the first hour would have nothing to do with the mathematics. He would recite poetry. He’d play, like, a Chopin nocturne on his piano. We would go for long walks on the beach. And then we’d launch into the math. After a few months of this, I’ll never forget the day, went to his house in Palisades Avenue, knocked on the door, he sat me down in the front room, and he started recitingMoby Dick. The opening toMoby Dickwas really striking. It was about a young man who was in search of self discovery and he decides to sail out on the sea to see what life brings his way. And that was the message that Basil explained to me, and then he said, “Look, doing mathematics is something like a voyage. You don’t know what you’re going to get. It won’t be related to the tests, it won’t be related to the papers, but you have to be in the right mindset that you’re willing to let it all go, just like the main character inMoby Dick.” And as we talk about this now, I don’t want to start crying, but Basil Gordon taught me how to love a subject, and look for the beauty, and not care about what anyone else thought. And I love that in him because he was privileged. He came from wealth, so he was able to live this wonderful life thinking about mathematics and physics. And he invited me into that mindset. And if there’s one lesson I could teach to my students — sometimes I describe myself as someone who thinks like a child. Someone who thinks like a child is curious and isn’t worried about what others think. So of course I think about what my colleagues think professionally and my neighbors, right? Do I mow the lawn to the right height? No, but professionally, I needed that freedom and I learned that from Basil Gordon. He really was a great man.

We all should be so lucky as to have a mentor like Basil Gordon. someone who can see right through us to understand the exact demons that are holding us back and then finds just the right way to talk to us, to exorcize those demons. Like Ken Ono, I had incredible mentors: my father and economists like Jim Perturb and Gary Becker. I can’t say any of them ever played Chopin for me on the piano or quoted from classical literature. If they had, I would have run as fast as they could in the opposite direction. But like Basil Gordon, they taught me not to care about what other people would think of me. And that, without a doubt, is one of the most valuable lessons I ever learned. We’re not done yet with the link between math and swimming, because next week we have a very special bonus episode featuring Kate Douglas. She’s a graduate student at the University of Virginia, working in statistics, a student of Ken Ono’s. And she also happens to be one of the world’s greatest swimmers. She won two gold medals and two silver medals at the Paris Olympics. And we’ll talk to her about her life in swimming and how she has benefited directly from the kind of tools Ken Ono works on.

LEVITT: So this is the point in the show where we welcome my producer Morgan on and we have a conversation.

LEVEY: Hi, Steve. A few episodes ago, we had evolutionary biologist Richard Dawkins, and as a result of that conversation you ended up hosting one of his live events. He’s on a book tour right now for his newest book,The Genetic Book of the Dead. You were in D.C. for that event. How did it go?

LEVITT: It was okay. I’m sorry to say I didn’t do that great of a job — not for lack of trying, I just missed the mark.

LEVEY: In what way did you miss the mark?

LEVITT: Part of what Dawkins does that is so fascinating to me is the science, the evolutionary biology. And for that reason, I just figured that’s the part of him that everybody thinks is interesting. But what happened when I was actually in the event is I realized the people in the audience were so much less interested in the science, and so much more interested in the atheism and some of his controversial public statements about religion or about biology. And so I had intentionally steered my questions away from that because I always feel like it’s risky to let an 83-year-old talk about social issues. I listen to my dad — my dad’s an incredibly smart and thoughtful man, but he says things that you cringe at sometimes. And so I just thought, I’ll try to avoid anything cringeworthy while Dawkins is on stage. But the thing was, whenever we did get to a topic that was more controversial, oh, the audience went crazy. They loved it. They cheered. They hooted. They hollered. So midway through, I realized my mistake, but there wasn’t really any getting out of it.

LEVEY: So if the audience wasn’t there, how would you judge the conversation? Do you reflect back in the conversation differently if you take the audience element out of the equation?

LEVITT: Yeah, I will say one of the reasons I did it is that for me, what’s most exciting and rewarding about a podcast episode is when I feel like I have a personal connection with the guests. And I was curious, I’ve never interviewed anybody live in front of a big group like this. And I imagined it would lead to a bond between me and the speaker. It would be us and there’d be this crowd, but somehow it would be more personal. That was completely and totally wrong. Having an audience there removed any feeling of intimacy that I ever feel when I’m talking one-on-one in a podcast. So that assumption just turned out to be totally wrong. I’m glad I experienced that because I’ll never make that mistake again and think that I should do something live in front of the audience because it will make it better or more interesting. The other thing that made it hard is that in talking with Richard on the podcast and after, I knew that he hates it if he ever gets asked the same question twice. And so I used up all my best thinking and my best material on the podcast, and I had to go back to square one and try to start over and come up with good questions for him. So I think the questions weren’t nearly as good or as interesting as the first round either. I pretty much struck out on all dimensions is probably the right answer.

LEVEY: Selfishly, I’m glad that your best questions were used for thePeople I (Mostly) Admireinterview. Do you have any sense of how Richard Dawkins thought the live conversation went?

LEVITT: Oh, he was kind, but you can never believe people in that setting. Of course he’s going to say that it went well. But he didn’t seem unhappy. At least in that regard, so maybe I — two strikes out of three, but I didn’t completely strike out because Richard seemed happy enough.

LEVEY: Listeners, if you have a question for us, our email is PIMA@freakonomics.com. That’s P-I-M-A@freakonomics. com. PIMA is an acronym for our show. We read every email that’s sent, and we look forward to reading yours.

Next week we’ve got the bonus episode with Olympic gold medalist Kate Douglass. And in two weeks, another brand new episode with MIT economist David Autor. David is a labor economist who *The Economist* magazine has called “The academic voice of the American worker.”

AUTOR: The people who’ve been most amused by that are my children. They say, “There goes ‘the academic voice of the American worker,’ shopping for shoes online.”

As always, thanks for listening and we’ll see you back soon.

* * *

*People I (Mostly) Admire* is part of the Freakonomics Radio Network, which also includes *Freakonomics Radio*, *No Stupid Questions*, and *The Economics of Everyday Things.* All our shows are produced by Stitcher and Renbud Radio. This episode was produced by Morgan Levey with help from Lyric Bowditch, and mixed by Jasmin Klinger. We had research assistance from Daniel Moritz-Rabson. Our theme music was composed by Luis Guerra. We can be reached at pima@freakonomics.com, that’s P-I-M-A@freakonomics.com. Thanks for listening.

ONO: Wow, I, so, I would have thought you were about my age. I’m 56.

LEVITT: I am. I’m just lazier and less ambitious than you are.

## Sources

**Ken Ono**, professor of mathematics and STEM adviser to the provost at the University of Virginia.

## Resources

- “‘Digital Twins’ Give Olympic Swimmers a Boost,” by Katherine Douglass, Augustus Lamb, Jerry Lu, Ken Ono, and William Tenpas (
*Scientific American,*2024). - “Swimming in Data,” by Katherine Douglass, Augustus Lamb, Jerry Lu, Ken Ono, and William Tenpas (
*The Mathematical Intelligencer,*2024). - “Integer Partitions Detect the Primes,” by William Craig, Jan-Willem van Ittersum, and Ken Ono (
*PNAS,*2024). *The Man Who Knew Infinity,*film by Matt Brown (2015).- “Proof of the Umbral Moonshine Conjecture,” by John F. R. Duncan, Michael J. Griffin, and Ken Ono (
*Research in the Mathematical Sciences,*2015). - “Ramanujan’s Ternary Quadratic Form,” by Ken Ono and K. Soundararajan (
*Inventiones Mathematicae,*1997).

## Extras

- “Richard Dawkins on God, Genes, and Murderous Baby Cuckoos,” by
*People I (Mostly) Admire*(2024).

## Comments