Jeremy Callner graduated from The Academy Music Department in 1994. He pursued a Bachelor’s Degree in Music at the University of Hartford in Connecticut for two years, where he studied with Jackie McLean, a jazz legend who played with musicians like Miles Davis and Charles Mingus. In 1996, he moved back to Chicago to complete his degree in jazz saxophone at Roosevelt University. Later on, Jeremy started a music school called Caltera School, with Academy Head of School Jason Patera.
After working for a number of years as a professional saxophone player in Chicago, Jeremy was inspired to pursue a new career. He was living with his cousin Sam, who was studying biology at The University of Illinois at Chicago (UIC). Most days, Jeremy would listen intently to Sam as he described what he had learned in class — especially what he learned in calculus. Jeremy was inspired and began to sneak into Sam’s classes. At the end of the semester, he decided to stick with it — he bought the books, attended the lectures, but didn’t officially enroll. Sooner or later, Jeremy’s assumed professors began to take notice. To his surprise, they didn’t care that he was crashing their class — they thought it was cool, and offered Jeremy the opportunity to take the exams. With that, Jeremy had slipped into the physics graduate program at UIC. He would later earn his Ph.D. at CERN, the European Organization for Nuclear Research, which houses a Large Hadron Collider and particle detector in Switzerland.
This is a conversation between Jeremy and Tim Butler (Director of Marketing at The Academy) about his career, the importance of music education, artificial intelligence, and more.
TIM BUTLER: So, you’ve been in Switzerland for over six years. And you initially kind of stumbled into a career in particle detection.?
JEREMY CALLNER: Yeah, and my interest was actually in heavy ion physics. I was fascinated with The Big Bang and understanding the state of matter one microsecond afterwards. But during my time at CERN, I sort of managed to “Forrest Gump” my way into physics history as one of the people who discovered the Higgs boson particle. I was in the right place at the right time.
TB: Wow, I watched a documentary about that!
JC: Don’t get me wrong. It’s not like they wouldn’t have found it without me. In fact, I’m sure any job I did would have gotten done by someone else had I not been there, but I was there, and got to be an author on the papers as a result.
TB: That’s still awesome.
JC: Shortly after that, my wife got a job in Zurich, and we decided to move there. After a year of traveling back and forth between Zurich and Geneva, I said “Well, I’m in Zurich. I should probably get a job in finance.”
TB: Another potential career change.
JC: I think I was largely inspired by the Occupy Wall Street movement. I realized I wasn’t so much of a protester. I didn't feel like standing out on the street was good enough. I remember thinking: I am really going to occupy Wall Street. I decided to get involved and effect change from within. That's how I got into risk, which is a branch of finance and financial mathematics. I eventually went to work within the Swiss financial infrastructure as a risk analyst, and now I am the head data scientist for the Swiss financial infrastructure at a company called SIX Group.
TB: What do you mean by ‘Swiss financial infrastructure’?
JC: SIX Group is a center of Swiss infrastructure and exchange. They own the stock market and all the clearing and settlement systems (the stuff that happens after you trade stock on the stock market). They also own a financial information business, which is sort of like a Bloomberg or Reuters. Lastly, every payment that happens electronically in Swiss francs goes through us. So all the card transactions and all the merchants — basically anything you do with Swiss francs comes through SIX Group. It’s kind of a fun time to be a data scientist for SIX Group because we are just starting to look at this idea of “big data” and its applications. And I’m helping to lead that movement. It’s very exciting.
TB: I can see why Jason wanted me to speak with you.
JC: And in the last three months I started playing the saxophone again!
TB: Even better. So was it out of necessity that you started focusing more on finance, as opposed to just physics?
JC: Well, there were really several reasons. First, I had a really strong desire to do something after the big crash. A lot of people lost pension funds. Companies went under. This was right after the housing bubble collapse and everybody was really angry at the banks. I think I was too. And I just decided that if you really want to change something, you have to get involved.
TB: And you happened to be living in a place that had a central role in the whole story.
JC: Definitely. I was inspired to try to do something. I would be lying if I said that I didn't realize there was a lot more money in finance than in physics, and definitely more than in music. But that was not my motivation. I really wanted to be part of what was going on in Zurich, which is really one of the world’s financial centers. And when the Occupy Wall Street movement happened, I suppose I felt compelled to try to make a difference.
TB: How did you make that transition so quickly? I assume the math was similar.
JC: I had made a career change before, so I knew how it went. I went on LinkedIn and started contacting everybody I could find who was a physicist working in finance. I’d try to meet them for coffee and ask: “How did you do it?” “What books should I read?” “What else should I learn?” “Who else do I need to talk to?” You do this enough and eventually someone says “OK! Let’s give this guy a job.”
TB: That’s great advice for young people. We're approaching the end of the school year quickly. I don't think the soon-to-be graduates realize how helpful it can be to just find someone on LinkedIn and say “Tell me how you did this.”
JC: That's advice I've given over and over again. Find the person doing what you want to do and ask them how they got there. And it’s important to note that music has played a much bigger role in my success than one would know if they just looked at my resume. Learning music really taught me how to learn, and how to practice. And how to practice learning and practice practicing.
I remember studying for the Ph.D. qualifying exams with a friend of mine, and we were attempting this really hard problem. When we finally got to the right answer, my friend was like “OK, let’s go on to the next one.” And I said “No, let’s do this one again!" He couldn’t understand why I would want to do it again. I wanted to do it until it was easy.
TB: Because you began your career practicing music in repetition.
JC: Yeah, and he just did not understand that. And ironically this exact problem ended up being on the qualifying exam.
TB: And it was easy for you.
JC: Practice was a difficult concept for my colleagues, and not something I learned outside of music.
TB: Is this something that you think The Academy helped instill in you?
JC: I think The Academy gave me the desire to become a musician, and that ultimately led to me mastering practice. And I think practice is something that The Academy teaches even better today than when I was there. Practice was one of the major pillars of Caltera School. Jason [Patera] and I felt that too many teachers in music education were not actually teaching students how to practice. It is rare that someone is taught how to learn, rather than just what to learn. I think that’s something that Jason and I really brought into music education, at least at Caltera School and around The Academy.
TB: Is there something else that sticks out as a lasting memory of your time here at The Academy? Jason frequently talks about the Academy community. I've always been curious what that meant in the earlier years of the school.
JC: It's interesting. I think it was very much a special community. But to a certain extent, I feel like it is as strong a community now, twenty years later, even though I haven't seen many of my classmates for years. Bottom line, I would not have finished high school if not for The Academy. I would have just dropped out. I was a terrible student. I never did homework. The Academy had every right to just kick me out, but they didn’t give up on me. And eventually, I learned to value that commitment. That is, I eventually realized the value of, and the opportunity that education is. A lot of people don't even get one chance. I had a hundred. And that idea has inspired me to look at the amount of privilege bestowed upon me. Had I not taken advantage of it, I think it would have been really offensive to people who have not had that chance. So, I think at least on a professional level, The Academy saved my life. On a literal level, it’s also possible. It gave me the freedom to grow, to be myself, and the freedom to screw up. The school was like a parent. It just could not give up on me. Even after I gave up on myself.
TB: It’s great to hear that the school had that kind of impact on you. With another class of seniors graduating from The Academy in a couple months, is there anything you hope they know leaving high school and going on to college or into the professional world?
JC: I think just to be grateful, and do not take this time for granted. Part of what came out of me studying jazz was a heavy focus on African American history. It inspired me to start reading slave narratives — actual books that were written by slaves in America. I think there are a few hundred known books that were written by slaves. The first time I opened one of these — the first time I saw the word “I”, the first person, the voice of a slave — It had a profound impact on me. And I think this motivated me quite a bit, all the way through physics. I said I was a terrible student in high school.. I wasn’t a great student with my music education, either. But then in physics, because of my new understanding of the value of education, I believe I'm the only person (at least I’m the only person anybody there can remember) who actually made it through the Ph.D. program at UIC with a 4.0. And that was inspired largely by the incredible reverence I had for the university, but also just knowing I had the opportunity to study something like physics at that level.
TB: I feel like I definitely did not appreciate higher education as much as I should have. After high school, college was just a foregone conclusion. I didn't realize at the time how much of a privilege it was, and I regret how much more I could have done in college.
JC: Yeah, exactly. And the question then becomes: can you actually impart this appreciation to graduating seniors? I don't know! College was a foregone conclusion for me as well. My dad always said “I don’t care what you do, but get a college degree.” I never even thought about what an honor it was. I could be a musician if I wanted, but I was going to get a degree.
TB: My parents approached it similarly. They were never saying you had to do one thing or another, but they wanted me to go to college and to graduate.
Switching gears, you mentioned big data earlier. Is there anything else you are working on right now that you are particularly excited about?
JC: There is a revolution happening all over the world in artificial intelligence. With SIX Group, I’m helping to usher it in. I’m talking about the concept of machine learning. Sometimes people think of A.I. as new technology, but it actually comes down to mathematical models developed many decades ago. And you need a physics or mathematics background to really work with it.
TB: So what does that mean? A “mathematical model” instead of a “new technology”.
JC: I mean that in the end, we’re just talking about the same old computer. A CPU and RAM. But the programs are mathematical models that describe how a machine should learn. For example, think about how a machine plays chess. In the past, you programmed the computer with the rules of chess and some guidelines for deciding moves, and it would then react to situations based on its best options. Now, we teach the computer the rules of chess, give it some parameter space, then let it play a million games against itself. And it learns what is effective by itself. Essentially, the computer learns from experience.
TB: So this is a process of creating a ton of scenarios and probabilities for it to learn from?
JC: It came from that direction. There is a branch of machine learning called neural networks, which is modeled after the human brain. As the brain has experiences, connections between neurons begin to form, and some are cut. That is, the brain realizes that some neurons don’t have to talk to other neurons, and some have to be very well connected. We’re applying this process to computers. A simple example is a spam filter. A spam filter uses text as the input, and provides an output of “this is spam” or “this is not spam”. Sooner or later, the computer (its neurons, so to speak) kind of figure out the features of the email that create “spam” and “not spam”. And with enough examples, they get really good at it.
TB: That's an interesting example. Now that I think about it, when my Gmail filters messages into the “promotions” or “social” tab, sometimes it feels like it is getting better at it.
JC: Absolutely. Spam is a great example because the hardest challenge in machine learning is getting enough data to actually train the machine. And the thing about spam is that users are moving stuff to their spam folders manually all the time. And Google can then say “Aha! These are the spam messages”. This is what artificial intelligence, neural networks, and machine learning are all about. And it's connected with this term big data because you need large amounts of data to train these models. Why is it happening now? It’s happening now because we finally have cheap enough storage and CPU power to really make these mathematical models work. We’ve had the knowledge for 50 years. But people hadn’t really been able to use it.
TB: We just have so much more space.
JC: And because of this, we can know something like how money is being spent in Switzerland at this moment. What stores it's being spent in and so on. But for me, being on the right moral side of this topic is extremely important. We’re not trying to take this data and sell secrets. It can be put to good use. One really powerful use of machine learning and artificial intelligence with payment data is fraud detection. We are training computers to detect fraudulent transactions. When somebody tries to use a card fraudulently, it is rejected instantly, and the computer is a lot faster than any other process that can do this. It’s all quite interesting.
TB: It’s fascinating.
JC: Well, it sounds fascinating, but I don’t know. At the end of the day, I’m still sitting in front of a computer screen like everyone else!