Rémi Drolet

As a child, what did you want to be when you grew up? 

I could never get enough of dinosaurs and other long-extinct creatures. So, for the longest time, I wanted to be a paleontologist.  My parents would take me to the Canadian Badlands, a hotbed of fossilized dinosaur remains, where we would marvel at huge reconstructed specimens and look around for little fossils in the rocks of the nearby riverbank. One time, I found a fossilized bone at one location and excitedly took it to the local museum. They told me it likely came from a duck-billed dinosaur, such as a hadrosaurus, and I thought it was the coolest thing ever.    

What was the most transformative course from your undergraduate education?

In my sophomore year, I took my first foray into more advanced physics with a class on Einstein’s theory of General Relativity. Before the start of that semester, the director of undergraduate studies, David Morin, told me that it was a beautiful subject, but so messy in practice. He was right: the homework problems were messy, often involving pages and pages of calculations per question, but the course delved deeply into all of the relevant background ideas. My instructor, Jacob Barandes, even took the time to tell the fascinating personal story of how Einstein came to develop the theory essentially all on his own. It was so inspiring to me, and though I do find experimental physics to be interesting, taking General Relativity cemented my heart’s place in theoretical physics.  
 

You are a skier, finishing your college skiing career as a three-time All-America honoree, and you represented Canada at the 2022 Beijing Winter Olympics. What were some of your keys to succeeding at both your sport and your academic career?

As counterintuitive as it might seem, an important lesson I learned during college was that sometimes, I should try not to force everything to go perfectly. In high school, I was hellbent on getting every minute detail in my work perfect. Similarly, I was extremely focused on optimizing my lifestyle for performance in skiing, and I maintained a very high training load most of the year. However, when I started college, this became too difficult for me. My class load was just too demanding to get upwards of nine hours of sleep every night while continuing to train just as hard, if not harder as I had before. In my first year, I would lie awake in bed, unable to sleep because I couldn’t stop thinking about how little sleep I would be getting. I soon became stuck in a cycle of constant sickness, and trained less hours than I would have had I taken things easier at the start. I also put huge pressure on myself to make it to the Olympic Games, and that weight ended up sapping a huge amount of energy from me. Gradually, and especially after the Olympics, I learned that sometimes less is more. I trained a little less during the school year and fretted less when I didn’t get enough sleep. I think I was better off for it in the end. Nowadays though, I find myself having a lot more time and flexibility, and it has been fun to find a new balance where it feels like I am no longer making sacrifices in my skiing career, but also staying engaged in other areas of my life.  
 

What has been your most fulfilling moment of your career in skiing?

This would have to be seeing the outpouring of support I received while in China for the Olympics. My local community and the skiing community at large have always been very good to me, but during the Games, it was amazing to see the scale and amount which people cared how I was doing. Even people I barely knew, or in some cases did not know at all, were sending me notes of encouragement. It was really something special that made representing Canada at the Games such a privilege.  
 

How has your liberal arts and sciences education informed how you think about and approach your athletic career?

I am someone who likes working with data, and now that I have more time post-college, I am applying some of the skills from my education to better collect and analyze skiing-related data. For instance, I am working to improve my training logging so that I can more easily look back at the end of a season to see what worked and what didn’t. On competition day, using the right ski for the right condition is also super important and can vary hugely from day to day. The best skiers in the world could likely have on the order of a hundred pairs to choose from, so I am trying to improve my system for remembering what skis I used when, which pairs are good, and which pairs are mostly obsolete and ought to be traded in for a better pair.  
 

As an undergraduate at Harvard, you contributed to research projects on quantum field theory and wave turbulence and the development of a self-assembly platform. Do you see any common threads between these varying fields of study that guided your interest in these areas?

My research in self-assembly is chiefly an application of statistical physics, specifically equilibrium statistical mechanics, that uses partition functions to predict yields of self-assembled structures. Meanwhile, quantum field theory has a deep connection to statistical physics, mainly in terms of the underlying mathematics. This means that some statistical systems can be reformulated as quantum field theories, famously including the Ising model of magnetic dipoles arranged in a lattice. Wave turbulence is also a topic in statistical physics, although it describes a system far from equilibrium, as opposed to the Ising model and equilibrium self-assembly. Despite its non-thermal-equilibrium nature, Turbulence can still be studied from the lens of quantum field theory, which is what I was attempting to do. Although there are these points of similarity, the topics of research you mention are very different. Self-assembly I began researching early in my undergraduate studies (and continue to research to this day) using mostly computational methods. My interest in quantum field theory, on the other hand, was developed in large part by taking advanced courses on the subject and involved entirely pen and paper work. My research in the field is overall very limited both in scope and in the amount of time I have spent on it so far, but I do very much hope to continue working on it in the future. It is another beautiful topic, and although the bar to making meaningful progress in the field is extremely high, I hope to be able to get to that level some day!  
 

Phi Beta Kappa’s motto is “the love of learning is the guide of life,” and we are dedicated to life-long learning. What do you want to learn next?

Although I have done a bit of work in machine learning in the past, I have never really played with neural networks before. I am excited now, though, to be starting a new research project that will involve neural networks in a major capacity. This has been on my to-do list for a long time, and I find it interesting to learn more about these amazing tools. Apart from that, I have been making a push to delve deeper into the world of Vim and Emacs (text editor programs). I have been finding that really fun, and I hope to develop my knowledge of the Lisp family of programming languages in the process.  
 

What was the best advice you were ever given and who gave it to you? 

What comes to mind is a word of advice from my mom, who frequently told me growing up when I would complain about something minor bothering me, that “you are not made of glass!”  Sometimes, when I am worried about something pushing me outside of my comfort zone, I remind myself that sometimes it is worth toughing it through.
 

What book(s) are you reading right now? Are you listening to any podcasts or watching any shows? Anything you'd recommend? 

I am currently picking my way through Ted Chiang’s Stories of Your Life and Others. I am also nearly through Cixin Liu’s Remembrance of Earth’s Past trilogy, so I’ve started watching The Three-Body Problem on Netflix, which is based on that series. I love science fiction and have been really enjoying these stories.  

Published on December 3, 2024