I was born in Namur, Belgium, and raised in a small village in the countryside. As a child, I was not particularly pushed toward excellence. I was raised with strict rules, especially regarding the importance of hard work, and lived in a world where sport held a central place—my mother was a sports teacher, and my father was a table tennis champion. My high school education took place at the Athénée François Bovesse in Namur, where I initially chose options in Maths and Latin before switching to Maths and Science in my last two years. As you might expect, I was talented in Maths, but I actually enjoyed most of my courses and their diversity—unless they required too much memory, which has never been my strength. The Athénée provided me with a solid education, but I was particularly fortunate to have a stimulating group of friends in my class who definitely pushed me intellectually—not to mention the many excesses of our teenage period.

When I was 17 and it came time to choose a university subject, I hesitated between Mathematics, Physics, and Engineering. Inspired by my wonderful Physics teacher, Mr. Charlier, I opted for the second. I just loved finding ways to translate real-world problems into the language of Mathematics. Regarding the choice of university, it could only be the Université Libre de Bruxelles, as it was the main secular university in Belgium—a criterion particularly important to my family—and it also gave me the chance to move to the "big city." I remember very well my first class in Calculus, where the lecturer started by defining the notions of continuity with deltas and epsilons. To be honest, I was completely lost, could not grasp the concept, and felt like an imposter when I discovered that several students, coming from more prestigious schools, already knew about it and found it obvious. It took me some time, but I eventually found beauty in these Greek letters.

In retrospect, I approached my university experience in a very naive way. I did not have big plans for my future. I attended classes and worked hard on my course material, but I did not think about applying for internships or gaining some research experience, for instance. My original plan was to become a teacher, and it was during my last year, as I started to take special optional courses on high-school mathematics, that I discovered the possibility of doing a PhD after my Master's. From today’s perspective, I feel like I come from another era, especially when I compare this younger version of myself with the students who currently apply for a PhD. They often have clear research questions, previous research experience—including published papers—and an impressive level of maturity.

I have ambivalent memories of my PhD. My subject was a continuation of my master's dissertation and focused on the modeling of fluidized granular media, systems composed of macroscopic, dissipative particles that may exhibit fluid-like behavior when sufficiently excited. I remember a very lonely period at first, exacerbated by my (then) shy nature and the lack of group dynamics in my department. My supervisor, Léon Brenig, and I met once in a while to discuss my ideas and my calculations. In a way, I had contradictory desires at the time, looking for guidance and trying to escape it. The first half of my PhD was unproductive in terms of outcomes, but I spent a lot of time reading and finding pleasure in discovering new ideas, looking for ways to connect them, always with the caring support of Léon. I discovered my classics, enjoying the original works of Ehrenfest, Kac, Kelvin, Boltzmann, and Jaynes. Most importantly, I learned how to propose my own research questions, to steer my work - in good or bad directions, and to accept that mistakes (my first work, entitled "Wooled inelastic hard spheres" was mathematically wrong, as I discovered the hard way during my first conference), a posteriori bad ideas, and dead ends are inherently part of the research process. I also learned to accept that smart ideas often appear so simple, almost deceptively simple, after they have been properly formulated.

My PhD took a completely different turn during its second half. I was invited by Michel Maréchal, first for short periods and then for my last year of PhD, to work at CECAM, the "Centre Européen de Calcul Atomique et Moléculaire," which was then hosted by ENS Lyon. It was time for me to look at more concrete problems and gather my thoughts. I finally decided to learn how to code—as I wrote before, a different era—starting first with C, and then spending almost 6 months writing my own event-driven simulation code in Java. I became fascinated by coding and was in awe of the power of Mathematics to explain numerical results. I remember the wonderful feeling of discovering a new phenomenon numerically, the so-called "granular clock," which could be explained, at least qualitatively, with a simple system of equations and would later be observed by experimentalists. CECAM offered a very different environment, with frequent visitors, seminars, and schools, giving me a first glimpse of what a collaborative environment could be like.

The journey through my PhD, which I finally defended in 2004, was not the most efficient in the short term, and I actually finished it without a single publication. However, I am convinced that it has made me the scientist that I am today, teaching me resilience, faith, and how to find a way—my way—when lost in science. After these formative years, the stage was set, and it was time to play. By chance and necessity (this was my only job offer), the next step in my academic career would happen in another field of research, network science, as we call it today. I instantly loved this interdisciplinary domain that allowed me to combine my theoretical background with my intellectual curiosity. Networks are the language of connectivity, and they provide a powerful framework for modeling interacting systems. What I particularly appreciated—and still appreciate 20 years later—were the endless possibilities in front of me, allowing me to explore theoretical models, e.g., in opinion dynamics, work on algorithms, e.g., in community detection, and collaborate with experts in neuroscience or urban science to help solve practical, data-driven problems.

In contrast to my PhD, I only have wonderful memories of my seven years as a postdoc. It was a time when I had reached the maturity to be an independent researcher, a period full of encounters, random or not, that helped me build a network of collaborators to whom I owe so much today. It was also a time when I met mentors who would shape my taste and style in science. Among them, I think of Paul Krapivsky, who reintroduced Maths into my research, Ed Bullmore, who showed me how to be a good scientific writer, Mauricio Barahona, who emphasized the importance of taking time and made me think of systems in terms of processes, Stefan Thurner, who broadened my research horizon, and Vincent Blondel, for his leadership. It was during this period that I understood I had found my place in science.

I moved to a new stage in my career in 2011, when I first became a Professor of Mathematics at the University of Namur (after numerous rejections from Universities across Europe. Never lose hope!)—my hometown—and then at the University of Oxford. The biggest challenge of this transition was definitely managing the multiple tasks that the position involves simultaneously. I rediscovered how much I love teaching—even if I complain at times—especially for the feeling of fulfillment and purpose it provides. I have now built a group of around 10 PhDs and postdocs, and I am doing my best to accompany them on their scientific journey, providing the right balance of freedom and guidance that is so important for their long-term development. My research agenda is also shaped by these interactions, and I currently work on a variety of problems that arose from our discussions and the combination of our differences. “More is different,” after all, and often better in science. At times, I miss delving deep into a mathematical problem and spending hours coding and debugging (I try to reserve one week during the summer for this "guilty pleasure"), but I love even more the richness of our interactions, the surprising and fascinating projects that would not have emerged otherwise, and seeing them grow as independent, empowered researchers. I am looking forward to discovering their own paths in science and in life 20 years after their PhD.

Figure 1. A. Embedding of a signed network through the repelling Laplacian, from Babul, Shazia’Ayn, and Renaud Lambiotte. "SHEEP, a Signed Hamiltonian Eigenvector Embedding for Proximity." Communications Physics 7.1 (2024): 8. B. Balanced and antibalanced networks with complex-valued weights, from Tian, Yu, and Renaud Lambiotte. "Structural balance and random walks on complex networks with complex weights." SIAM Journal on Mathematics of Data Science (2024) in press. C. Curvature of Euclidean random graph from their effective resistance, from Devriendt, Karel, and Renaud Lambiotte. "Discrete curvature on graphs from the effective resistance." Journal of Physics: Complexity 3.2 (2022): 025008. These three recent projects are the fruit of the coincidences of my discussions with PhD students.