O wonder!
How many goodly creatures are there here!
How beauteous mankind is! O brave new world
That has such people in’t!

This section of the website is dedicated to the many people that shaped my life in some form or fashion. I am sure that most of them do not even know about their positive influence—and I myself am constantly re-discovering how much more ‘obvious’ their influence was in hindsight. If you find your name on the list, I hope it makes you smile.

(By the way: I only included full names for people that have an established online presence; I do not want to inadvertently ‘dox’ anyone here)

Why?

In academia, there are only two acceptable places for acknowledgements:

1. A thesis (B.Sc., M.Sc., Ph.D.)
2. The end of a paper

In the former, one typically looks back at a period of a few years, while in the latter, one typically thanks funding agencies, the reviewers, and—in some cases—a select set of people. However, a thesis or a paper is but a small glimpse of what we usually hope to be a long and productive career. Hence, I thought that it would make sense to keep a continuously-updated (or at least something that is being updated every once in a while) list around, which I could use to keep track of all the people that influenced me. Credit where credit’s due!

This list might seem unusually ‘sappy,’ but it serves as an important reminder to me, viz. that ‘No man is an island.’ I am grateful that I was able to meet so many people that helped define and refine me. Any successes are shared, any failures are exclusively mine.

Teachers

After a rocky start in mathematics, I was lucky to be taught by D. Bartholomä and S. Rese in my last years in high school. They provided a glimpse into the wonders of mathematics.

Professors

My love for algebra, topology, and mathematical precision is in no small parts due to my excellent professors who taught undergraduate and graduate courses at Heidelberg University. A big thanks to Markus Banagl, Sigrid Böge, Matthias Kreck, and Rainer Weissauer. While I would never claim to be cut from the same cloth as they are, their enthusiasm for their respective research topics was catching. To this day, I refer back to lessons I learned in their courses—and not all of these lessons pertain directly to the material itself, but also about how to deal with mathematics in general.

(I am particularly happy to have co-authored a publication with Prof. Banagl afterwards; I never expected this, not in my wildest dreams.)

Colleagues

During my Ph.D. studies, I was lucky to stumble over the work of many researchers that inspired me. I am happy to see that some of them became my colleagues even! My way of writing, presenting, and publishing scientific results was markedly inspired by Robert Ghrist and Vidit Nanda. Not only is their work very accessible, it is also written in a way to easily captivate one’s attention. In the absence of ’local’ people (i.e. people in my university or graduate school) that were interested in topological machine learning at this time, their work provided me with knowledge and guidance, even though we never had any personal interactions in those years.

When I started pivoting towards machine learning to work on my vision of ’topological machine learning methods,’ I am first and foremost indebted to Karsten Borgwardt, who provided me with the opportunity to delve into a new domain. I learned a lot from his working style; most importantly, I am grateful that Karsten has the highest standards for comparing methods and assessing their performance. The stringent, precise, and objective way of looking at one’s algorithms has served me well in numerous publications and will continue to do so in the future!

I also learned a lot from the other postdoctoral researchers. We are a pretty diverse bunch in terms of backgrounds, so there are numerous opportunities to learn about different perspectives. I am particularly grateful for Damian Roqueiro’s stoic perspective in all things. His encouragement and support are like a rope that helps me scale the mountains of academia; in more than one way, Damian’s advice is like having a secondary, external conscience that functions way better than my own! Catherine Jutzeler-Walter always served as a kick-starter for project proposals—my first ‘stand-alone’ grant would not exist without her! Thanks to her advice, I also tried my luck at more exclusive grants. While the fruits of this particular labour are yet to appear, I learned at least a few new ways of how to be wrong, and how to sharpen my ideas.

Another pleasant facet of my postdoctoral life at ETH was to meet bright, enthusiastic Ph.D. students and accompany them on their journey to greatness.¹ Christian Bock, Elisabetta Ghisu, Thomas Gumbsch, Max Horn, Michael Moor, Leslie O’Bray, Matteo Togninalli, and Caroline Weis are true powerhouses who continue to amaze me. I am happy that we found so many cool projects to work on together. Even though my incessant rambling about topology and manifolds must be unnerving at times, they never complained so far—and I, for one, look forward to continuing our projects!

I am also very lucky to have met Jannis Born, who not only continues to give me lots of food for thought, but also captivated my interest with impactful applications. His sage advice still serves as a counterbalance to my sometimes lackadaisical attitude when it comes to considering use cases.

After joining ETH, I also had to learn how to grow another professional network. I was therefore very lucky that our work on topology-based graph classification using the Weisfeiler–Le(h)man framework provided a means to get in contact with Roland Kwitt and Guy Wolf. Not only did we find the time to exchange ideas in a very productive atmosphere, I also got to meet them and their research groups in person.²

I do not want to downplay their influence to ‘mere’ co-authors—even though I am very grateful to have them as co-authors, of course—because they also helped me improve my standing in the machine learning community. Together, we organised a NeurIPS workshop and an ICLR workshop. Guy also introduced me to Smita Krishnaswamy, and together, we are working on exciting topics in machine learning for biomedicine. Reading this now, it almost defies belief!

Most of all, and I really cannot stress this enough, all of them³ were kind enough to provide me with references. I would have never expected to have such a strong team of referees that cheered me on and supported me. From the bottom of my heart: thank you!

Then, just as I was starting to establish my own research group, I met Christopher Morris and got to participate in a project on the expressivity of graph neural networks. I learned a lot here, and was floored to be invited to a Dagstuhl Seminar on Graph Embeddings. I am very excited about the future here!

Mentors

I also received quite a lot of mentoring by engaging with the content of other people. Matt Might continues to be a source of inspiration to me; I first discovered his materials while I was still an undergraduate and keep referring back to it. Later on, during my Ph.D., I stumbled over the writings of Veronika Cheplygina. Her outspoken and honest voice about failure and the ‘shadow CV’ was a true boon in times of doubt. As for Matt’s work, I keep revisiting her website for continuous reminders.

Mentees

While I am serving as a supervisor to many people as part of my various affiliations, I also try to ‘give back’ to the academic community at large by volunteering to serve as a mentor to external students. Seeing their progress and achievements makes me truly happy:

• Samuelson Atiba (now a Research Assistant at Cardiff University)
• Sayantan Das (now at a master’s student at Queen’s University Canada)
• Adrish Dey (now a Machine Learning Engineer at Weights & Biases, Inc.)

I am proud of you and grateful for the additional perspective you provide.

Family & friends

It may sound trite, but family & friends are really crucial. Coming from a non-traditional background, it’s always nice to be ‘grounded’ again. I hope that I will never lose my understanding of the relevance of things, especially those that do not pertain to directly to academia. Next to the members of my core family, I am particularly grateful for the support by Lutz Büch and Matthias Maier. Their advice continues to be invaluable and helps me navigate the straits of academia, which sometimes appear shrouded in fog that my intellect fails to pierce. I hope that I will be able to repay them at some point.

To end on a somewhat sappy note, which does not make the sentiment any less true: ‘Whatever I did to deserve you, it couldn’t have been enough.’