Feb 12, 2025: Yay, our work Max-Rank: Efficient Multiple Testing for Conformal Prediction on permutation-based multiple testing was accepted to AISTATS 2025! That means Thailand in May, who thought machine learning can take you to so many places. We switched up the paper name to better reflect our contributions, and give a simple solution to multiple testing issues. I’ve been collaborating on two other projects in the meantime, with preprints coming soon (the usual IP things). Keywords: geometric deep learning (yup) and sequential hypothesis testing!

Jan 15, 2025: Hello Tokyo, また会えて嬉しいよ！ I’ll be spending the next three months at RIKEN AIP, Japan’s national research institute. Specifically, I’ll be learning about a Bayesian perspective on optimization through variational learning as part of the Approximate Bayesian Inference Team, under leadership of Emtiyaz Khan. They are taking a very exciting perspective on Bayesian deep learning, go check out their latest IVON optimizer that scales to GPT-tier models!

Dec 18, 2024: Really enjoyed NeurIPS, not only to present our own work but to visit a bunch of related posters and talk to researchers from across the globe, incl. Yaniv Romano and his team from Technion, Osvaldo Simeone and his UCL group, students from UC Berkley, Carnegie Mellon, Johns Hopkins, JP Morgan Research, KAIST, etc. So many smart people doing interesting work! Vancouver is also pretty, and warmer than expected. Side topic: I’m co-supervising three master students on their thesis, jointly with Rajeev and Xiaoyan on topics ranging from recommender systems to uncertainty for SAM.

Nov 8, 2024: Attended the E-Day organized by Rianne de Heide, and hosted next door at CWI. It was fun to learn about some of the (local) research on E-values and safe statistics, and I particularly enjoyed the talk by Glenn Shafer, one of the founding fathers of game-theoretic statistics. Awesome that he joined.

Oct 10, 2024: ECCV was fun, and I attended a few talks on topics far outside my own (e.g. 3D modelling) that really impressed me with their demos. Uncertainty researchers, including myself, should try harder to really get their ideas – motivated from a reliability angle – into practice. Also, thanks Xiaoyan for a great snapshot from my oral talk. Cool side announcement: after slowly being in the works since the MLSS in Okinawa, I’m happy to confirm I’ll be doing a research stay with Emtiyaz Khan and the Approximate Bayesian Inference Team at RIKEN AIP in Tokyo, starting coming January. I’m always trying to learn new things, and explore all facets (incl. the Bayesian angle) of uncertainty quantification!

Sept 26, 2024: Happy to have our work on risk control for early-exiting accepted at NeurIPS 2024! After promoting its usefulness for efficient but safe inference already at ICML this summer, we are also continuing the message in (wintery?) Vancouver. Side topic #1: The COPA symposium in Milan was great, and I was impressed how positive, down-to-earth and tight-knit the conformal community is. Would love to attend again should the opportunity arise. Side topic #2: After quite the lead time finally got admitted to the ELLIS PhD program! I’m on the industry track, since we’re working with Bosch.

Aug 21, 2024: Our accepted paper at ECCV received an Oral (top 3%), providing us with an opportunity to present our motivation to a wider audience! Seems like the vision community is indeed excited about the possibilities of conformal prediction to advance safety aspects in the field. We will also be presenting at the ECCV Workshop on Uncertainty Quantification for Computer Vision amidst the uncertainty community.

Jul 24, 2024: I’m glad to announce a new preprint, great joint work with my lab mate Metod Jazbec. Our paper titled Fast yet Safe: Early-Exiting with Risk Control addresses how to select the exit threshold for early-exit models with performance guarantees via risk control. I’m presenting our work at two ICML workshops this week: one focused on probabilistic inference (SPIGM), and one on efficient inference (ES-FOMO). Hoping this will get both communities excited (and beyond) about the usefulness of statistical ML for all kinds of applications!

Jul 8, 2024: Happy to have our paper titled Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction accepted at this year’s European Conference on Computer Vision (ECCV)! We extended our workshop paper from last year to incorporate classification uncertainty using a two-step conformal approach – looking forward to sharing it with the vision community. And yes, that will be me visiting Milan twice in September.

May 23, 2024: On the side, I’ve been working with a small student team on an idea related to conformal prediction for time series. We’ve fleshed it out into a full paper titled Conformal time series decomposition with component-wise exchangeability, which has been accepted at this year’s COPA symposium, the dedicated venue for conformal prediction research! Looking forward to attending the conference in Milan in September, and hopefully meeting some of the field’s founding figures like Prof. Vladimir Vovk and Prof. Alexander Gammerman.

Mar 18, 2024: Good times attending the Machine Learning Summer School in Okinawa at OIST, with particularly interesting talks by Shai Ben-David and Pierre Alquier on PAC and PAC-Bayes respectively – the evolution of generalization bounds continues. The OIST campus is breath-taking, and Okinawa seems like a bit of a hidden gem for beach getaways. Continuing the adventure with travels across Japan (Tokyo, Fuji, Kyoto, Osaka) until end of month, including a short visit to the RIKEN AIP in Tokyo!

Jan 29, 2024: Due to Eric’s switch from the University of Amsterdam to Johns Hopkins in Baltimore, I am happy to have Christian Naesseth join as a co-supervisor alongside Eric. Christian has done very interesting work in probabilistic modeling and we share a common interest in statistical ML topics such as conformal prediction and e-values – here’s to a nice collaboration!

Dec 4, 2023: We’ve published a preprint on a multiple testing correction dedicated to conformal prediction applications, titled A powerful rank-based correction to multiple testing under positive dependency.

Nov 20, 2023: I am glad to see my student Derck Prinzhorn receive an Amsterdam AI Thesis award for his bachelor thesis on conformal prediction, and present his work to the local community. He worked diligently at the intersection of multiple complicated topics – well done!

Oct 23, 2023: I will be helping out as a teaching assistant for the courses on Human-in-the-loop ML (MSc) and Introduction to ML (BSc) for the winter teaching block until the end of the year – hoping to excite some new faces to the field!

Oct 7, 2023: Participated in ICCV 2023 in Paris to present our poster on ‘Adaptive Bounding Box Uncertainty via Conformal Prediction’ at the Workshop on Uncertainty Quantification in Computer Vision – thanks to the organizers! It was fun to see the thriving ML community and the various research directions, particularly in the computer vision domain. Notably, there was a lot of work on multi-modality and foundation model finetuning. Some interesting thoughts on the current state of the field were given in the Quo Vadis, Computer Vision workshop by the likes of David Forsyth, Bill Freeman, and Jitendra Malik – is “putting language into vision” the best approach? Also got to catch Yann LeCun in person at the Meta booth!

Sept 1, 2023: I was happy to participate in the ELLIS Doctoral Symposium 2023 in Helsinki, Finland! A lot of fun activities (and sauna galore) and great to see so many fellow ML PhD students enthusiastically present their work. Particularly interesting discussions on calibration for regression and conformal prediction for time series – thanks and maybe see you next year!

Aug 23, 2023: The AMLab yearly retreat was good fun, with karaoke and escape room action as well as plenty of socializing – I’m happy to be working with such a nice crowd!

Aug 11, 2023: The research paper Uncertainty Quantification for Image-based Traffic Prediction across Cities, a resulting work from my master thesis in collaboration with the Mobility Information Engineering Lab at ETH Zurich, is finally publicly available on arXiv!

Aug 9, 2023: Our workshop submission on ‘Adaptive Bounding Box Uncertainty via Conformal Prediction’ got accepted to the Workshop on Uncertainty Quantification in Computer Vision at ICCV 2023 – looking forward to presenting our work in Paris!

Aug 1, 2023: My bachelor student Derck Prinzhorn successfully graduated with a thesis on the topic of Benchmarking conformal prediction methods for time series regression – great job and congratulations!

Apr 4, 2023: Started a bachelor thesis supervision project on conformal prediction for time series, as well as TA’ing the Deep Learning 2 course with a module on uncertainty quantification. I am also supervising a course project on Bayesian subnetwork inference.

Mar 28, 2023: Had an interesting time at the Bosch Center for Artificial Intelligence HQ in Renningen in the context of a lab visit organized by the Delta Lab. It was great to meet my Bosch co-supervisors in person and see how our research can potentially translate to Bosch applications, such as in autonomous driving and robotics.

Feb 16, 2023: Participated in the CWI Machine Learning Theory Bootcamp just next door to the University campus. I particularly enjoyed the talk by Bob Williamson on imprecise probability.

Oct 10, 2022: Joined the Amsterdam Machine Learning Lab (AMLab) as a PhD candidate supervised by Eric Nalisnick!