Siu Lun Chau

Michele is an outstanding collaborator and mentor—deeply knowledgeable in maths and always generous with his time and insights. If you’re interested in developing next-generation uncertainty quantification models grounded in elegant mathematical rigour, please apply!

🙏 Honoured to receive the IJAR Young Researcher Award 2025 at #ISIPTA2025! Grateful for the recognition of our efforts to bridge the mathematical rigour of imprecise probability with ML’s computational challenges. Excited to keep pushing forward! 💪

🚨 PhD opportunity! I’m recruiting a PhD student with a strong background in math/stats/CS to join my group at NTU Singapore 🇸🇬 Start: Jan or Aug 2026 Topic: Foundations of Epistemic Uncertainty in ML 🧠🔍 📌 Details: chau999.github.io/group/ RT appreciated!

🚨 Exciting news! Our new paper on “nested expectation with kernel quadrature” just got accepted to #ICML2025! 🎉 Sadly, I won’t be able to attend due to visa issues 😔 — but my amazing collaborator Masha Naslidnyk will be presenting on our behalf. Don’t miss it if you’re there!

We've written a monograph on Gaussian processes and reproducing kernel methods. arxiv.org/abs/2506.17366

I’m hiring multiple PhD students and one postdoc at NTU Singapore, starting in Spring or Fall 2026, to push the frontiers of robot planning, learning, and embodied AI. Details are available here: yoonchangsung.com/opportunity Thank you for your support in sharing this opportunity!…

Thrilled to launch the Rational Intelligence Seminar Series (RISS), starting June 18, 2025! Join us to engage in lively discussions in the first session, “Prediction, Potential Outcomes, and Performativity,” by Sebastian Zezulka. ri-lab.org/riss/

🙋Excited to be speaking at NUS DSDS tomorrow about our recent work "Integral IMPRECISE Probability Metrics"! If you are curious about what imprecise probabilities are, and how they are (gradually) changing the way we (at least my) view (epistemic) uncertainty in ML? Join us!

In the presence of noisy data, the learner faces epistemic uncertainty (EU) at test time which, in many cases, cannot be resolved on the basis of training data alone. What to do then? @sabinasloman, @samikaski and I tried to answer this in arxiv.org/abs/2505.23496 1/3

🚨 Ever wonder why we have kernel mean embeddings but not kernel *quantile* embeddings? What do we gain (a lot) or lose (not much) if so? Wait, what even are quantiles in RKHS? 🤯 Curious? 👉 rb.gy/6ilcbp Joint work with Masha Naslidnyk, @fx_briol , @krikamol!

🚨 🇪🇺 Seeking a postdoc opportunity under the 2025 call for the Marie Sklodowska-Curie Actions (MSCA 2025) Postdoctoral Fellowships? 😎 Come work with the Rational Intelligence Lab at CISPA in Saarbrücken, Germany. 🔗 ri-lab.org/assets/pdf/ri-… RT Please 🙏

🥳 Just managed to finish the first draft of this work after almost four years of work 😅 (Im)possibility of Collective Intelligence arxiv.org/abs/2206.02786 Any feedback is highly appreciated 🙏

Happy to share I've been nominated Action Editor of @TmlrPub. If you have papers on • Imprecise Probabilistic Machine Learning, • Aleatoric vs Epistemic Uncertainty Quantification and Representation, • Conformal Prediction, • Evidential Learning, submit to TMLR! 😀

🎉Thrilled to share that our paper “Truthful Elicitation of Imprecise Forecasts” has been accepted as an Oral presentation at #UAI2025! 🙌 Check it out: arxiv.org/pdf/2503.16395 @_anurags14 @krikamol

Happy to share that our paper on "Active Reward Modeling" has been accepted to ICML 2025! #ICML2025 The part I like the most about the project is its simplicity! Huge thanks to my amazing co-authors @ShenRaphael @HolarisSun More to come! For more detailed 🧵 see 👇