Niek Tax
Niek Tax is a Staff Research Scientist and Tech Lead at Meta's Central Applied Science team in London. He focuses on longer-term, foundational work that addresses new opportunities and challenges across Meta, bridging the gap between academic rigour and product teams. Niek has extensive experience overseeing the end-to-end lifecycle of production-grade ML systems, from research to global deployment. His expertise is in uncertainty quantification, including active learning and probability calibration, and he has published articles at NeurIPS and KDD on those topics.
Before joining Meta, Niek worked as an ML engineer at Booking.com and in applied R&D at Philips Research. He holds a PhD in Computer Science from Eindhoven University of Technology, and has authored 35+ peer-reviewed publications with over 2,500 citations.
Session
Your model is well-calibrated on average, but is it calibrated for every subgroup of your users? In this hands-on tutorial you will learn what multicalibration is, why standard calibration methods leave systematic errors hidden in subpopulations, why this matters for ML models in production, and how to fix it in a few lines of code using MCGrad, an open-source Python library that has been battle-tested on hundreds of production models at a large tech company. Attendees will leave with a working notebook they can immediately apply to their own projects.