Investigating the science of mini golf
Preliminary findings: postdocs and professor dominate PhD students by a wide margin.
Johannes Lederer
Data Science & Artificial Intelligence
Latest posts
📝 NeurIPS 2026
NeurIPS has long been one of the leading venues for machine learning and computational neuroscience, and it’s an honor to contribute to the review process at this level. Amid ongoing discussions about review quality, reviewer workload, and the role AI should—or should not—play in peer review, I’m looking forward to doing my part to support [ ]
🌐 Call for Contributions & Poster Competition
We’ve been taught that unreliability is failure in mobility. Delays, disruptions, missed connections: things to eliminate. But what if that’s only half the story? What if unreliability is also what makes systems more adaptive, resilient, and human? This workshop brings together perspectives from data science, AI, transport planning, and social science to discuss a simple [ ]
🎙️ Podcast: Do election graphics mislead us?
A number of important elections take place in Germany this year 🇩🇪🗳️. This includes five state elections, with around 16 million eligible voters 👥. Graphics about polls, forecasts, and election results are appearing everywhere 📊 — but be careful ⚠️: many of them are misleading. Sometimes they are presented sloppily, sometimes they are deliberately manipulated [ ]
AI research in Nature Communications Engineering
Our new paper, “Microstructure-informed constitutive modeling of granular media under multidirectional loading: from particle-scale to continuum,” has been accepted for publication in Nature Communications Engineering. 🚀📘 This work highlights how theoretical insight, AI expertise, and applied engineering can work hand in hand to push the boundaries of civil engineering research. 🧠🤖🏗️ 👏 Huge…
Deep-learning algorithms don’t work…
… and yet they do. 🤖✨ Deep learning relies on highly complex loss functions. Algorithms should optimize them, but we know they can’t. So why are the results still so impressive? Our new paper offers a mathematical explanation. 📘🧠 We rigorously prove that deep-learning algorithms don’t actually need to find the true optimum. Being close [ ]
🚀 Additional Funding for High-Dimensional Time Series Research
Time-series analysis is one of the core pillars of statistics. However, the high dimensionality and sheer size of today’s datasets pose new statistical and algorithmic challenges. Our project tackles these challenges while also addressing classical questions like stability and stationarity. More broadly, we aim to contribute to the modernization and expansion of the theoretical and [ ]
Back to the roots
Two decades ago, I began studying physics at ETH Zürich, driven by the desire to understand the world around us. Along the way, I drifted into the depths of mathematics and the excitement of AI — and somewhere in that journey, physics slipped a bit into the background. Recently, though, my group and I have [ ]
🎲 Mit KI zum Spiel des Jahres? 🎲
Spieleautoren erschaffen Welten, erfinden Regeln und lassen Fantasie lebendig werden – doch wie sieht der Alltag hinter diesem Traumberuf wirklich aus? Reiner Knizia, einer der erfolgreichsten Spieleautoren der Welt, gibt exklusive Einblicke: 💡Welche Rolle spielt KI schon heute in der Spieleentwicklung? 💡Versteht KI Spaß? 💡Und wie entsteht aus einer vagen Idee ein fertiges Spiel, das [ ]
A New Type of Sparsity for More Efficient Matrix-Matrix Multiplications
We all love sparsity: it makes computations faster, guarantees tighter, and interpretations easier. In our paper, , which will appear in TMLR, we introduce a new type of sparsity, which we term cardinality sparsity . We show that cardinality sparsity has all the usual perks, and more importantly, we demonstrate that it is also a very [ ]