Focus area: Foundations of ML
Dr. Kuldeep Singh is the CEO and Co-founder of Eka Labs AI, a deeptech startup building hyper-specialized Small Language Models (SLMs) for domain-specific intelligence.
Previously, he served as Director of AI Technology at Cerence AI, where he led the development and scaling of Generative AI and LLM-powered products from 0→1, enabling millions of user interactions across leading automotive OEMs, including Volkswagen, Audi, Toyota, Geely, JLR, and Ford, as well as Tier-1 suppliers. He also contributed to the development of Cerence’s automotive-grade LLM (CaLLM) and its deployment into premium infotainment systems, shaping next-generation voice-driven mobility experiences.
Dr. Singh holds a Ph.D. from the University of Bonn as a Marie Curie Fellow under Prof. Dr. Sören Auer. He later led Conversational AI research at Fraunhofer IAIS, Sankt Augustin in collaboration with Prof. Dr. Jens Lehmann. He has authored 50+ publications in top AI venues such as ICLR, AAAI, ACL, and The Web Conference, and holds two U.S. patents. His research focuses on knowledge distillation and Reinforcement learning for SLMs and graph representation learning, and he actively contributes in program committee of leading AI conferences.
Jilles Vreeken is tenured faculty at the CISPA Helmholtz Center for Information Security, honorary professor at Saarland University, and fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. in 2009 from Utrecht University, was a post-doctoral researcher at Antwerp University until 2013, and both independent research group leader (W2) at Saarland University and senior researcher at the Max Planck Institute for Informatics until 2018. His research focuses on developing well-founded theory and efficient methods that give clear and actionable insight into large, complex data and models. In more general terms, he likes data mining, machine learning, and causal inference.
Dominik Janzing is a Principal Research Scientist at Amazon Research Tübingen, Germany, where he works on causal inference for monitoring AWS cloud services.
Education / degrees:
– 1995: “Diplom” in Physics, Universität Tübingen
– 1998: Dr. in Mathematics, Universität Tübingen
– 2006: “Habilitation” (teaching permission) in Computer Science, Universität Karlsruhe (now KIT)
Research topics:
– 1995 – 2006: quantum information and thermodynamics
– Since 2003: causal inference – foundations and applications
His contributions range from the formalization of the independence of mechanisms principle to the formalization of root cause analysis, quantification of causal influence, and evaluation of causal discovery methods without ground truth. The textbook Elements of Causal Inference received the ”Causality in Statistics Education Award” from the American Statistical Association.
Selected publications:
1) Janzing, Wocjan, Zeier, Geiss, Beth: Thermodynamic cost of reliability and low temperatures, tightening Landauer’s principle and the second law, Int. J. Physics, 2000.
2) Janzing and Schölkopf: Causal inference using the algorithmic Markov condition, IEEE TIT 2010.
3) Janzing, Grosse-Wentrupp, Balduzzi, Schölkopf: Quantifying causal influences, Annals of Statistics 2013
4) Peters, Janzing, Schölkopf: Elements of Causal Inference, MIT Press 2017.
5) Budhathoki, Minorics, Blöbaum, Janzing: causal structure-based root cause analysis of outliers, ICML 2022.
6) Faller, Vankadara, Mastakouri, Locatello, Janzing: Self-compatibility: evaluating causal discovery without ground truth, AISTATS 2024.
I am currently a PhD student in the group of Fred Hamprecht at the Interdisciplinary Centre for Scientific Computing (IWR) of Heidelberg University. My background is physics and I am primarily interested in how we may use our intuition and tools from theoretical physics to understand and improve machine learning methods. I first became interested in this during my masters thesis where (among other things) I worked on a grand canonical MCMC sampling algorithm for inference problems. [1] In my PhD, I am working on inference problems on manifolds particularly the simplex (ie inference problems where the parameters are distributions themselves). The physical methods we use mostly stem from differential geometry (as in general relativity and information geometry) and our applications are also in physics, namely cosmology.
I am a third year ELLIS Ph.D. at TU Darmstadt (UKP Lab), Germany and the University of Cambridge, UK. I am supervised by Prof. Iryna Gurevych and Prof. Anna Korhonen. My research interests lie at the intersection of NLP and ML. Specifically, I am interested in:
– Cross-lingual generalization (e.g., plasticity, early period of training, memorization vs generalization, spurious correlations etc.)
– Culturally aware and adapted NLP
– Multimodality
Previously, I studied at the University of Toronto, where I obtained both of my undergraduate and master’s degrees. My master’s research was under the supervision of Prof. Brendan Frey (PSI Lab, Toronto ML Group). I have been working in the industry for nearly a decade in the domain of NLP before I went back to school. I worked at Canadian start-ups like Meta (acquired by CZI), Wattpad (acquired by NAVER WEBTOON), and ElementAI.
Batuhan Koyuncu is currently a Ph.D. candidate in Computer Science at Saarland University, affiliated with the ELLIS Ph.D. program and co-advised by Prof. Isabel Valera and Prof. Ole Winther. His research lies at the intersection of deep generative modeling and probabilistic methods for spatial and time-series data. Recently, his work has focused on developing interpretable and scalable architectures for nowcasting and forecasting, with applications in macroeconomics and personalized healthcare. Among his most influential publications is “Variational Mixture of Hyper Generators for Learning Distributions Over Functions” (ICML 2023, with Sanchez-Martin, Peis, Olmos, and Valera), which introduced a flexible generative framework for modeling function distributions. This line of research continues with “Hyper-Transforming Latent Diffusion Models” (ICML 2025, with Peis, Valera, and Frellsen), extending the capabilities of generative models over functions through structured diffusion-based techniques.
I am an ELIZA and ELLIS Ph.D. student at the Technical University of Munich and TU Darmstadt, with co-supervision from the University of Oxford. My research focuses on unsupervised scene understanding in {2, 3, 4}D and representation learning. I am supervised by Daniel Cremers (CVG), Stefan Roth (VisInf), and Christian Rupprecht (VGG).
Prior to starting my Ph.D., I was a research intern at NEC Laboratories America (Princeton), where I worked with Biplob Debnath on controlling standardized image and video codecs for deep vision models using self-supervised learning.
During my studies, I worked at the Self-Organizing Systems Lab with Tim Prangemeier on 2D and 3D segmentation for biomedical applications, as well as generative approaches for live-cell in silico experiments. I also collaborated with the Artificial Intelligent Systems in Medicine Lab (led by Christoph Hoog Antink), focusing on ECG analysis using deep learning.
Bernhard Schölkopf’s scientific interests are in machine learning and causal inference. He has applied his methods to a number of different fields, ranging from biomedical problems to computational photography and astronomy. Bernhard studied physics and mathematics and earned his Ph.D. in computer science in 1997, becoming a Max Planck director in 2001. He has (co-)received the Berlin-Brandenburg Academy Prize, the Royal Society Milner Award, the Leibniz Award, the BBVA Foundation Frontiers of Knowledge Award, and the ACM AAAI Allen Newell Award. He is Fellow of the ACM and of the CIFAR Program “Learning in Machines and Brains”, a member of the German Academy of Sciences, and a Professor at ETH Zurich. He helped start the MLSS series of Machine Learning Summer Schools, the Cyber Valley Initiative, the ELLIS society, and the Journal of Machine Learning Research, an early development in open access and today the field’s flagship journal. In 2023, he founded the ELLIS Institute Tuebingen, and acts as its scientific director.