Zeynep Akata is a Liesel Beckmann Distinguished professor of Computer Science at Technical University of Munich and the director of the Institute for Explainable Machine Learning at Helmholtz Munich. After completing her PhD at the INRIA Rhone Alpes with Prof Cordelia Schmid (2014), she worked as a post-doctoral researcher at the Max Planck Institute for Informatics with Prof Bernt Schiele (2014-17) and at University of California Berkeley with Prof Trevor Darrell (2016-17) and as an assistant professor at the University of Amsterdam with Prof Max Welling (2017-19). Before moving to Munich in 2024, she was a professor of computer science (W3) within the Cluster of Excellence Machine Learning at the University of Tübingen. She received a Lise-Meitner Award for Excellent Women in Computer Science from Max Planck Society in 2014, a young scientist honour from the Werner-von-Siemens-Ring foundation in 2019, an ERC-2019 Starting Grant from the European Commission, The DAGM German Pattern Recognition Award in 2021, The ECVA Young Researcher Award in 2022 and the Alfried Krupp Award in 2023. Her research interests include multimodal learning and explainable AI.

Rahaf Aljundi is a Senior Research Scientist in the AI Core Research department of Toyota Motor Europe and an ELLIS member. Rahaf is among the pioneers in the field of Continual Learning. Her research interests cover Green and Trustworthy AI, focusing on enhancing the efficiency of generative models through updates, context length and architecture while maintaining reliability and controlling hallucination.

Christopher Kanan is an Associate Professor of Computer Science at the University of Rochester. He has created multiple state-of-the-art continual learning algorithms. A major emphasis of his work is aligning continual learning with industry needs, where the systems need to rival systems trained from scratch and learn more efficiently than them. He has been a frequently invited panelist and speaker at continual learning conferences, workshops, and meetings (CoLLAs-2024, ContinualAI Un-Conference-2023, CLVISION-2023, AAAI Bridge Program on Continual Causality-2023, ContinualAI 2021 Lecture Series, CLVISION-2020).

Irina Rish is a Canada CIFAR AI Chair at Mila and a full professor at the Department of Computer Science and Operations Research (DIRO) at the University of Montreal. She holds the Canada Excellence Research Chair in Autonomous AI. Rish’s extensive research career spans multiple AI domains, from automated reasoning and probabilistic inference in graphical models, to machine learning, sparse modeling, and neuroscience-inspired AI. Her current research endeavors concentrate on continual learning, out-of-distribution generalization, robustness; and understanding neural scaling laws and emergent behaviors (w.r.t. both capabilities and alignment) in foundation models – a vital stride towards achieving maximally beneficial Artificial General Intelligence (AGI).

Ludwig Schmidt is an assistant professor at Stanford University and a member of the technical staff at Anthropic. Ludwig’s research interests revolve around the empirical foundations of machine learning, often with a focus on datasets, reliable generalization, multimodality, and large models. Recently, Ludwig’s research group contributed to open source machine learning by creating OpenCLIP, OpenFlamingo, and the LAION-5B dataset. Ludwig completed his PhD at MIT and was a postdoc at UC Berkeley. Ludwig’s research received a new horizons award at EAAMO, best paper awards at ICML & NeurIPS, a best paper finalist at CVPR, and the Sprowls dissertation award from MIT.