Biography:
Augustin Žídek works as a Research Engineer at Google DeepMind and has been a member of the AlphaFold team since 2017. He studied Computer Science at the University of Cambridge. He enjoys working at the boundary of research and engineering, hiking, playing musical instruments and fixing things.

Biography:
Dr. Fátima Sánchez Cabo graduated in Mathematics from the Complutense University of Madrid in 2000. From there she moved to the University of Manchester where she obtained a grant from the BBSRC to develop her doctoral work on statistical analysis and mathematical data modeling of microarrays. In 2005 she joined the Institute of Genomics and Bioinformatics of the Polytechnic University of Graz, Austria, where she developed her work first as a postdoctoral researcher and later as an associate professor. Since 2008 she has been working at the CNIC, leading the Bioinformatics Unit since 2017 and the Computational Systems Biomedicine Lab since 2025. Dr. Sánchez-Cabo has published more than 100 articles in peer-reviewed journals and is especially interested in the use of AI algorithms to enhance the understanding of biological systems in the context of cardiovascular disease and aging. Likewise, she has a strong commitment to the training of researchers in areas related to bioinformatics. Since 2021 is associate professor of the Autonoma University in Madrid. She is also the vicepresident of the Spanish Society for Computational Biology and Bioinformatics and member of the Advisory Board of ELIXIR.

Biography:
Babak Ehteshami Bejnordi is a Research Scientist at Qualcomm AI Research in the Netherlands, leading a research group focusing on conditional computation for efficient deep learning. His primary research focus lies in the realm of efficient Deep Learning for Large Language Models (LLMs) and Computer Vision. His recent research works have been in the areas of Efficient Autoregressive decoding in LLMs, Mixture of Experts, Multi-Task Learning, and Continual Learning. Babak obtained his Ph.D. in machine learning for breast cancer diagnosis from Radboud University in the Netherlands. During his Ph.D., he organized the CAMELYON16 challenge on breast cancer metastases detection which demonstrated one of the first medical diagnostic tasks in which AI algorithms outperform expert pathologists. Before joining Qualcomm he was a visiting researcher at Harvard University, BeckLab, and a member of the Broad Institute of MIT and Harvard. He has been the organizer of the Qualcomm Innovation Fellowship Program in Europe since 2019.

Biography:
Stefan Haufe is a joint Associate Professor of Uncertainty, Inverse Modeling, and Machine Learning at Technische Universität Berlin and Physikalisch-Technische Bundesanstalt Berlin. He also serves as a Group Leader at Charité - Universitätsmedizin Berlin, where his research focuses on developing advanced computational methods. His group specializes in signal processing, inverse modeling, and machine learning techniques for analyzing neuroimaging and other medical data. This work aims to address challenges in understanding complex physiological processes and improving medical diagnostics and treatment strategies. Additionally, he has a keen interest in model interpretation and explainable artificial intelligence, striving to make machine learning applications more transparent and interpretable for practical use in medical and scientific contexts. Through collaborations with interdisciplinary teams, Stefan Haufe contributes to advancing both theoretical and applied aspects of computational neuroscience and biomedical data analysis.

Biography:
Grigory is the CTO and co-founder of Intento, a company dedicated to advancing machine learning and artificial intelligence technologies. Before founding Intento, he gained extensive experience in both industry and academia, working at Yandex and the Higher School of Economics. With a career spanning over 25 years in software engineering, Grigory has dedicated nearly 20 years to data analysis, artificial intelligence, and machine learning, building expertise in these cutting-edge fields. Since 2011, Grigory has been deeply involved in deep learning, contributing to the development and application of these transformative technologies. His work bridges theoretical advancements and practical implementations, making him a leader in the AI community. Grigory is also a Google Developer Expert in Machine Learning, a recognition of his deep technical knowledge and active contributions to the developer ecosystem.

Biography:
Gergely Neu is a research assistant professor at the Pompeu Fabra University, Barcelona, Spain. He has previously worked with the SequeL team of INRIA Lille, France and the RLAI group at the University of Alberta, Edmonton, Canada. He obtained his PhD degree in 2013 from the Budapest University of Technology and Economics, where his advisors were András György, Csaba Szepesvári and László Györfi. His main research interests are in machine learning theory, with a strong focus on sequential decision making problems. Dr. Neu was the recipient of a Google Faculty Research award in 2018, the Bosch Young AI Researcher Award in 2019, and an ERC Starting Grant in 2020.

Biography:
Patricia Schmidtova is a Ph.D. student in Natural Language Processing (NLP) at Charles University, focusing on semantic accuracy in natural language generation and its evaluation methodologies. Her work has earned her Best Paper Awards at EACL and INLG. With seven years of industry experience, she has specialized in implementing NLP components for task automation in the banking sector. In her talk, Patricia will share her experience designing an automatic evaluation protocol to assess the reliability of millions of summaries generated by large language models (LLMs).

Biography:
A principal ML engineer and an ex startup CTO working on modern search and recommendations problems. A pragmatic fan of open-source software, functional programming, LLMs and performance engineering.

Biography:
Martin Genzel is a Staff Machine Learning Researcher at Merantix Momentum, developing deep learning solutions for tabular and time-series data in real-world applications. As an applied mathematician by training, he got his Ph.D. from TU Berlin working on compressed sensing and high-dimensional signal processing. In his postdocs at Utrecht University and the Helmholtz Centre Berlin, he explored deep learning techniques for ill-posed inverse problems and computational imaging.