by Max Welling | CTO | CuspAI & Professor | University of Amsterdam
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Prof. Dr. Max Welling is a full professor and research chair in machine learning at the University of Amsterdam and a Merkin distinguished visiting professor at Caltech. He is co-founder and CTO of the startup CuspAI in Materials Design. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he served on the founding board. His previous appointments include Partner and VP at Microsoft Research, VP at Qualcomm Technologies, professor at UC Irvine.
He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft. He then switched fields to focus on machine learning, first as a postdoc at Caltech under supervision of prof. Pietro Perona and then as postdoc under supervision of Nobel laureate prof. Geoffrey Hinton at UCL & U. Toronto.
Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015, he is co-founder of the European Lab for Learning and Intelligence Systems (ELLIS) and served on its board until 2021, he has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair and co-founder of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010, and the 10 year Test of Time awards at ICML in 2021 and ICLR in 2024.
by Baptiste Roziere | Researcher and Code Generation Team Leader | Mistral AI
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Baptiste is leading the code generation team at Mistral AI. Previously, he was a research scientist in the codegen team at Meta AI in Paris working. He contributed to Llama and led Code Llama.
During his PhD at Meta AI and Université Paris Dauphine, Baptiste conducted research on unsupervised translation of programming languages and model pre-training for code. His work was featured in dozens of news articles in more than ten languages. Prior to that, Baptiste worked as an applied scientist in the dynamic advertising team at Amazon.
Biography:
Andrew Fitzgibbon is an Engineering Fellow at Graphcore, working on the future of computing hardware and programming for AI and numerical computing.
He is best known for his work on computer vision: he was a core contributor to the Emmy-award-winning 3D camera tracker “boujou”, having co-founded the company “2d3”, with Andrew Zisserman, Julian Morris, and Nick Bolton; at Microsoft, he introduced massive synthetic training data for Kinect for Xbox 360; and was science lead on the real-time hand tracking in Microsoft's HoloLens. His research interests are broad, spanning computer vision, graphics, machine learning, neuroscience, and most recently programming languages. He has published numerous highly-cited papers, and received many awards for his work, including ten “best paper” prizes at various venues, the Silver medal of the Royal Academy of Engineering, and the BCS Roger Needham award. He is a fellow of the Royal Academy of Engineering, the Royal Society, the British Computer Society, and is very proud to be a Distinguished Fellow of the British Machine Vision Association.
Before joining Graphcore in 2022, he spent 15 years at Microsoft, and before then, he was a Royal Society University Research Fellow at Oxford University, having previously studied at Edinburgh University, Heriot-Watt University, and University College, Cork.
by Stefan Haufe | Head of the UNIML group & Professor | Technische Universität Berlin
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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.
by Patrícia Schmidtová | PhD student | Charles University
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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).
by Roman Grebennikov | Principal Engineer | Delivery Hero
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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.