Christopher Bishop is a Microsoft Technical Fellow and Founder of Microsoft Research AI for Science. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. Bishop was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.

Shirley Ho is an American astrophysicist and machine learning expert, currently Group Leader at Simons Foundation and a Professor at New York University, with a visiting appointment at Princeton University.
Group Leader, Cosmology X Data Science, CCA, Simons Foundation
Professor, Department of Physics & Center for Data Science, NYU

Gilles Louppe is computer scientist and professor at the University of Liège, Belgium. He is specializing in artificial intelligence for science and known to be one of the key contributors to scikit-learn. His research efforts are focused on the development of deep learning models and statistical methods for scientific applications, with a strong emphasis on (Bayesian) inverse problems in the physical sciences. He is group leader of the Science with AI Lab (SAIL) at the Montefiore Institute, where he works on a variety of projects across sciences, including particle physics, astronomy, and weather science (see the SAIL group page).

Tobias Hodel is Associate Professor of Digital Humanities at the University of Bern, working at the intersection of history, machine learning, and digital methods.
His research focuses on machine learning in the humanities, text processing, information extraction, digital history, and critical algorithm studies.
He has contributed to computational approaches for archival and historical sources, including handwritten text recognition, annotation, named entity recognition, and content extraction. Before joining Bern, he worked on the READ project and digital editions at Zurich-based institutions, linking historical scholarship with scalable document-processing workflows.

Chris Dallago is Senior Research Scientist in Digital Biology, NVIDIA, and Visiting Assistant Professor in Biostatistics & Bioinformatics, and Cell Biology at Duke University. His work focuses on fast tools for large-scale biological data analysis, such as alignment and prediction software, machine learning models that learn general biological representations, including protein and nucleotide LLMs, and benchmarks, datasets, and frontier tools for evaluating and advancing computational programmable biology.