Confirmed speakers are listed here. We will update this list as the program is finalised.
Gisbert Schneider, a full professor at ETH Zurich and an Elected Fellow of the University of Tokyo, is a leading voice in AI-driven drug discovery. His research explores adaptive biosystems and the use of AI to accelerate scientific discovery. With a career that includes leadership roles at the Pharmaceuticals Division at Roche, Goethe University, and the Singapore-ETH Centre, he brings a unique blend of industry and academic expertise. He has been widely recognized for his foundational contributions to the field, receiving prestigious honors such as the Ernst Schering Prize and the Gmelin-Beilstein Medal.
Jianying Hu is an IBM Fellow, Director of HCLS Research, and Global Science Leader of AI for Healthcare at IBM. She is also Adjunct Professor of Medicine at Icahn School of Medicine at Mount Sinai. Dr. Hu joined IBM in 2003 after working at Bell Labs. She has over 30 years of experience conducting and leading research on machine learning, with recent focus on AI enabled acceleration of scientific discovery in health. Dr. Hu has served on many editorial and advisory boards, most recently on the National Academy of Medicine’s Committee on Establishing a Framework for Emerging Science, Technology and Innovation in Health and Medicine, and the External Advisory Board of the NIH AIM-AHEAD Program. Dr. Hu is a fellow of the American College of Medical Informatics, International Academy of Health Sciences Informatics, IEEE, and the International Association of Pattern Recognition.
Djork-Arné Clevert is a computational scientist with strong expertise in developing machine learning methods (published at NIPS, ICML and ICLR) and extensive experience in analyzing high-dimensional data. He is a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research is related to probabilistic modeling, deep learning (invented ELUs and RFNs), biclustering, recommender systems, Bayesian filtering technique for controlling the false discovery rate, biomarker development, toxicogenomics, drug repositioning and chemoinformatics.
Patrick Schwab is a Senior Director of Machine Learning and Artificial Intelligence and Head of the Biomedical AI group at GSK.ai. His work aims to advance personalised medicine by utilising machine learning, computational systems biology methods and large-scale health data, such as genetics, multi-omics, cell-based assays, and continuous measurements from smart devices and electronic health records, to better understand and treat complex diseases.
J.B. Brown is a Principal Scientist at Boehringer-Ingelheim and adjunct Associate Professor in the Kyoto University Center for Cancer Immunotherapy and Immunobiology. He is the recipient of an award in a Boehringer-Ingelheim AI contest in the category ‘Most Technical’ . His daily routine involves designing molecules and innovation of drug discovery technologies.
Jake Taylor-King is co-founder and Chief Innovation Officer at Relation Therapeutics, where his role looks across strategy, science, and technology. Jake worked with a number of startups to understand how machine learning could be utilized to derive biological insights from novel experimentation. Working at Juvenescence ultimately led to the formation of Relation Therapeutics with co-founders Benjamin Swerner and Charlie Roberts. Relation is a new type of TechBio company using machine learning to integrate genetics, single cell profiling, and functional genomics. Our top priority is to use the Relation platform to deliver a pipeline of therapeutics to treat select musculoskeletal disorders.
Yoshihiro Yamanishi is a full professor at Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Japan. He received his Ph.D. from Kyoto University in 2005. He was post-doctoral fellow at Ecole des Mines de Paris from 2005 to 2006. He was assistant professor at Kyoto University from 2006 to 2007. He was permanent researcher at Mines ParisTech and Curie Institute from 2008 to 2012. He was associate professor at the Medical Institute of Bioregulation, Kyushu University from 2012 to 2018. He was professor at Department of
Bioscience and Bioinformatics, Kyushu Institute of Technology from 2018 to 2023. He is working on machine learning in bioinformatics, chemoinformatics, and drug discovery.
Le Song is the CTO of GenBio AI, and also a full professor of Mohamed bin Zayed University of AI (MBZUAI). He was a tenured associate professor of Georgia Institute of Technology, and the conference program chair of ICML 2022. He is an expert in AI and AI for Science and has won many best paper awards in premium AI conferences such as NeurIPS, ICML and AISTATS. Recently, his work on using large language models for protein structure predictions has been featured as the cover story in Nature Machine Intelligence.