Keynote Speakers

Jürgen Bajorath
University of Bonn, Germany

Topics

Artificial Intelligence in the Life Sciences, Machine Learning in Chemistry, Drug Discovery

Biography

Jürgen Bajorath received a diploma and PhD in biochemistry (1988) from the Free University in West-Berlin. He was a postdoctoral Fellow with Arnie Hagler in San Diego where he began to work on computational drug design. From 1990-2004, Jürgen held positions at the Bristol-Myers Squibb Pharmaceutical Research Institute, New Chemical Entities, and the University of Washington in Seattle. During this time, his work increasingly focused on bio- and cheminformatics. In 2004, Jürgen was appointed Professor and Chair of the newly formed Department of Life Science Informatics at the University of Bonn. He continues to be an Affiliate Professor at the University of Washington. Research of Jürgen’s group focuses on the development of computational methods for drug discovery, machine learning, and concepts for explainable AI. Jürgen is the recipient of the 2015 Herman Skolnik Award, the 2016 Fujita Award, and the 2018 National Award for Computers in Chemical and Pharmaceutical Research of the American Chemical Society. In 2021, he co-founded the Core Area Program Molecular Machine Learning of the German Research Foundation. He is also the Editor-in-Chief of Artificial Intelligence in the Life Sciences.

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Topics

Artificial Intelligence, Deep Learning

Biography

Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a  PhD in Mathematics from the California Institute of Technology. He is currently Distinguished Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems  at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines. He has made contributions to the theory of deep learning, and  developed and applied deep learning methods for the natural sciences. He recently published his fifth book: Deep Learning in Science,
Cambridge University Press (2021). His honors include the 1993 Lew Allen Award at JPL,  the 2010 E. R. Caianiello Prize for research in machine learning,  and election to Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.

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Topics

Artificial Intelligence, Machine Learning

Biography

Ross D. King has joint positions at the University of Cambridge, and Chalmers Institute of Technology, Sweden. He is one of the most experienced machine learning researchers in the UK. His main research interest is the interface between computer science and science. He originated the idea of a ‘Robot Scientist’: integrating AI and laboratory robotics to physically implement closed-loop scientific discovery. His Robot Scientist ‘Adam’ was the first machine to autonomously discover scientific knowledge.

His Robot Scientist ‘Eve’ is currently searching for drugs against neglected tropical diseases, and cancer. This research has been published in top scientific journals, Science, Nature, etc. and has received wide publicity. His other core research interest is DNA computing. He developed the first nondeterministic universal Turing machine, and is now working on ‘DNA supremacy’: a DNA computer that can solve larger NP complete problems that conventional or quantum computers. He is also very interested in computational economics and aesthetics.

https://en.wikipedia.org/wiki/Ross_D._King

 

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Rema Padman
Carnegie Mellon University, USA

Biography

Rema Padman is Trustees Professor of Management Science and Healthcare Informatics, Thrust Leader of Healthcare Informatics Research at iLab, Research Area Director for Operations and Informatics at the Center for Health Analytics in the H. John Heinz III College of Information Systems and Public Policy at Carnegie Mellon University, and Adjunct Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine. Her research investigates healthcare analytics and operations, data-driven decision support and process modeling and risk analysis in the context of clinical and consumer-facing information technology interventions, such as e-health, m-health, chronic and infectious disease management and workflow analysis, in the inpatient, ambulatory and consumer self- health management settings. She is an elected Fellow of the American Medical Informatics Association.

https://www.heinz.cmu.edu/faculty-research/profiles/padman-rema/

 

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Panos Pardalos
University of Florida, USA

Topics

Optimization, Complex Networks & Data Science

Biography

Panos M. Pardalos serves as distinguished professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. He is also an affiliated faculty member of the computer and information science Department, the Hellenic Studies Center, and the biomedical engineering program. He is also the director of the Center for Applied Optimization. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

https://en.wikipedia.org/wiki/Panos_M._Pardalos

https://scholar.google.com/citations?user=4e_KEdUAAAAJ&hl=en

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