Prof. Pierre Baldi University of California Irvine

Distinguished Professor, Department of Computer Science. Director, Institute for Genomics and Bioinformatics. Associate Director, Center for Machine Learning and Intelligent Systems. Office: +1 (949) 824-5809 Email: pfbaldi@ics.uci.edu

AI and Deep Learning in Biomedical Imaging

The process of learning is essential for building natural or artificial intelligent systems. Thus, not surprisingly, machine learning is at the center of artificial intelligence today. And deep learning--essentially learning in complex systems comprised of multiple processing stages--is at the forefront of machine learning. In the last few years, deep learning has led to major performance advances in a variety of engineering disciplines from computer vision, to speech recognition, to natural language processing, and to robotics. In this talk, we will first provide an overview of the history, as well as some of the key theoretical results, in the field of deep learning. We will then examine the application of deep learning methods to problems in biomedical imaging using different imaging modalities (e.g X-rays, Microscopy Images, Videos) and problems (e.g. Drug Discovery, Cancer Detection). Issues of efficiency, training data size, and interpretability will be addressed.

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, Founding 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 several contributions to the theory of deep learning, and developed and applied deep learning methods for problems in the natural sciences such as the detection of exotic particles in physics, the prediction of reactions in chemistry, and the analysis of biomedical images. He has written four books and over 300 peer-reviewed articles. He is the recipient of the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, and a 2014 Google Faculty Research Award. He is an Elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB and has co-founded several startup companies.