Atul Butte

Atul Butte, MD, PhD is the Priscilla Chan and Mark Zuckerberg Distinguished Professor and inaugural Director of the Institute for Computational Health Sciences (ichs.ucsf.edu) at the University of California, San Francisco (UCSF). Dr. Butte is also the Executive Director for Clinical Informatics across the six University of California Medical Schools and Medical Centers. Dr. Butte has authored over 200 publications, with research repeatedly featured in the New York Times, Wall Street Journal, and Wired Magazine. Dr. Butte was elected into the National Academy of Medicine in 2015, and in 2013, he was recognized by the Obama Administration as an Open Science Champion of Change for promoting science through publicly available data. Dr. Butte is also a founder of three investor-backed data-driven companies: Personalis, providing medical genome sequencing services, Carmenta (acquired by Progenity), discovering diagnostics for pregnancy complications, and NuMedii, finding new uses for drugs through open molecular data. Dr. Butte is a principal investigator of three major programs: the California Initiative to Advance Precision Medicine; ImmPort, the clinical and molecular data repository for the National Institute of Allergy and Infectious Diseases; and the California Precision Medicine Consortium, helping recruit tens of thousands of participants into President Obama's Precision Medicine Initiative. Dr. Butte trained in Computer Science at Brown University, worked as a software engineer at Apple and Microsoft, received his MD at Brown University, trained in Pediatrics and Pediatric Endocrinology at Children's Hospital Boston, then received his PhD from Harvard Medical School and MIT. Website: http://profiles.ucsf.edu/atul.butte

Jennifer Chayes

Jennifer Tour Chayes is Distinguished Scientist and Managing Director of Microsoft Research New England in Cambridge, Massachusetts and Microsoft Research New York City, two renowned interdisciplinary centers, bringing together computer scientists, mathematicians, physicists, social scientists, and biologists, and helping to lay the foundations of data science. Prior to founding these labs, Chayes was Research Area Manager for Mathematics, Theoretical Computer Science, and Cryptography at Microsoft Research Redmond. Chayes joined Microsoft Research in 1997, when she co- founded the Theory Group. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of large networks, mechanism design, and graph algorithms. She is the co-author of about 130 scientific papers and the co-inventor of about 30 patents.

Greg Corrado

Greg Corrado, PhD - Greg Corrado is a senior research scientist at Google interested in biological neuroscience, artificial intelligence, and scalable machine learning. He has published in fields ranging across behavioral economics, neuromorphic device physics, systems neuroscience, and deep learning. At Google he has worked for some time on brain inspired computing, and most recently has served as one of the founding members and the co-technical lead of Google's large scale deep neural networks project. Website: https://research.google.com/pubs/GregCorrado.html

Isaac (Zak) Kohane

Isaac Kohane, MD/PhD - Isaac Kohane is the inaugural chair of the Department of Biomedical Informatics at Harvard Medical School. Over the last 30 years, his research agenda has been driven by the vision of what biomedical researchers could do to find new cures, provide new diagnoses and deliver the best care available if data could be converted more rapidly to knowledge and knowledge to practice. In so doing, Kohane has designed and led multiple internationally adopted efforts to “instrument” the healthcare enterprise for discovery and to enable innovative decision-making tools to be applied to the point of care. At the same time, the new insights afforded by ’omic-scale molecular analyses have inspired him and his collaborators to work on re-characterizing and re-classifying diseases such as autism, rheumatoid arthritis and cancers. In many of these studies, the developmental trajectories of thousands of genes have been a powerful tool in unraveling complex diseases. Website: https://dbmi.hms.harvard.edu/person/faculty/zak-kohane

Jure Leskovec

Jure Leskovec is an Associate Professor of Computer Science at Stanford University. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. The problems he investigates are motivated by large scale data, the Web and on-line media. Dr. Leskovec is also Chief Scientists at Pinterest, leading Pinterest Labs. Dr. Leskovec is the recipient of the Lagrange Prize in 2015 and the Alfred P. Sloan Fellowship in 2012.

Fei-Fei Li

Fei-Fei Li, of Stanford University, works on computer vision, cognitive neuroscience and computational neuroscience, and Big Data analysis. She has authored more than 100 scientific articles. Her work appears in computer science and neuroscience journals including Nature, the Journal of Neuroscience and IEEE Transactions on Pattern Analysis and Machine Intelligence. Among her best-known work is the ImageNet project, which has revolutionized the field of large-scale visual recognition. Fei-Fei is the recipient of the 2014 IBM Faculty Fellow Award, the 2011 Alfred Sloan Faculty Award, the 2012 Yahoo Labs FREP Award, the 2009 NSF CAREER Award, and the 2006 Microsoft Research New Faculty Fellowship. She has been featured in media venues such as the New York Times and Science Magazine.

Jill Mesirov

Jill Mesirov, PhD, has joined UC San Diego School of Medicine and Moores Cancer Center as associate vice chancellor for computational health sciences and professor of medicine. Mesirov most recently served as associate director and chief informatics officer at the Broad Institute of MIT and Harvard, where she directed the Computational Biology and Bioinformatics Program. As associate vice chancellor, Mesirov will help formulate an overarching strategy for computational health sciences and research computing at UC San Diego School of Medicine. Mesirov’s research focuses on applying machine-learning methods to functional genomics data in two main areas: cancer and infectious disease. In cancer, Mesirov’s team is analyzing molecular data to determine the underlying biological mechanisms of specific tumor subtypes and to stratify patients according to their relative risks of relapse. In infectious disease, the team is using functional data to better understand the host-pathogen relationship in malaria, as well as to identify biomarkers for differential diagnosis of viral and bacterial diseases and biomarkers of vaccine efficacy.

Susan Murphy

Susan Murphy is a statistician developing new methodologies to evaluate courses of treatment for individuals coping with chronic or relapsing disorders such as depression or substance abuse. In contrast to the treatment of acute illness, where clinicians make a single decision about treatment, doctors treating chronic ailments make a sequence of decisions over time about the best therapeutic approach based on the current state of a patient, the stage of the disease, and the individual’s response to prior treatments. Prof. Murphy has developed a formal model of this decision-making process and an innovative design for clinical trials that allow researchers to test the efficacy of adaptive interventions. While the standard clinical trial paradigm simply tests and compares “one shot” treatments in a defined population, Murphy’s Sequential Multiple Assignment Randomized Trial (SMART) is a means for learning how best to dynamically adapt treatment to each individual’s response over time.

Mihaela van der Schaar

Mihaela van der Schaar is Man Professor of Quantitative Finance in the Oxford – Man Institute of Quantitative Finance (OMI) and the Department of Engineering Science at Oxford, Fellow of Christ Church College and Faculty Fellow of the Alan Turing Institute, London. Mihaela's research interests and expertise are in machine learning, data science and decisions for a better planet. In particular, she is interested in developing machine learning and decision theory for finance, medicine and personalized education. She also has research interests and expertise in game theory and applications, and in social, economic and biological networks. She leads the Data Science and Decisions Research Group.

Susann Beier

Susann Beier is a passionate researcher, scientist and teacher with interest in science and engineering applied for human aid. Her mixed background in Materials and Biomedical Engineering enables unique insights into how we can learn from nature to advance technology. Dr. Beier works as a Research Fellow at the University of Auckland Medical School in New Zealand and assists with teaching at the Engineering School. Her research interests are in the areas of computational fluid dynamics, image analysis and machine learning applied to biomedical engineering research fields. One area of interest is the combination of experimental and computational research areas, such as combination of in vitro 4D flow MRI and computational fluid dynamics. Pitch topic: Creation of a statistical atlas of coronary flow for disease prediction, risk assessment and treatment advances.

James Priest

Dr. James Priest is an Assistant Professor of Pediatric Cardiology at the Stanford University Medical Center. His research interests are in the area of genetics and pathogenesis of congenital heart disease, intended to complement and enhance the surgical and clinical care of infants with this group of disorders. With his research training in genomics and clinical training in pediatric cardiology, Dr. Priest is well positioned to close this gap by investigating into the genetic basis of congenital heart malformations and developing new models of disease. His research objective is to translate an improved molecular genetic and developmental understanding of congenital heart disease from the laboratory into clinically actionable models, diagnostics, and ultimately therapeutic interventions. Pitch topic: Computer vision techniques for the recovery of measures of interest of the aortic valve from heart MRI videos.

Irina Strigo

Irina Strigo, Ph.D. is Associate Professor in the Department of Psychiatry at UCSF and UCSD and Research Physiologist at the U.S. Department of Veterans Affairs. She received her research training from Columbia University (Radiology), Barrow Neurological Institute (Neurosurgery) and UC San Diego (Psychiatry). She received her Ph.D. and B.Sc. in Physiology from McGill University, Montreal, Canada. Her group investigates brain networks that process pain and emotion in individuals with depressive, anxiety disorders, chronic pain and traumatic brain injury.

Enrique Velazquez

Enrique Velazquez, M.D., Ph.D., M.S., M.P.H., is Assistant Professor at The University of Southern California (USC). His research is primarily concerned with integrating clinical and genomic data for Precision Medicine, at the intersection of bioinformatics, statistical analysis, genetics, epidemiology, clinical medicine and public & global health. Dr. Velazquez’ current projects include studies of regulation of cellular immunity in humans with specific relevance to understanding transplantation immunity and development of biomarkers for clinical translation that also extend our understanding of immunity and individualized immunosuppressive therapy in transplant patients (pharmacogenomics). In addition to the integrated study of transplantation immunology, he is also involved in highly technical bioinformatics and statistical research, which describes the integration of multiple genome sequencing technologies.