Zoltan Haiman is a Professor of Astronomy at Columbia University. He received a Ph.D. in Astronomy from Harvard University in 1998, and a B.S. in Physics and Electrical Engineering from MIT. He had postdoctoral positions at Fermilab and at Princeton. His main research is on topics in theoretical astrophysics and cosmology, including the formation of the first stars and black holes, the subsequent growth of black holes, and determining the nature of dark energy and dark matter using large Astronomical surveys. He is a frequent user of NASA's Pleiades and NSF's XSEDE supercomputing facilities. His current research includes simulations of mergers between astrophysical black holes, and large-scale simulations of weak gravitational lensing, developing tools, including neural networks, to extract cosmological information from non-Gaussian features of the stochastic gravitational lensing signal.

Prof. Haiman is a recipient of NASA's Hubble Fellowship, a Gyorgy Bekesy Fellowship from the Hungarian Ministry of Education, an NYAS Blavatnik Award for Young Scientists and a Simons Fellowship in Theoretical Physics. He was named in 2002 as one of the Brilliant 10 young scientists by Popular Science magazine. is currently serving on the Science and Technology Definition Team for NASA’s concept study for the X-ray satellite Lynx. He has published over 200 peer-reviewed publications, and has mentored 12 Astronomy and Physics PhD students.

Debbie Bard leads the Data Science Engagement Group at the National Energy Research Scientific Computing Center (NERSC) at Berkeley National Lab. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and worked at Imperial College London and SLAC National Accelerator Laboratory before joining NERSC, where her group leads the support of supercomputing for experimental science. Her work focuses on data-intensive computing and research, including machine learning at scale.

Thomas Hofmann is Professor of Computer Science at ETH Zurich, co-Director of the Institute for Machine Learning, co-Director of the Max-Planck ETH Center for Learning Systems and Head of the Data Analytics Group. His areas of interest include machine learning, optimization, and intelligent systems, with an application focus on natural language understanding, text mining. He is also co-founder of 1plusX, a startup developing technology for user data modeling. Thomas has published more than 80 peer reviewed papers, which received close to 23,000 citations. Some of his most recognized scientific contributions are the ones on probabilistic latent semantic analysis, pairwise data clustering, and large-margin structured prediction.

Before joining ETH, Thomas held various positions in industry and academia. From 2006-2013, he was Engineering Director at Google, working on ads optimization, e-commerce, web search, and various research projects that involved machine learning. During this time, he helped grow the Zurich Engineering Center from 50 to 1500 employees. In the years 2004-2005 Thomas was Professor for Intelligent Systems at TU Darmstadt and co-appointed as the Director of the Fraunhofer Institute IPSI. He spent 7 years in the US from 1997-2004, during which time he was Assistant/Associate Professor of Computer Science at Brown University and a Postdoctoral Fellow at MIT and UC Berkeley. Thomas also co-founded Recommind, a San Francisco-based company developing eDiscovery solutions, Thomas holds a Diploma in Computer Science and a PhD (rer. nat.) from University of Bonn.

Michelle Ntampaka is a postdoctoral fellow at the Harvard Data Science Initiative and the Center for Astrophysics, Harvard & Smithsonian. Her research focuses on constraining cosmological models with galaxy clusters and other large scale structre. She uses machine learning and statistical tools to tease out complicated patterns in the data that are inaccessible through more traditional means. She received her Ph.D. from Carnegie Mellon University.

Dr. Barnabás Póczos is an associate professor in the Machine Learning Department at the School of Computer Science, Carnegie Mellon University. His research interests lie in the theoretical questions of statistics and their applications to machine learning. Currently he is developing machine learning methods for advancing automated discovery and efficient data processing in applied sciences including health-sciences, neuroscience, bioinformatics, cosmology, agriculture, robotics, civil engineering, and material sciences. His results have been published in top machine learning journals and conference proceedings, and he is the co-author of 100+ peer reviewed papers. He has been a PI or co-Investigator on 15+ federal and non-federal grants. Dr. Poczos is a member of the Auton Lab in the School of Computer Science. He is a recipient of the Yahoo! ACE award. In 2001 he earned his M.Sc. in applied mathematics at Eotvos Lorand University in Budapest, Hungary. In 2007 he obtained his Ph.D. in computer science from the same university. From 2007-2010 he was a postdoctoral fellow in the RLAI group at University of Alberta, then he moved to Pittsburgh where he was a postdoctoral fellow in the Auton Lab at Carnegie Mellon from 2010-2012.

Wojciech Samek is head of the Machine Learning Group at Fraunhofer Heinrich Hertz Institute, Berlin, Germany. He studied Computer Science at Humboldt University of Berlin as a scholar of the German National Academic Foundation, and received his PhD in Machine Learning from the Technical University of Berlin in 2014. He was a visiting researcher at NASA Ames Research Center, Mountain View, CA, and a PhD Fellow at the Bernstein Center for Computational Neuroscience Berlin. He was co-organizer of workshops and tutorials about interpretable machine learning at various conferences, including CVPR, NIPS, ICASSP, MICCAI and ICIP. He is part of the Focus Group on AI for Health, a world-wide initiative led by the ITU and WHO on the application of machine learning technology to the medical domain. He is associated with the Berlin Big Data Center and the Berlin Center of Machine Learning and is a member of the editorial board of Digital Signal Processing and PLOS ONE. He has co-authored more than 90 peer-reviewed papers, predominantly in the areas deep learning, interpretable machine learning, neural network compression, robust signal processing and computer vision.

Fernando Pérez-Cruz received a PhD. in Electrical Engineering in 2000 from the Technical University of Madrid and an MSc/BSc in Electrical Engineering from the University of Sevilla in 1996. He is the Chief Data Scientist at the Swiss Data Science Center (ETH Zurich and EPFL). He has been a member of the technical staff at Bell Labs and an Associate Professor with the Department of Signal Theory and Communication at University Carlos III in Madrid and Computer Science at Stevens Institute of Technology.

He has been a visiting professor at Princeton University under a Marie Curie Fellowship and a Research Scientist at Amazon. He has also held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), BioWulf Technologies (New York) and the Technical University of Madrid and Alcala University (Madrid). His current research interest lies in machine learning and information theory and its application to signal processing and communications. Fernando has organized several machine learning, signal processing, and information theory conferences. Fernando has supervised 8 PhD students and numerous MSc students, as well as one junior and one senior Marie Curie Fellow. Fernando has published over 40 papers in leading academic journals, as well as over 60 peer-reviewed conferences.