Christopher K. Wikle is Curators’ Distinguished Professor of Statistics at the University of Missouri (MU), with additional appointments in Soil, Environmental and Atmospheric Sciences and the Truman School of Public Affairs. He received a PhD co-major in Statistics and Atmospheric Science in 1996 from Iowa State University. He was research fellow at the National Center for Atmospheric Research from 1996-1998, after which he joined the MU Department of Statistics. His research interests are in spatio-temporal statistics applied to environmental, geophysical, agricultural and federal survey applications, with particular interest in dynamics. Awards include elected Fellow of the American Statistical Association (ASA), Distinguished Alumni Award from the College of Liberal Arts and Sciences at Iowa State University, ASA ENVR Section Distinguished Achievement Award, co-awardee 2017 ASA Statistical Partnership Among Academe, Industry, and Government (SPAIG) Award, the MU Chancellor’s Award for Outstanding Research and Creative Activity in the Physical and Mathematical Sciences, the Outstanding Graduate Faculty Award, and Outstanding Undergraduate Research Mentor Award. His book Statistics for Spatio-Temporal Data (co-authored with Noel Cressie) was the 2011 PROSE Award winner for excellence in the Mathematics Category by the Association of American Publishers and the 2013 DeGroot Prize winner from the International Society for Bayesian Analysis. He is Associate Editor for several journals and is one of six inaugural members of the Statistics Board of Reviewing Editors for Science.

Stefano Ermon is an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment. His research is centered on techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability with applications in poverty mapping and remote sensing.

M. Ani Hsieh is a Research Associate Professor in the Department of Mechanical Engineering & Applied Mechanics at the University of Pennsylvania. She received a B.S. in Engineering and B.A. in Economics from Swarthmore College in 1999 and a PhD in Mechanical Engineering from the University of Pennsylvania in 2007. Dr. Hsieh has been a Visiting Assistant Professor in the Engineering Department at Swarthmore College (2007-2008) and an Associate Professor in the Mechanical Engineering & Mechanics Department at Drexel University (2008-2017). Her research interests include many robot systems and marine robotics, geophysical fluid dynamics, and dynamical systems. She is a recipient of a 2012 Office of Naval Research (ONR) Young Investigator Award and a 2013 National Science Foundation (NSF) CAREER Award.

Chelsea Finn is a postdoctoral fellow in Computer Science at UC Berkeley, where she works on machine learning and its intersection with robotic perception and control. She is a part of Berkeley AI Research Lab (BAIR). She recently spent time at Google Brain. Before graduate school, she received a Bachelors in EECS at MIT, where she worked on several research projects, including an assistive technology project in CSAIL and an animal biometrics project. She has also spent time at Counsyl, Google, and Sandia National Labs. She will discuss on spatiotemporal aspects of video prediction and deep spatial autoencoders for visuomotor learning.

Girish Chowdhary is the director of DAS laboratory and Assistant Professor. Girish has a Ph.D. degree from Georgia Institute of Technology. He then spent around two years at Massachusetts Institute of Technology’s Laboratory for Information and Decision Systems and the School of Aeronautics and Astronautics as a postdoctoral associate. Prior to coming to Georgia Tech, he spent three years working as a research engineer with the German Aerospace Center’s (DLR’s) Institute for Flight Systems Technology in Braunschweig, Germany. He holds a BE with honors from RMIT University in Melbourne, Australia. Girish is the author of several peer-reviewed publications spanning the area of adaptive control, spatiotemporal modeling, autonomy and decision making, LIDAR-based perception for Unmanned Aerial Systems (UAS), and GPS denied navigation.

Abstract: Modern sensors provide immense amounts of information that need to be efficiently integrated into probabilistic models representing the environment autonomous systems operate in. In this talk I will show statistical machine learning methods for spatial and spatial-temporal data that are able to fuse information from heterogeneous sources, scaling gracefully to very large datasets. I will demonstrate how Bayesian reasoning and the principle of modelling uncertainty can be used to mitigate risks in decision making, for motion planning with indoor robots, to continental-scale natural resource exploration.

Bio: Fabio Ramos is an Associate Professor in machine learning and robotics at the School of Information Technologies, University of Sydney, and co-Director of the Centre for Translational Data Science. He received the B.Sc. and the M.Sc. degrees in Mechatronics Engineering at University of Sao Paulo, Brazil, in 2001 and 2003 respectively, and the Ph.D. degree at University of Sydney, Australia, in 2008. He has over 130 peer-reviewed publications and received best paper awards at ECML’18, IROS’05, ACRA’07, and Best Paper Finalist at RSS’17. His research focuses on statistical machine learning techniques for data fusion, with applications in robotics, large-scale autonomous systems, environmental monitoring and healthcare.