Invited Speakers

Anthony Francis studies human and other minds to design intelligent machines and emotional robots; his popular writing on robotics includes articles in the books Star Trek Psychology and Westworld Psychology as well as a Google AI blog article titled "Maybe your computer just needs a hug." Currently, he's a Senior Software Engineer at Google Brain Robotics specializing in deep reinforcement learning for robot navigation. Previously, he worked on emotional long-term memory for robot pets at Georgia Tech's PEPE robot pet project, on models of human memory for information retrieval at Enkia Corporation, and on large-scale metadata search and 3D object visualization at Google. He earned his B.S., M.S. and Ph.D. in Computer Science from Georgia Tech, along with a Certificate in Cognitive Science. Recently, Anthony and his colleagues won the ICRA 2018 Best Paper Award for Service Robotics for their paper "PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning". He's the author of over a dozen peer-reviewed publications and is an inventor on over a half-dozen patents. He's published over a dozen short stories and four novels, including the EPIC eBook Award-winning Frost Moon and the steampunk adventure Jeremiah Willstone and the Clockwork Time Machine. He lives in San Jose with his wife and cats, but his heart will always belong in Atlanta. To read more about Anthony's research and writing, visit his blog at dresan.com.

Neil T. Dantam is an Assistant Professor of Computer Science at the Colorado School of Mines. Neil's research focuses on robot planning and control. He has developed methods to combine discrete and geometric planning, improve Cartesian control, and analyze discrete robot policies. In addition, he has worked on practical aspects of robot manipulation and software design to validate new theoretical techniques.

Previously, Neil Dantam was a Postdoctoral Research Associate in Computer Science at Rice University working with Prof. Lydia Kavraki and Prof. Swarat Chaudhuri. Neil received a Ph.D. in Robotics from Georgia Tech, advised by Prof. Mike Stilman, and B.S. degrees in Computer Science and Mechanical Engineering from Purdue University. He has worked at iRobot Research, MIT Lincoln Laboratory, and Raytheon. Neil received the Georgia Tech President's Fellowship, the Georgia Tech/SAIC paper award, an American Control Conference '12 presentation award, and was a Best Paper and Mike Stilman Award finalist at HUMANOIDS '14.

Marco Morales is an Assistant Professor in the Department of Digital Systems at the Instituto Tecnológico Autónomo de México (ITAM), where he leads the Robotics Laboratory. Also, he is currently a Visiting Professor at the Department of Computer Science and Engineering at Texas A&M University. His main research interests are in motion planning and control in robotics. Morales received a Ph.D. in Computer Science from Texas A&M University, a M.S. in Electrical Engineering and a B.S. in Computer Engineering from Universidad Nacional Autónoma de México (UNAM). He received a Fulbright/García Robles scholarship to pursue his PhD, a CONACYT scholarship to pursue his Masters, and was a SuperComputing Scholar at UNAM. He has been member of the National System of Researchers in Mexico. He has served as Associate Editor for IEEE ICRA since 2011 and for IEEE/RSJ IROS since 2008. Morales was one of the chairs of the Eight International Workshop on the Algorithmic Foundations of Robotics, held in Guanajuato, México, in 2008. He is a founder member of the Mexican Federation of Robotics that promote robotics through events such as the Mexican Tournament of Robotics (of which he was chair in 2011), the Mexican School of Robotics, and the RoboCup that was brought to Mexico City in 2012.

Michael Otte is an assistant professor in Aerospace Engineering at the University of Maryland College Park. His research interests include motion planning for single and multi-agent systems and distributed robotic algorithms for contested environments that are hazardous, changing, and/or require or impose limited communication between agents, as well as methods that enable cooperation between agents given such constraints. He was previously a National Science Foundation Postdoctoral Associate at the U.S. Naval Research Laboratory (NRL), a visiting Scholar at the U.S. Air Force Research Laboratory (AFRL), and a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT). He received his M.S. and Ph.D. from the University of Colorado Boulder (CU) in Computer Science.

Jason O'Kane is Associate Professor and Associate Chair for Academics in Computer Science and Engineering at the University of South Carolina, where he is also Director of the Center for Computational Robotics. He holds the Ph.D. (2007) and M.S. (2005) degrees from the University of Illinois at Urbana-Champaign and the B.S. (2001) degree from Taylor University, all in Computer Science. He has received a CAREER Award from NSF, a Breakthrough Star Award from the University of South Carolina, and the Outstanding Graduate in Computer Science Award from Taylor University. His research spans algorithmic robotics, planning under uncertainty, and computational geometry.

Vikas Sindhwani is Research Scientist in the Google Brain team in New York where he leads a research group focused on solving a range of perception, learning and control problems arising in Robotics. His interests are broadly in core mathematical foundations of statistical learning, and in end-to-end design aspects of building large-scale, robust machine intelligence systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI) 2013, the IBM Pat Goldberg Memorial Award in 2014, and was co-winner of the Knowledge Discovery and Data Mining (KDD) Cup in 2009. He serves on the editorial board of IEEE Transactions on Pattern Analysis and Machine Intelligence, and has been area chair and senior program committee member for International Conference on Learning Representations (ICLR) and Knowedge Discovery and Data Mining (KDD). He previously led a team of researchers in the Machine Learning group at IBM Research, NY. He has a PhD in Computer Science from the University of Chicago and a B.Tech in Engineering Physics from Indian Institute of Technology (IIT) Mumbai. His publications are available at: http://vikas.sindhwani.org/.