Stefanie Tellex is an Associate Professor of Computer Science at Brown University. Her group, the Humans To Robots Lab, creates robots that seamlessly collaborate with people to meet their needs using language, gesture, and probabilistic inference, aiming to empower every person with a collaborative robot. She completed her Ph.D. at the MIT Media Lab in 2010, where she developed models for the meanings of spatial prepositions and motion verbs. Her postdoctoral work at MIT CSAIL focused on creating robots that understand natural language. She has published at SIGIR, HRI, RSS, AAAI, IROS, ICAPs and ICMI, winning Best Student Paper at SIGIR and ICMI, Best Paper at RSS, and an award from the CCC Blue Sky Ideas Initiative. Her awards include being named one of IEEE Spectrum's AI's 10 to Watch in 2013, the Richard B. Salomon Faculty Research Award at Brown University, a DARPA Young Faculty Award in 2015, a NASA Early Career Award in 2016, a 2016 Sloan Research Fellowship, and an NSF Career Award in 2017. Her work has been featured in the press on National Public Radio, BBC, MIT Technology Review, Wired and Wired UK, as well as the New Yorker. She was named one of Wired UK's Women Who Changed Science In 2015 and listed as one of MIT Technology Review's Ten Breakthrough Technologies in 2016.

He is a full-time professor and researcher in the School of Engineering at Universidad Panamericana (Mexico), and he is the leader and principal investigator of the research group "Computational Intelligence and Vision" in the same university. He graduated in Mechatronics Engineering at Tecnologico de Monterrey (Mexico), obtained a Master in Science Engineering, and a Ph.D. in Computer Science in the same university. He is a member of the National System of Researchers rank level 1 of the National Council of Science and Technology (Mexico). He is the author of more than 85 international journal and conference publications, 8 book chapters, and 4 books in the area of artificial intelligence and robotics. He is an executive board member of the Mexican Society of Artificial Intelligence (Mexico), a member of the Technical Committee on Neural Networks in the IEEE Computational Intelligence Society, a member of the Technical Committee on Robotics and Mechatronics in IFToMM, among other memberships. He was awarded a Google Research Award for Latin America in 2017. He has served as guest editor in different special issues of reputed journals. He is currently Associate Editor in IEEE Access.

Yulia Sandamirskaya is a research scientist at Intel Labs: she leads the Applications Research group in the Neuromorphic Computing Lab in Munich. Sandamirskaya was a group leader in the Institute of Neuroinformatics at the University of Zurich and ETH Zurich, leading research on Neuromorphic Cognitive Robots. She specializes in perception, movement control, memory formation, and learning in embodied neuronal systems, and implemented neuronal architectures in neuromorphic devices, interfaced with robotic sensors and motors. Sandamirskaya has served as the chair of EUCOG, the European Society for Artificial Cognitive Systems, and as coordinator of the NEUROTECH project, shaping the neuromorphic computing technology community in Europe.

Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making.

Dr. Conrad Tucker is an Arthur Hamerschlag Career Development Professor of Mechanical Engineering at Carnegie Mellon University, with courtesy faculty appointments in Machine Learning and Robotics. His research employs Artificial Intelligence (AI) techniques to advance the personalization of engineered-systems and educational systems. In the educational systems domain, Dr. Tucker has proposed AI methods that automatically assess students’ performance on engineering hands-on activities such as design prototyping that helps guide students towards more efficient solutions. Within the digital learning space, Dr. Tucker has proposed AI-driven procedural content generation methods for 3D virtual environments that can be used to advance the personalization of students’ learning experiences in virtual/augmented reality environments. Dr. Tucker’s most recent work, funded by the National Science Foundation, investigates the risks and opportunities posed by high-quality AI-generated STEM learning content that could then be disseminated via publicly-available educational sources. On one hand AI-generated learning content has the potential to broaden access of high-quality STEM learning content across a wide range of student learners. On the other hand, AI-generated content that is controversial or intentionally deceptive, has the potential to misinform the student population and reduce public trust in STEM, AI, and science and a whole. Dr. Tucker is part of the inaugural class of the Gates Millennium Scholars Program, funded by a $1Billion grant from the Bill and Melinda Gates Foundation. In February 2016, he was invited by National Academy of Engineering (NAE) President Dr. Dan Mote, to serve as a member of the Advisory Committee for the NAE Frontiers of Engineering Education (FOEE) Symposium. He received his Ph.D., M.S. (Industrial Engineering), and MBA degrees from the University of Illinois at Urbana-Champaign, and his B.S. in Mechanical Engineering from Rose-Hulman Institute of Technology.

Hadas Kress-Gazit is a Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Her research focuses on formal methods for robotics and automation and more specifically on synthesis for robotics - automatically creating verifiable robot controllers for complex high-level tasks. Her group explores different types of robotic systems including modular robots, soft robots and swarms, and synthesizes ideas from robotics, formal methods, control, hybrid systems and human-robot interaction. She has received multiple recognitions and awards for her research, her teaching and her advocacy for groups traditionally underrepresented in robotics.

Katie Driggs-Campbell is currently an assistant professor at the University of Illinois at Urbana-Champaign in the Department of Electrical and Computer Engineering. Prior to that, she was a Postdoctoral Research Scholar at the Stanford Intelligent Systems Laboratory in the Aeronautics and Astronautics Department. She received a B.S.E. with honors from Arizona State University in 2012 and an M.S. from UC Berkeley in 2015. She earned her PhD in 2017 in Electrical Engineering and Computer Sciences from the University of California, Berkeley. Her lab works on human-centered autonomy, focusing on the integration of autonomy into human dominated fields, merging ideas robotics, learning, and control.

Kalesha Bullard is a postdoctoral researcher at Facebook AI Research. She completed her PhD in Computer Science at Georgia Institute of Technology in 2019, where her research lied at the intersection of human-robot interaction and machine learning, in interactive robot learning. During her postdoc, Kalesha has expanded her research to explore the space of multi-agent reinforcement learning, currently investigating how to enable embodied multi-agent populations to learn general communication protocols. More broadly, Kalesha’s research interests span the space of autonomous reasoning and decision making for artificial agents in multi-agent settings. To date, her research has focused on models and algorithms for enabling agents to learn through interaction with other agents (human or artificial). Kalesha has also participated in a number of service roles throughout her research career. She currently serves as a member of the organizing committee for the NeurIPS 2020 Workshop on Zero-Shot Emergent Communication. And recent prior appointments include serving as: a Program Committee (PC) member for the 2020 NeurIPS Cooperative AI Workshop and the 2019 International Conference on Autonomous Agents and Multi-Agent Systems, an Area Chair for the 2019 NeurIPS Women in Machine Learning Workshop, and PC Co-Chair for the 2017 AAAI Fall Symposium on Artificial Intelligence for Human-Robot Interaction.

Rohan is an Assistant Professor and Pankaj Gupta Faculty Fellow at the Department of Computer Science and Engineering at IIT Delhi. His research is centered around human-robot teaming and semantic mapping aimed towards capable service robots that can work effectively with humans. Rohan obtained his B. Tech. and M.Tech. degrees at the Indian Institute of Technology Delhi, doctoral research at the University of Oxford, UK (as a Rhodes Scholar from India), and Postdoctoral research at the Massachusetts Institute of Technology, USA. He is a recipient of Best Paper Awards at RSS 2016, IROS 2013 and Finalist for Best Vision Paper Award at ICRA 2010. He is a recipient of the National Award for Outstanding Applied Research Improving the Life of Persons with Disabilities, 2015 conferred by the Govt. of India and was named one of “35 Global Innovators Under 35” by MIT Technology Review in 2015.