- Michelle Zhou, Juji, Inc
Dr. Michelle Zhou is a Co-Founder and CEO of Juji, Inc., an Artificial Intelligence (AI) startup located in Silicon Valley, specializing in building state-of-the-art AI technologies and solutions that enable the creation and adoption of responsible and empathetic AI agents. Prior to starting Juji, Michelle led the User Systems and Experience Research (USER) group at IBM Research – Almaden and then the IBM Watson Group. Michelle’s expertise is in the interdisciplinary area of intelligent user interaction (IUI), including conversational AI systems and personality analytics. She is an inventor of the IBM Watson Personality Insights and has led the development and commercialization of at least a dozen products in her areas of expertise. Michelle has also published over 100 peer-reviewed, refereed scientific articles and 45 patents. She received a Ph.D. in Computer Science from Columbia University and is an ACM Distinguished Scientist.
- Oliver Lemon, Heriot-Watt University
Oliver Lemon is a Professor of Computer Science at Heriot-Watt University, Edinburgh, and Director of the Interaction Lab, which focuses on conversational AI, NLP, machine learning approaches to spoken and multimodal interaction, and Human-Robot Interaction. He was previously a research fellow at Stanford and Edinburgh universities, and holds a PhD from Edinburgh (1996). He has led several national and international research projects funded by EPSRC and the European Union. He is an associate editor for ACM Transactions on Interactive Intelligent Systems. He is a faculty advisor of Heriot-Watt’s Alexa-prize team, which was a finalist in both 2017 and 2018.
- Gabriel Skantze, Furhat Robotics
Gabriel Skantze is the Chief Scientist and co-founder of Furhat Robotics. Gabriel is also a Professor in Speech Technology with a specialization in Conversational Systems at KTH. He is leading several research projects and has published 100+ papers on conversational systems and human-robot interaction.
- Iolanda Leite, KTH Royal Institute of Technology, Sweden
Iolanda Leite is an Associate Professor at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. She received her PhD degree from the Technical University of Lisbon (IST). Before joining KTH, she was a Postdoctoral Associate at the Yale Social Robotics Lab and an Associate Research Scientist at Disney Research Pittsburgh. The goal of her research is to develop social robots that can capture, learn from and respond appropriately to the subtle dynamics that characterize real-world situations, allowing for truly efficient and engaging long-term interactions with people.
- Alessandra Sciutti, Istituto Italiano di Tecnologia
Alessandra Sciutti is a Tenure Track Researcher and Principal Investigator of the COgNiTive Architecture for Collaborative Technologies Unit (CONTACT) at IIT. She received her Ph.D. in Humanoid Technologies from the University of Genova (Italy). The scientific aim of her research is to investigate the sensory and motor mechanisms underlying mutual understanding in human-human interaction, with the technological goal of designing robots that can naturally cooperate with people in carrying out everyday tasks. Her research is aimed at defining which features of human and robot motion allow for natural mutual understanding, with particular reference to low-level kinematics properties (as biological motion) and higher-level, cognitive aspects (as intention reading and goal anticipation).
- Ognjen Rudovic, MIT Media Lab
Ognjen Rudovic is a Marie Curie Fellow in the Affective Computing group at Media Lab, working with Rosalind Picard on machine learning for the new generation of affective robots! His background is in Automatic Control Theory, Computer Vision, Artificial Intelligence and Machine Learning. In 2014, he received a PhD from Imperial College London, UK, where he worked on machine-learning and computer vision models for automated analysis of human facial behavior. His current research is focused towards the design of novel and more engaging machine learning paradigms for personalized, context-sensitive and multi-modal sensing (from audio, visual and physiological signals) of human behavior. The aim of these models is to improve the personal medicine and healthcare, as well as the existing wellbeing technologies.
- Cristina Conati, University of British Columbia
Cristina Conati is a Professor in the Department of Computer Science at the University of British Columbia. Her goal is to integrate research in Artificial Intelligence, Human Computer Interaction and Cognitive Science to create intelligent user interfaces that can effectively and reliably adapt to the needs of each user, particularly by extending the range of user’s states and traits that can be reliably captured in a computational user model and leveraged for adaptation - from purely cognitive features (knowledge, skills, goals), to affective states (emotions, moods, attitudes), to meta-cognitive skills (e.g., the capability of effectively exploring a large information space) and personality traits.