Data Science Coast To Coast — Robotics and human-computer interaction
Date: Wednesday, March 17, 2021
Time: 12:00–1:00 PM Pacific
The Data Science Coast to Coast (DS C2C) seminar series is hosted jointly by seven academic data science institutes — BIDS, NYU’s Center for Data Science, Rice University’s Ken Kennedy Institute, Stanford Data Science, the University of Michigan’s Michigan Institute for Data Science (MIDAS), and the University of Washington’s eScience Institute, and Johns Hopkins University's Institute of Data Intensive Engineering and Science (IDIES) — to provide a unique opportunity to foster a broad-reaching data science community. In the first half of 2021, DS C2C will host five seminars, each featuring one faculty member and one postdoctoral fellow from two universities. Each speaker will give a 20-minute talk about ongoing projects and motivating issues, followed by 20 minutes of discussion with the audience. These seminars will be the launching point for follow-on research discussion meetings that will hopefully lead to fruitful collaborative research.
Robotics in the Era of Data Science
Lydia Kavraki, Director of Ken Kennedy Institute and Professor of Computer Science, Rice University
Abstract: Advances in mechanisms, control theory and algorithms are delivering robots that explore the deep seas and distant planets, robots that work tirelessly in fulfillment centers, and robots that increasingly interact with people. This talk will touch upon recent developments in robotics with emphasis on our own work in motion planning. It will then discuss the tremendous impact that the integration of research in robotics, AI, and data science will have in our lives and society as a whole.
Bio: Lydia E. Kavraki is the Noah Harding Professor of Computer Science and the Director of the Ken Kennedy Institute at Rice University. Her interests span Robotics, AI, and Biomedicine. In 2020 she received the ACM-AAAI Allen Newell Award and the IEEE Robotics Pioneer Award. She is a member of the National Academy of Medicine, the Academy of Athens, and Academia Europaea. Information about her work can be found at http://www.kavrakilab.org.
Towards naturalistic representation learning in health and disease
Angela Radulescu, Moore-Sloan Faculty Fellow, Center for Data Science, New York University
Abstract: Humans learn more from their experiences than just how to behave in different situations. They also learn to organize experiences into internal representations that facilitate flexible behavior, in domains ranging from simple decision-making to goal-directed action in naturalistic, richly structured environments. In the first part of the talk, I will show that such representation learning relies on selective attention to constrain the dimensionality of environments that humans learn from; and that attention is in turn guided by inference over what features of the environment are relevant for the task at hand. In the second part of the talk, I will present ongoing work leveraging virtual reality (VR) in combination with eye-tracking to study representation learning in naturalistic settings. I will conclude with a discussion of how predictive modeling of behavior in VR may yield insights into cognitive factors that affect mental health.
All events in the series are free to attend, and all who are interested are welcome and encouraged to participate. Questions may be directed to Jing Liu (email@example.com), Managing Director of MIDAS.