Robotics Engineers Take on COVID-19
Methods that were originally created to help robots to walk and autonomous cars to drive safely can also help epidemiologists predict the spread of the COVID-19 pandemic. Aaron Ames, Bren Professor of Mechanical and Civil Engineering and Control and Dynamical Systems, and colleagues took these tools and applied them to the development of an epidemiological methodology that accounts for human interventions (like mask mandates and stay-at-home orders). By utilizing the U.S. COVID-19 data from March through May, they were able to predict the infection wave during the summer to high accuracy. "This is the greatest health challenge to face our society in a generation at least. We all need to pitch in and help in any way we can," Ames says. [Caltech story]
IRCA Best Paper Awards
Two teams of Caltech researchers have won three International Conference on Robotics and Automation (ICRA) Best Paper Awards in multiple categories along with the overall best paper award. The ICRA is the largest and most prestigious robotics conference of the year. Awards are given on the basis of technical merit, originality, potential impact on the field, clarity of the written paper, and quality of the presentation. Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, and Aaron Ames, have won the ICRA Best Conference Paper Award and the ICRA Best Paper Award on Human-Robot Interaction (HRI) for their paper entitled "Preference-Based Learning for Exoskeleton Gait Optimization." Amanda Bouman, Paul Nadan, Matthew Anderson, Daniel Pastor, Jacob Izraelevitz, Joel Burdick, and Brett Kennedy, have won the ICRA Best Paper Award on Unmanned Aerial Vehicles for their paper entitled "Design and Autonomous Stabilization of a Ballistically Launched Multirotor."
Meet the 2018 Amazon Fellows
The Amazon Fellows program is the result of a partnership between Caltech and Amazon AWS around Machine Learning and Artificial Intelligence (AI). The 2018 Amazon fellows are Ehsan Abbasi, Gautam Goel, Jonathan Kenny, Palma London, and Xiaobin Xiong. Abbasi is interest in contributing to a deeper understanding of convex and non-convex learning methods in AI and is an Electrical Engineering graduate student working with Professor Babak Hassibi. Goel’s research interest is at the interface of the theory and practice of machine learning and is advised by Professor Adam Wierman. London is also working with Professor Wierman. She is developing efficient algorithms for solving extremely large optimization problems. The methods are applicable to distributed and parallel optimization. For example in a distributed data center setting, the algorithms are robust to unreliable data transfer between data centers and take into account privacy concerns. Kenny is a Computation & Neural Systems graduate student working with Professor Thanos Siapas on deep neural networks to identify and classify brain states. Xiong is a mechanical engineering graduate student who enjoys working on real physical robots, to make them walk, jump, and run in real life. He is advised by Professor Aaron Ames and their research is focused on robotic bipedal locomotion