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Past Seminars and Events
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| September 11, 2020 |
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Title: QICI online seminar: Noncommuting conserved quantities in quantum many-body thermalization
Time: 10:00pm
Venue: Online
Speaker(s): Dr. Nicole Yunger Halpern, Harvard University
Remark(s): Date: Sept 11, 2020 (Friday)
Time: 10:00 pm (HK Time) (GMT+8)
Join Zoom Meeting
https://hku.zoom.us/j/95512510759?pwd=cUZ1Z3BjY0ZBV0lDYzdoaGpHVlpkQT09
Meeting ID: 955 1251 0759
Password: 373431
Speaker: Dr. Nicole Yunger Halpern, Harvard University
Abstract:
In statistical mechanics, a small system exchanges conserved quantities— heat, particles, electric charge, etc.—with a bath. The small system may thermalize to the canonical ensemble, the grand canonical ensemble, etc. The conserved quantities are represented by operators usually assumed to commute with each other. But noncommutation distinguishes quantum physics from classical. What if the operators fail to commute? I will argue, using quantum-information-theoretic thermodynamics, that the small system thermalizes to near a “non-Abelian thermal state.” I will present a protocol for realizing this state experimentally, supported with numerical simulations of a spin chain. The protocol is suited to ultracold atoms, trapped ions, quantum dots, and more. This work introduces a nonclassical phenomenon—noncommutation of conserved quantities—into a decades-old thermodynamics problem.
About the Speaker
Dr. Nicole Yunger Halpern currently is an ITAMP Postdoctoral Fellow at Harvard. She completed her Ph.D. in 2018, under John Preskill's supervision at Caltech. Her dissertation won the Ilya Prigogine Prize for a thermodynamics Ph.D. thesis. She earned her Master's degree from the Perimeter Scholars International (PSI) program of the Perimeter Institute for Theoretical Physics, working with Rob Spekkens and Markus P. Müller. Before that, she was at Dartmouth College from where she earned her Bachelor's degree and graduated as a co-valedictorian of her class.
All are welcome!
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| September 03, 2020 |
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| August 27, 2020 |
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| August 26, 2020 |
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Title: [CANCELLED] Informative Planning of Autonomous Robots for Spatiotemporal Environmental Monitoring
Time: 10:00am
Venue: Online
Speaker(s): Professor Lantao Liu, Indiana University
Remark(s):
Zoom meeting link:
https://hku.zoom.us/j/99484141050
Meeting ID: 994 8414 1050
Title: Informative Planning of Autonomous Robots for Spatiotemporal Environmental Monitoring
Speaker: Professor Lantao Liu, Indiana University
Abstract:
Date: August 26, 2020 (Wednesday)
Time: 10:00 am (HK Time) (GMT+8)
Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. I will first discuss a Monte Carlo tree search method which enables the robot to not only well balance the environment exploration and exploitation in space, but also catch up to the environmental dynamics that are related to time. This is achieved by incorporating multi-objective optimization and a look-ahead model-predictive rewarding mechanism. The method produces optimized decision solutions for the robot based on its knowledge (estimation) of the environment model, leading to better adaptation to environmental dynamics. Then I will discuss robot decision-making in uncertain and unstructured environments, such as in the scenario when strong winds and water flows cause robot stochastic behaviors. We explore the time-varying stochasticity of robot motion and investigate robot states' reachability, based on which we develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity.
About the speaker:
Lantao Liu is an Assistant Professor in the Luddy School of Informatics, Computing, and Engineering at Indiana University-Bloomington. He has been working on planning, learning, and coordination techniques for autonomous systems involving single or multiple robots with potential applications in environmental monitoring, surveillance and security, search and rescue, as well as smart transportation. Before joining Indiana University, he was a Research Associate in the Department of Computer Science at the University of Southern California during 2015 - 2017. He also worked as a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University during 2013 - 2015. He received a Ph.D. from the Department of Computer Science and Engineering at Texas A&M University in 2013, and a Bachelor degree from the Department of Automatic Control at Beijing Institute of Technology in 2007.
All are welcome!
Tel: 2859 2180
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| August 20, 2020 |
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| July 16, 2020 |
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| July 14, 2020 |
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| July 10, 2020 |
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Title: Robust Decision Making in a Partially Observable World
Time: 02:00pm
Venue: Online
Speaker(s): Hanna Kurniawati, Australian National University
Remark(s):
Zoom meeting link:
https://hku.zoom.us/j/94650715947
Meeting ID: 946 5071 5947
Title: Robust Decision Making in a Partially Observable World
Speaker: Hanna Kurniawati, Australian National University
Date: July 10, 2020 (Friday)
Time: 2:00 pm (HK Time) (GMT+8)
Abstract:
Robust robot operation must answer: What to do now, to receive good long-term returns, despite notRobust robot operation must answer: What to do now, to receive good long-term returns, despite notknowing the exact effect of its actions, despite various errors in sensors and sensing, and despitelimited information about the environment and itself. This problem is not new. Mathematically principledconcepts --called Partially Observable Markov Decision Processes (POMDPs)-- have been developedmore than five decades ago to address the problem mentioned above. However, such concepts arenotorious for their computational complexity, that they have often been considered impractical. I willpresent some of our effort in addressing the computational complexity issues of solving POMDPs, anddemonstrate that this decision making concept has now become practical (to some extent) for solvingvarious problems in robotics. I will end with a discussion on what this technology could mean inbridging the gap between sensing and acting in robotics, and between planning and learning ingeneral.
About the speaker:
Hanna Kurniawati is a Senior Lecturer with ANU and CS Futures Fellowship at the Research School ofHanna Kurniawati is a Senior Lecturer with ANU and CS Futures Fellowship at the Research School ofComputer Science, Australian National University (ANU). Prior to ANU, she was an academic at theUniversity of Queensland and a Research Scientist at the Singapore-MIT Alliance for Research andTechnology. She earned a PhD in Computer Science from National University of Singapore for work onrobot motion planning. Her current research focuses on the design and development of algorithms thatenable mathematically principled concepts for robust decision making to become practical tools inrobotics. Along with colleagues and students, she won a best paper award at ICAPS’15 and was afinalist of the best paper award at ICRA’15. She was also a keynote speaker at IROS’18.
All are welcome!
Tel: 2859 2180
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| July 02, 2020 |
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| June 30, 2020 |
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