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April 2024

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Uma Girish will present her FPO "Quantum versus Classical Communication and Query Complexity" on Thursday, May 2nd, 2024 at 11am in CS 201.
by Gradinfo 05 Apr '24

05 Apr '24

Uma Girish will present her FPO "Quantum versus Classical Communication and Query Complexity" on Thursday, May 2nd, 2024 at 11am in CS 201. The members of her committee are as follows: Examiners: Ran Raz (adviser), Mark Braverman and Gillat Kol Readers: Matt Weinberg and Huacheng Yu All are welcome to attend. Please see abstract below. Understanding the relative power of quantum versus classical computation is a fascinating and important problem in modern science on which there is likely to be much research and progress in the next few decades. While quantum algorithms are widely believed to exponentially outperform classical algorithms, there are very few settings in which this has been unconditionally proved. Communication complexity and query complexity are striking examples of such settings, where we can mathematically prove that quantum algorithms exponentially outperform classical algorithms. In this thesis we study the advantages of quantum over classical in these settings and present new and improved quantum speedups over classical. Among several results, we achieve state-of-the-art quantum speedups in communication complexity, using quantum protocols that are efficiently implementable, and using noisy quantum protocols where all qubits except for one are maximally noisy. We also study the power of entanglement and demonstrate in various ways that a slight increase in the entanglement can provide exponential savings in the communication complexity. We also study the power of adaptive queries in quantum query complexity and show that each successive round of quantum queries can yield super-exponential speedups in the number of quantum queries.

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Princeton Robotics Seminar: Friday @ 11 AM (Changliu Liu) and 12:30 PM (Claire Tomlin)
by Emily C. Lawrence 02 Apr '24

02 Apr '24

Join us Friday 4/5 for two fantastic robotics speakers! Speaker: Prof. [ http://icontrol.ri.cmu.edu/ | Changliu Liu ] , CMU Date: Friday, April 5th, 2024 Time: 11:00 AM Room: Computer Science 105 Princeton Robotics Seminar Students: [ https://forms.gle/rK7HNHkKeVqRB4sKA | Sign-up for lunch with the speaker here ] (12:00 - 1:30 PM.) Title: Ensuring Robot Safety Through Safety Index Synthesis Abstract: Safety Index is a special class of high order control barrier functions. Its purpose is to ensure forward invariance within a user-specified safe set and achieve finite time convergence to that set. Synthesizing a valid safety index poses significant challenges, particularly when dealing with control limits, uncertainties, and time-varying dynamics. In this talk, I will introduce a variety of approaches that can be used for safety index synthesis, including a rule-based method, an evolutionary optimization-based approach, a constrained reinforcement learning-based approach, an adversarial optimization-based approach, as well as sum of square programming. The parameterization of the safety index can either take an analytical form or be a neural network. I will conclude the talk by highlighting the limitations of existing work and discuss potential future directions, including integrating formal verification into neural safety index synthesis. Bio: Dr. Changliu Liu is an assistant professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU, Dr. Liu was a postdoc at Stanford Intelligent Systems Laboratory. She received her Ph.D. in Engineering together with Master degrees in Engineering and Mathematics from University of California at Berkeley and her bachelor degrees in Engineering and Economics from Tsinghua University. Her research interests lie in the design and verification of intelligent systems with applications to manufacturing and transportation. She published the book “Designing robot behavior in human-robot interactions” with CRC Press in 2019. She is the founder of the International Neural Network Verification Competition launched in 2020. Her work has been recognized by NSF Career Award, Amazon Research Award, Ford URP Award, Advanced Robotics for Manufacturing Champion Award, and many best/outstanding paper awards. Speaker: Prof. [ http://hybrid.eecs.berkeley.edu/ | Claire Tomlin ] , UC Berkeley Date: Friday, April 5th, 2024 Time: 12:30 PM Room: Bowen Hall 222 MAE Baetjer Colloquium Lunch will be served at 12:00 PM. Students: To meet with Claire Tomlin, please email Julia L. Brav at [ mailto:jbrav@princeton.edu | jbrav(a)princeton.edu ] no later than 5:00PM on Wednesday, April 3, and indicate your department. Title: Safe Learning in Control Abstract: In many applications of autonomy in robotics, guarantees that constraints are satisfied throughout the learning process are paramount. We present a controller synthesis technique based on the computation of reachable sets, using optimal control and game theory. Then, we present methods for combining reachability with learning-based methods, to enable performance improvement while maintaining safety, and to move towards safe robot control with learned models of the dynamics and the environment. We will discuss different interaction models with other agents, and some implications of model vs. learning-based predictions. Bio: Claire Tomlin is the James and Katherine Lau Professor and Chair of the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She was an Assistant, Associate, and Full Professor at Stanford from 1998-2007, and in 2005 she joined Berkeley. Claire works in hybrid systems and control and integrates machine learning methods with control theoretic methods in the field of safe learning. She works in the applications of air traffic and unmanned air vehicle systems. She is a MacArthur Fellow, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences.

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