Ryan O'Donnell
My research is in the areas of quantum computation, quantum complexity theory, and quantum information. I am particularly interested in the mathematical theory of quantum tomography, as well as related problems concerning testing, certifying, and learning quantum states with low sample complexity and computational complexity. I like to apply a wide range of mathematical tools (representation theory, probability, combinatorics, Fourier analysis) when attacking problems in the theory of quantum computation and quantum information.
- "Optimal Inapproximability Results for MAX‐CUT and Other 2‐Variable CSPs?," Subhash Khot, Guy Kindler, Elchanan Mossel, and Ryan O’Donnell, SIAM J. Comput.,37, 319 (2007)
- "Analysis of Boolean Functions," Ryan O’Donnell,Cambridge University Press (2014).
- "Noise stability of functions with low influences: invariance and optimality,"Elchanan Mossel, Ryan O'Donnell, Krzysztof Oleszkiewicz, FOCS 171, 295 (2005).
- "Learning functions of k relevant variables," Elchanan Mossel, Ryan O’Donnell, and Rocco A. Servedioc, Journal of Computer and System Sciences 69, 421 (2004).
- "Learning intersections and thresholds of halfspaces," Adam R. Klivans, Ryan O’Donnell, and Rocco A. Servedio, Journal of Computer and System Sciences 68, 808 (2004).
- "Optimal mean-based algorithms for trace reconstruction." De, Anindya, Ryan O’Donnell, and Rocco A. Servedio. The Annals of Applied Probability 29, no. 2 (2019): 851-874.
- "The threshold for SDP-refutation of random regular NAE-3SAT." Deshpande, Yash, Andrea Montanari, Ryan O'Donnell, Tselil Schramm, and Subhabrata Sen. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 2305-2321. Society for Industrial and Applied Mathematics (2019).
- "Learning sparse mixtures of rankings from noisy information." De, Anindya, Ryan O'Donnell, and Rocco Servedio. arXiv preprint arXiv:1811.01216 (2018).
- "A log-Sobolev inequality for the multislice, with applications." Filmus, Yuval, Ryan O'Donnell, and Xinyu Wu. arXiv preprint arXiv:1809.03546 (2018).
- "SOS lower bounds with hard constraints: think global, act local." Kothari, Pravesh, Ryan O'Donnell, and Tselil Schramm. arXiv preprint arXiv:1809.01207 (2018).
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Jimmy Zhu received his B.S. in Physics from Huazhong University of Science and Technology in China and his M.S. and Ph.D., also in Physics, from the University of California at San Diego. He was an assistant professor and later associate professor in the Department of Electrical Engineering at the University of Minnesota before joining the Department of Electrical and Computer Engineering of Carnegie Mellon University in 1996. He received an NSF Presidential Young Investigator Award in 1993 and the 1996 R&D100 Award for the CPP/GMR read sensor patent, one out of the twelve U.S. patents he currently holds.
Zachary W. Ulissi joined Carnegie Mellon University in 2017, after doing his PhD at MIT and post-doc at Stanford. His research at MIT focused on the the applications of systems engineering methods to understanding selective nanoscale carbon nanotube devices and sensors under the supervision of Michael Strano and Richard Braatz. Prof. Ulissi did his postdoctoral work at Stanford with Jens Nørskov where he worked on machine learning techniques to simplify complex catalyst reaction networks, applied to the electrochemical reduction of N2 and CO2 to fuels. The Ulissi group builds on this foundation to model, understand, and design nanoscale interfaces using modern predictive methods to guide detailed molecular simulations.
Dr. George is Department Chair, R&H Mickle Endowed Chair, and Professor of Electrical and Computer Engineering at the University of Pittsburgh. He is Founder and Director of the NSF Center for Space, High-performance, and Resilient Computing (SHREC), a national research center and consortium founded in 2017 and headquartered at Pitt. SHREC features nearly 40 academic, industry, and government partners and is considered by many as the top research center in its field.