2022

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Visible to the public  39th International Conference on Machine Learning (ICML 2022)
Jul 17, 2022 7:30 am - Jul 23, 2022 7:30 pm EDT

The 39th International Conference on Machine Learning (ICML 2022) will be held in Baltimore, Maryland USA July 17-23, 2022 and is planned to be an in-person conference with virtual elements. In addition to the main conference sessions, the conference will also include Expo, Tutorials, and Workshops. Please submit proposals to the appropriate chairs.

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Visible to the public  2022 NSF-MOST Workshop on CPS & SCC
Mar 01, 2022 7:00 pm - 10:00 pm EST

2022 NSF-MOST Workshop on Cyber Physical Systems (CPS) & Smart and Connected Communities (SCC)

March 2nd 2022, 8-11 a.m. (GMT+8)
March 1st 2022, 7-10 p.m. (EST, GMT-5)

https://event.nchc.org.tw/2022/cps_scc/
Register: https://event.nchc.org.tw/2022/cps_scc/registration/

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Visible to the public  CPS Rising Stars Workshop 2022
May 26, 2022 7:00 am - 6:00 pm EDT

CPS Rising Stars Workshop 2022 aims to identify and mentor outstanding PhD students and postdocs who are interested in pursuing academic careers in Cyber-Physical Systems (CPS) related areas. CPS are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components. CPS technologies are transforming the way people interact with engineered systems, just as the Internet has transformed the way people interact with information.

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Visible to the public Solicitation: NSF Secure and Trustworthy CyberSpace (SaTC) [Solicitation 22-517]

Secure and Trustworthy Cyberspace (SaTC)

PROGRAM SOLICITATION
NSF 22-517

REPLACES DOCUMENT(S):
NSF 21-500

National Science Foundation

Directorate for Computer and Information Science and Engineering
     Division of Computer and Network Systems
     Division of Computing and Communication Foundations
     Division of Information and Intelligent Systems
     Office of Advanced Cyberinfrastructure

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Visible to the public CPS 2022 - Travel Reimbursement & Honoraria

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Visible to the public A Look at Resilience Breakdowns of Human-assisted Cyber Reasoning Systems

Yan Shoshitaishvili is an Assistant Professor at Arizona State University, where he pursues parallel passions of cybersecurity research, real-world impact, and education. His research focuses on automated program analysis and vulnerability detection techniques. Aside from publishing dozens of research papers in top academic venues, Yan led Shellphish's participation in the DARPA Cyber Grand Challenge, achieving the creation of a fully autonomous hacking system that won third place in the competition.

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Visible to the public Resiliency in Systems Engineering Context

Bill Scherlis is a professor of computer science at Carnegie Mellon University (CMU). He recently completed a second tour at DARPA, where he served as Director of the Information Innovation Office (I2O) from Sept 2019 to May 2022 and as Special Assistant to the Director from May to Sept 2022. As I2O director, he led program managers in the development of research programs in cyber operations, secure and resilient systems, AI, and information operations.

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Visible to the public Attestation and Time

Perry Alexander is the AT&T Foundation Distinguished Professor of Electrical and Computer Science and Director of the Institute for Information Sciences at the University of Kansas. His research and teaching interests include formal verification and synthesis, trusted systems, and programming language semantics.

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Visible to the public CyberAI: Defense through Offense

Dr. Paul Roysdon is the Leidos AI/ML Chief Scientist and both a Technical Fellow and Master-level Solution Architect. He presently provides technical leadership in the Leidos Artificial Intelligence and Machine Learning (AI/ML) Accelerator and leads the Leidos Cyber AI research portfolio.

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Visible to the public Cognitive Aid for Vulnerability Analysis (CAVA)

Rajan Bhattacharyya is Director of R&D in the Intelligent Systems Laboratory at HRL Laboratories. His research interests include neurocognitive modeling, brain-machine interfaces, and AI. Rajan received a PhD in Computation and Neural Systems from Caltech, and a Bachelor's of Science in Electrical Engineering and Computer Science from UC Berkeley.