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Mithun Mukherjee <[log in to unmask]>
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Mithun Mukherjee <[log in to unmask]>
Wed, 20 Nov 2019 11:14:58 +0000
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WAAS: Workshop on Assured Autonomous Systems- Call for Papers
WAAS: Workshop on Assured Autonomous Systems
In conjunction with
41st IEEE Symposium on Security and Privacy Workshops
MAY 21, 2020, San Francisco, CA

Paper submission deadline: 1/19/2020 (Anywhere on Earth)
Acceptance notification: 2/17/2020
Publication-ready Papers Due: 3/6/2020
Workshop: 5/21/2020

The Workshop on Assured Autonomous Systems (WAAS) plans to address the gap that exists between theory-heavy autonomous systems and algorithms and the privacy, security, and safety of their real-world implementations. Advances in machine learning and artificial intelligence have shown great promise in automating complex decision-making processes across transportation, critical infrastructure, and cyber infrastructure domains. Practical implementations of these algorithms require significant systems engineering and integration support, especially as they integrate with the physical world. This integration is wrought with artificial intelligence (AI) safety, security, and privacy issues.
The primary focus of this workshop is the: (1) detection of, (2) response to, and (3) recovery from AI safety, security, and privacy violations in autonomous systems. Key technical challenges include discriminating between application-layer data breaches and benign process noises, responding to breaches and failures in real-time systems, and recovering from decision making failures autonomously.

WAAS seeks contributions on all aspects of AI safety, security, and privacy in autonomous systems. Papers that encourage the discussion and exchange of experimental and theoretical results, novel designs, and works in progress are preferred. Topics of interest include (but are not limited to):

Detecting dataset anomalies that lead to unsafe AI decisions
Engineering trusted AI software architectures
Status of existing approaches in ensuring AI/ML safety and gaps to be addressed
AI safety considerations and experience from industry
Evaluating safety of AI systems according to their potential risks and vulnerabilities
Resilient, explainable deep learning, and interpretable machine learning
Game theoretic analysis on machine learning models
Misuse of AI and deep learning

Detecting dataset anomalies that lead to autonomous system security and privacy violations
Differential privacy and privacy-preserving learning and generative models
Adversarial attacks on machine learning and defenses against adversarial attacks
Theoretical foundations of machine learning security
Formal verification of machine learning models and systems
Define and understand AI vulnerabilities and exploitable bugs in ML systems
Improve resiliency of AI methods and algorithms to various forms of attacks

You are invited to submit regular papers of up to six pages, or four pages for works in progress, including references. To be considered, papers must be received by the submission deadline. Submissions must be original work and may not be under submission to another venue at the time of review. Please mark all of your conflicts of interest when submitting your paper.
Papers must be formatted for US letter (not A4) size paper and MUST be double blind. The text must be formatted in a two-column layout, with columns no more than 9.5 in. tall and 3.5 in. wide. The text must be in Times font, 10-point or larger, with 11-point or larger line spacing. Authors are strongly recommended to use the latest IEEE conference proceedings templates. Failure to adhere to the page limit and formatting requirements are grounds for rejection without review.

All accepted submissions will be presented at the workshop and included in the IEEE workshop proceedings. Due to time constraints, accepted papers will be selected for presentation as either talk or poster based on their review score and novelty. Nonetheless, all accepted papers should be considered as having equal importance.
One author of each accepted paper is required to attend the workshop and present the paper for it to be included in the proceedings.

Submissions should be made online at:

Lanier Watkins, Johns Hopkins University & Applied Physics Lab

Howard Shrobe, MIT Computer Science & Artificial Intelligence Lab

Chris Rouff, Johns Hopkins University Applied Physics Lab

Reza Ghanadan, Google

Natalia Alexandrov, NASA Langley
Yair Amir, Johns Hopkins University
Saurabh Bagchi, Purdue University
Raheem Beyah, Georgia Institute of Technology
Yinzhi Cao, John Hopkins University
Anupam Chattopadhyay, Singapore Nanyang Technological University
Joel Coffman, United States Air Force Academy
Misty Davies, NASA Ames Research Center
David Doria, HERE Technologies
Abhishek Dubey, Vanderbilt University
Ashutosh Dutta, Johns Hopkins University Applied Physics Lab
Mike Hinchey, University of Limerick
Dezhi Hong, University of California San Diego
Yan Huang, Indiana University
John H. Hurley, National Defense University
Avinash Kalyanaraman, University of Virginia
Gabor Karsai, Vanderbilt University
Mykel Kochenderfer, Stanford University
Xenofon Koutsoukos, Vanderbilt University
Jose A. Morales, Carnegie Mellon University
Sirajum Munir, Bosch Research and Technology Center
William H. Robinson, Vanderbilt University
Yasser Shoukry, University of Maryland
Houbing Song, Embry-Riddle University
Tamim Sookoor, Johns Hopkins University Applied Physics Lab
Roy Sterritt, Ulster University
Jeremy Straub, University of North Dakota
A. Selcuk Uluagac, Florida International University
Kristen Walcott, University of Colorado
Louis Whitcomb, Johns Hopkins University
Paul Wood, Johns Hopkins University Applied Physics Lab

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