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Events for March 17, 2022
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CS Colloquium: Amir Houmansadr (UMass Amherst) - Communication Secrecy in the Age of AI
Thu, Mar 17, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Amir Houmansadr, UMass Amherst
Talk Title: Communication Secrecy in the Age of AI
Series: CS Colloquium
Abstract: Internet users face constant threats to the secrecy of their communications: repressive regimes deprive them of open access to the Internet, corporations and surveillance organizations monitor their online behavior, advertising companies and social networks collect and share their private information, and cybercriminals hurt them financially by stealing their private information. In this talk, I will present the key research challenges facing communication secrecy in a world overtaken by the AI. In particular, I will introduce new ML-specific mechanisms to defeat AI-enabled surveillance. I will also discuss crucial AI trustworthiness research problems that are essential to the secrecy of Internet communications in the age of AI.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Amir Houmansadr is an associate professor of computer science at UMass Amherst. He received his Ph.D. from the University of Illinois at Urbana-Champaign in 2012, and spent two years at the University of Texas at Austin as a postdoctoral scholar. Amir is broadly interested in the security and privacy of networked systems. To that end, he designs and deploys privacy-enhancing technologies, analyzes network protocols and services (e.g., messaging apps and machine learning APIs) for privacy leakage, and performs theoretical analysis to derive bounds on privacy (e.g., using game theory and information theory). Amir has received several awards and recognitions including the 2013 IEEE S&P Best Practical Paper Award, a 2015 Google Faculty Research Award, an NSF CAREER Award in 2016, a CSAW 2019 Applied Research Competition Finalist, an IMC 2020 Best Paper Award Runner-up, and a Facebook 2021 Privacy Enhancing Technologies Award Finalist. He is an Associate Editor of the IEEE TDSC and frequently serves on the program committees of major security conferences.
Host: Barath Raghavan
Location: Olin Hall of Engineering (OHE) - 132
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Matthew Mirman (ETH Zürich) - Trustworthy Deep Learning: methods, systems and theory
Thu, Mar 17, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matthew Mirman , ETH Zürich
Talk Title: Trustworthy Deep Learning: methods, systems and theory
Series: CS Colloquium
Abstract: Deep learning models are quickly becoming an integral part of a plethora of high stakes applications, including autonomous driving and health care. As the discovery of vulnerabilities and flaws in these models has become frequent, so has the interest in ensuring their safety, robustness and reliability. My research addresses this need by introducing new core methods and systems that can establish desirable mathematical guarantees of deep learning models.
In the first part of my talk I will describe how we leverage abstract interpretation to scale verification to orders of magnitude larger deep neural networks than prior work, at the same time demonstrating the correctness of significantly more properties. I will then show how these techniques can be extended to ensure, for the first time, formal guarantees of probabilistic semantic specifications using generative models.
In the second part, I will show how to fuse abstract interpretation with the training phase so as to improve a model's amenability to certification, allowing us to guarantee orders of magnitude more properties than possible with prior work. Finally, I will discuss exciting theoretical advances which address fundamental questions on the very existence of certified deep learning.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Matthew Mirman is a final-year PhD student at ETH Zürich, supervised by Martin Vechev. His main research interests sit at the intersection of programming languages, machine learning, and theory with applications to creating safe and reliable artificial intelligence systems. Prior to ETH, he completed his B.Sc. and M.Sc. at Carnegie-Mellon University supervised by Frank Pfenning.
Host: Mukund Raghothaman
Location: 115
Audiences: By invitation only.
Contact: Assistant to CS chair
