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Colloquia

Spring 2017 Colloquia

Check back soon for more information on the computer science seminar series. Unless otherwise noted, the seminars meet on Mondays at 3pm in Stanley Thomas 302. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv.

Jan 23

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Abstract: TBA
Jan 30

Improving Tor’s Security with Trust-Aware Path Selection

Aaron Johnson Naval Research Laboratory

Abstract: Tor is a popular tool for low-latency anonymous communication, with over an estimated 1.5 million daily users. Tor users are vulnerable to deanonymization by an adversary that can observe some Tor relays or some parts of the network. We demonstrate that previous network-aware path-selection algorithms that propose to solve this problem are vulnerable to attacks across multiple Tor connections. We suggest that users use trust to choose the paths through Tor that are less likely to be observed, where trust is flexibly modeled as a probability distribution on the location of the user's adversaries, and we present the Trust-Aware Path Selection algorithm for Tor that helps users avoid traffic-analysis attacks while still choosing paths that could have been selected by many other users. We evaluate this algorithm in two settings using a high-level map of Internet routing: (i) users try to avoid a single global adversary that has an independent chance to control each Autonomous System organization, Internet Exchange Point organization, and Tor relay family, and (ii) users try to avoid deanonymization by any single country. Simulation results using data from the live Tor network show reductions of as much as 85% in the chance of deanonymization by a global adversary and 60% in the number of countries to which Tor users are vulnerable.

About the Speaker: Dr. Aaron Johnson is a computer scientist at the U.S. Naval Research Laboratory. His research interests include private communication and privacy-preserving data analysis. He has performed foundational mathematical research in the area of anonymous communication by modeling and analyzing the security of onion routing. He has also applied mathematically-rigorous privacy-preserving methods to publishing sensitive genetic and network data. Much of his work has been focused on the Tor network, which is an onion-routing network used by over 2 million users daily to secure their communications. He designed several improvements to Tor, including denial-of-service defenses, faster onion services, privacy-preserving network monitoring, and improvements to Tor's path selection. Many of these results have been incorporated into the Tor network and provide enhanced security, performance, and utility to its many users. Dr. Johnson received his Ph.D. in 2009 from the computer science department at Yale University and completed postdoctoral training at the University of Texas at Austin.
Feb 6

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Feb 13

Deep Learning for Natural Language Processing: Summarization and Language Vagueness

Fei Liu University of Central Florida

Abstract: Deep learning techniques have revolutionized the field of natural language processing in the past few years, yet there remain challenges and open problems. In this talk I will discuss two case studies where deep learning techniques are called upon to solve natural language processing problems: summarization is a classic NLP task, whereas modeling language vagueness is a new area of research where limited work has been done. I will focus the talk on leveraging the hierarchical attention networks for forum thread summarization, while providing overviews of other projects. Challenges, opportunities, and future works will be discussed toward the end of the talk.

About the Speaker: Dr. Fei Liu is an assistant professor of Computer Science at the University of Central Florida. Her research areas are natural language processing and machine learning. From 2013 to 2015, Fei was a postdoctoral fellow at Carnegie Mellon University, member of Noah's ARK. From 2011 to 2013, she worked as a senior research scientist at Bosch Research, Palo Alto, California. Fei received her Ph.D. in Computer Science from the University of Texas at Dallas in 2011. Prior to that, she obtained her Bachelors and Masters degrees in Computer Science from Fudan University, Shanghai, China. Fei was a recipient of a Best Paper Nomination at the 25th International World Wide Web Conference (WWW) in 2016. She served as an Area Chair for the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).
Feb 20

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Mar 6

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Mar 13

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Mar 20

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Apr 3

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Apr 10

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Apr 24

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May 1

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