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Conferences, Lectures, & Seminars
Events for January
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CS Colloquium: Gale Lucas (University of Southern California) - The Best of Both Worlds: Social Agents Leverage Rapport and Social Safety to Increase Trust
Thu, Jan 18, 2018 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Gale Lucas, University of Southern California
Talk Title: The Best of Both Worlds: Social Agents Leverage Rapport and Social Safety to Increase Trust
Series: Computer Science Colloquium
Abstract: There are risks and benefits to trusting others. For example, when one shares a secret, the discloser can experience benefits (e.g., catharsis, sometimes even health benefits); however, they have to trust the recipient won't use or hold it against them. There are two key factors that increase willingness to engage in such actions that require trust. The first is social safety: the sense that one's identity is protected (i.e., anonymous) and won't be judged. The second is rapport: the harmony, fluidity, synchrony, and flow felt during interaction. These two factors -social safety and rapport- are normally set in opposition to each other. The former is maximized in the absence of another human, while the latter is maximized in intensive face-to-face (i.e., non-anonymous) interactions. Thus, usually, there is a trade-off, where either social safety or rapport has to be chosen, but not both. Social agents (virtual humans or robots), however, offer the best of both worlds. They can engage in rapport-building like their human counterparts, but also foster a sense of social safety (anonymity, lack of judgement). In this talk, I present research showing how social safety and rapport, both together and separately, can be leveraged to increase trust in agents and robots. I discuss effects across various user outcomes related to trust: sharing personal information and honest disclosure, as well as feeling comfortable practicing negotiation with social agents, trusting them to control the physical environment, and taking their advice. Finally, I discuss implications for user design and describe possibilities for future research.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in OHE 100D, seats will be first come first serve.
Biography: Gale M. Lucas is a Senior Research Associate at University of Southern California's Institute for Creative Technologies (ICT). While earning her PhD from Northwestern University, she was awarded a National Science Foundation Graduate Research Fellowship to test models of emotion, motivation, and social interaction. After completing her doctorate, she spent two years teaching in a liberal arts context. She then went on to complete her post-doctoral work at ICT, where she established a research program in the areas of Affective Computing and Human-Computer Interaction. Now as a Senior Research Associate, she continues her line of work in affective and personality computing that focuses on models predicting mental health, perceptions of trust and emotion in real-world situations. Her work in HCI is centered around understanding how various social factors affect trust in agents and robots.
Host: Kevin Knight
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Joshua Garcia (UC Irvine) - Automated Android Security Assessment: Malware, Vulnerabilities, and Exploits
Thu, Jan 18, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Joshua Garcia, UC Irvine
Talk Title: Automated Android Security Assessment: Malware, Vulnerabilities, and Exploits
Series: Computer Science Colloquium
Abstract: Android has become the dominant mobile platform. Millions of Android apps have been produced and disseminated across app markets, spurred by the relative ease of construction using the Android development framework. Unfortunately, this ease of dissemination and construction, and access to millions of users, has attracted malicious app developers and contributed to a growing number of exploitable software vulnerabilities. In this talk, to address these aforementioned challenges, I present two approaches for Android security assessment that I have constructed: LetterBomb, the first approach for automatically generating exploits for Android apps, and RevealDroid, a lightweight, obfuscation-resilient approach for malware detection and family identification that leverages machine learning and static analysis of both conventional and unconventional code (i.e., reflective code and native code).
In the first part of this talk, I introduce LetterBomb, which relies on a combined path-sensitive symbolic execution-based static analysis, and the use of software instrumentation and test oracles. I ran LetterBomb on 10,000 Android apps from Google Play, where I identified nearly 200 exploits from over 800 vulnerable apps, including popular apps with up to 10 million downloads. Compared to a state-of-the-art detection approach for three ICC-based vulnerabilities, LetterBomb obtains 30%-60% more vulnerabilities at a 7 times faster speed.
In the second part of this talk, I present RevealDroid, which operates without the need to perform complex program analyses or to extract large sets of features, and examines unconventional code. Specifically, our selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. I assessed RevealDroid on more than 54,000 malicious and benign apps, where it achieved an accuracy of 98% for detection of malware, an accuracy of 95% for determination of their families, and very high obfuscation resiliency. I further demonstrate RevealDroid's superiority against state-of-the-art approaches.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Joshua Garcia is a Postdoctoral Researcher at the Institute for Software Research at the University of California, Irvine (UCI) and the Software Engineering and Analysis Lab at UCI's Department of Informatics in the Donald Bren School of Information and Computer Sciences. His current research interests including mobile security, testing, and analysis-”and addressing problems of software architectural drift and erosion. He received three degrees from the University of Southern California: a B.S. in computer engineering and computer science, an M.S. in computer science, and a Ph.D. in computer science. His industrial experience includes software-engineering or research positions at the NASA Jet Propulsion Laboratory, the Southern California Earthquake Center, and Xerox Special Information Systems.
Host: Chao Wang
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Distinguished Lecture: Ronitt Rubinfeld (MIT and Tel Aviv University) - Testing Properties of Distributions Over Big Domains
Tue, Jan 23, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ronitt Rubinfeld, MIT and Tel Aviv University
Talk Title: Testing Properties of Distributions Over Big Domains
Series: Computer Science Distinguished Lecture Series
Abstract: We describe an emerging research direction regarding the complexity of testing global properties of discrete distributions, when given access to only a few samples from the distribution. Such properties might include testing if two distributions have small statistical distance, testing various independence properties, testing whether a distribution has a specific shape (such as monotone decreasing, k-modal, k-histogram, monotone hazard rate,...), and approximating the entropy. We describe bounds for such testing problems whose sample complexities are sublinear in the size of the support.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Ronitt Rubinfeld is a professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory. Ronitt's main research area is theory of computation. Ronitt received her PhD from the University of California, Berkeley in 1991, and prior to that graduated from the University of Michigan with a BSE in Electrical and Computer Engineering. Before coming to MIT, Ronitt held postdoctoral researcher positions at Princeton University and Hebrew University. In 1992, she joined the faculty of the Computer Science Department at Cornell University, where she was an ONR Young Investigator, a Sloan Research Fellow, the 1995 Cornell Association for Computer Science Undergraduates Faculty of the Year, and a recipient of the Cornell College of Engineering Teaching Award. From 1999 to 2003, Ronitt was a Senior Research Scientist at NEC Research Laboratories, and in 2004, she was a Fellow at the Radcliffe Institute for Advanced Study.
Ronitt's research interests include randomized and sublinear time algorithms. In particular, her work focuses on what can be understood about data by looking at only a very small portion of it.
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Jay Pujara (University of Southern California) - Probabilistic Models for Large, Noisy, and Dynamic Data
Thu, Jan 25, 2018 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jay Pujara, University of Southern California
Talk Title: Probabilistic Models for Large, Noisy, and Dynamic Data
Series: Computer Science Colloquium
Abstract: We inhabit a vast, uncertain, and dynamic universe. To succeed in such an environment, artificial intelligence approaches must handle massive amounts of noisy, changing evidence. My research addresses the problems of building scalable, probabilistic models amenable to online updates. To illustrate the potential of such models, I present my work on knowledge graph identification, which jointly resolves the entities, attributes, and relationships in a knowledge graph by combining statistical NLP signals and semantic constraints. Using probabilistic soft logic, a statistical relational learning framework I helped develop, I demonstrate how knowledge graph identification can scale to millions of uncertain candidate facts and tens of millions of semantic dependencies in real-world data while achieving state-of-the-art performance. My work further extends this scalability by adopting a distributed computing approach, reducing the inference time of knowledge graph identification from two hours to ten minutes. Updating large, collective models like those used for knowledge graphs with new information poses a significant challenge. I develop a regret bound for probabilistic models and use this bound to motivate practical algorithms that support low-regret updates while improving inference time over 65%. Finally, I highlight several active projects in causal explanation, sustainability, bioinformatics, and mobile analytics that provide a promising foundation for future research.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in OHE 100D, seats will be first come first serve.
Biography: Jay Pujara is a research scientist at the University of Southern California's Information Sciences Institute whose principal areas of research are machine learning, artificial intelligence, and data science. He completed a postdoc at UC Santa Cruz, earned his PhD at the University of Maryland, College Park and received his MS and BS at Carnegie Mellon University. Prior to his PhD, Jay spent six years at Yahoo! working on mail spam detection, user trust, and contextual mail experiences, and he has also worked at Google, LinkedIn and Oracle. Jay is the author of over thirty peer-reviewed publications and has received three best paper awards for his work. He is a recognized authority on knowledge graphs, and has organized the Automatic Knowledge Base Construction (AKBC) and Statistical Relational AI (StaRAI) workshops, has presented tutorials on knowledge graph construction at AAAI and WSDM, and has had his work featured in AI Magazine. For more information, visit https://www.jaypujara.org
Host: Stefan Scherer
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Laurie Williams (NCSU) - If Not Us, Then Who?
Thu, Jan 25, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Laurie Williams, NCSU
Talk Title: If Not Us, Then Who?
Series: Computer Science Colloquium
Abstract: Stolen personal information, hospitals shutdown until they pay in bitcoin to get their data back, spying through our smart TVs-“ cybersecurity breaches are in the news every day. Cyberspace will not be more secure until engineers build more secure systems. Each of has a role in a more secure cyberspace. Teachers have to teach students how to develop securely; researchers have to understand the attackers' motives and actions and develop techniques that can be easily adopted to stop those attackers in their tracks; practitioners need to adopt secure development practices into their workflow; users need to interact with computers more prudently. This talk will present trend analysis obtained by an extensive and longitudinal interview study of security professionals in six business verticals over a five-year period. The interviews from more than 100 companies worldwide focused on the technical and organizational practices adopted by the companies in their quest to develop more secure software. This talk will present lessons learned that can guide all of us in our role toward a more secure cyberspace.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Laurie Williams is the Interim Department Head of Computer Science and a Professor in the Computer Science Department of the College of Engineering at North Carolina State University (NCSU). Laurie is a co-director of the NCSU Science of Security Lablet sponsored by the National Security Agency. Laurie's research focuses on software security; agile software development practices and processes; software reliability, and software testing and analysis. In 2018, Laurie was names an IEEE Fellow for contributions to reliable and secure software engineering.
Host: Chao Wang
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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MASCLE Machine Learning Seminar: David Sontag (MIT) - When Inference is Tractable
Tue, Jan 30, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Sontag, MIT
Talk Title: When Inference is Tractable
Series: Visa Research Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: A key capability of artificial intelligence will be the ability to reason about abstract concepts and draw inferences. Where data is limited, probabilistic inference in graphical models provides a powerful framework for performing such reasoning, and can even be used as modules within deep architectures. But, when is probabilistic inference computationally tractable? I will present recent theoretical results that substantially broaden the class of provably tractable models by exploiting model stability (Lang, Sontag, Vijayaraghavan, AI Stats '18), structure in model parameters (Weller, Rowland, Sontag, AI Stats '16), and reinterpreting inference as ground truth recovery (Globerson, Roughgarden, Sontag, Yildirim, ICML '15).
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: David Sontag joined MIT in January 2017 as Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) and Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Sontag's research focuses on machine learning and artificial intelligence; at IMES, he leads a research group that aims to use machine learning to transform health care.
Previously, he was an assistant professor in computer science and data science at New York University's Courant Institute of Mathematical Sciences and a postdoctoral researcher at Microsoft Research New England. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
