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Conferences, Lectures, & Seminars
Events for February
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CS Colloquium: Nanyun Peng (University of Southern California) – Jointly Learning Representations for Low Resource Information Extraction
Thu, Feb 01, 2018 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nanyun Peng , University of Southern California
Talk Title: Jointly Learning Representations for Low Resource Information Extraction
Series: Computer Science Colloquium
Abstract: There is abundant knowledge out there carried in the form of natural language texts, such as social media posts, scientific research literature, medical records, etc., which grows at an astonishing rate. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. Information extraction (IE) processes raw texts to produce machine understandable structured information, thus dramatically increasing the accessibility of knowledge through search engines, interactive AI agents, and medical research tools. However, traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In this talk, I will present how to leverage the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct information extraction with scarce human annotations.
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: Nanyun Peng is a computer scientist at Information Science Institute. She got her Ph.D at Johns Hopkins University. She is broadly interested in Natural Language Processing, Machine Learning, and Information Extraction. Her research focuses on low-resource information extraction, creative language generation, and phonology/morphology modeling. Nanyun is the recipient of the Johns Hopkins University 2016 Fred Jelinek Fellowship. She has a background in computational linguistics and economics and holds BAs in both from Peking University.
Host: David Traum
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
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MASCLE Machine Learning Seminar: Dr. Harry Shum (Microsoft)
Mon, Feb 05, 2018 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Harry Shum, Microsoft
Talk Title: TBA
Series: MASCLE NVIDIA Distinguished Lecture Series
Abstract: TBA
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in MCB 101, seats will be first come first serve.
Biography: Harry Shum is executive vice president of Microsoft's Artificial Intelligence (AI) and Research group. He is responsible for driving the company's overall AI strategy and forward-looking research and development efforts spanning infrastructure, services, apps and agents. He oversees AI-focused product
groups -” the Information Platform Group, Bing and Cortana product groups -” and the Ambient Computing and Robotics teams. He also leads Microsoft Research, one of the world's premier computer science research organizations, and its integration with the engineering teams across the company.
Previously, Dr. Shum served as the corporate vice president responsible for Bing search product development from 2007 to 2013. He received his Ph.D. in robotics from the School of Computer Science at Carnegie Mellon University. In 2017, he was elected to the National Academy of Engineering of the United States.
Host: Yan Liu
Location: Michelson Center for Convergent Bioscience (MCB) - Michelson Center 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CAIS Seminar: Dr. Robin Stevens (University of Pennsylvania) - Hashtag HIV?: Using Digital Epidemiology to advance HIV prevention among youth
Wed, Feb 21, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Robin Stevens, University of Pennsylvania
Talk Title: Hashtag HIV?: Using Digital Epidemiology to advance HIV prevention among youth
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Racial and ethnic disparities in HIV remain a public health priority in the U.S. In this talk, Dr. Stevens will discuss her research investigating associations between offline and online sexual risk behavior among African American and Latino youth. She will also discuss her ongoing Virus to Viral study, that seeks to provide a characterization of HIV risk and prevention behaviors on Twitter, with the goal of offering insights on effective messaging strategies that leverage social media. She will also discuss her approach to conducting community-engaged, theory driven digital epidemiology.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Robin Stevens is a health communication scholar focused on achieving health equity in African American and Latino communities. Her research integrates public health and communication science to influence the individual, social and structural determinants that drive health inequity. Her area of expertise is in examining the relationship between new and traditional media and youth risk behavior. Dr. Stevens is an Assistant Professor of Nursing at the University of Pennsylvania School of Nursing and the Director of the Health Equity & Media Lab.
Host: Milind Tambe
Location: Seeley G. Mudd Building (SGM) - 123
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Pratik Chaudhari (UCLA) – A Picture of the Energy Landscape of Deep Neural Networks
Thu, Feb 22, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Pratik Chaudhari, UCLA
Talk Title: A Picture of the Energy Landscape of Deep Neural Networks
Series: Computer Science Colloquium
Abstract: Deep networks are mysterious. These over-parametrized machine learning models, trained with rudimentary optimization algorithms on non-convex landscapes in millions of dimensions, have defied attempts to put a sound theoretical footing beneath their impressive performance.
This talk will shed light upon some of these mysteries. I will employ diverse ideas ---from thermodynamics and optimal transportation to partial differential equations, control theory and Bayesian inference--- and paint a picture of the training process of deep networks. Along the way, I will develop state-of-the-art algorithms for non-convex optimization.
The goal of machine perception is not just to classify objects in images but instead, enable intelligent agents that can seamlessly interact with our physical world. I will conclude with a vision of how advances in machine learning and robotics may come together to help build such an Embodied Intelligence.
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: Pratik Chaudhari is a PhD candidate in Computer Science at UCLA where he works with Stefano Soatto. His research interests include deep learning, robotics and computer vision. He has worked on perception and control algorithms for safe autonomous urban navigation as a part of nuTonomy Inc. Pratik holds Master's and Engineer's degrees from MIT and a Bachelor's degree from IIT Bombay in Aeronautics and Astronautics.
Website: pratikac.info
Host: Computer Science Department
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Satish Kumar Thittamaranahalli (USC) – The Constraint Composite Graph and Its Applications
Tue, Feb 27, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Satish Kumar Thittamaranahalli, USC Information Sciences Institute
Talk Title: The Constraint Composite Graph and Its Applications
Series: Computer Science Colloquium
Abstract: The weighted constraint satisfaction problem (WCSP) is a fundamental combinatorial problem with applications in such diverse areas as artificial intelligence, statistical physics, computer vision, and information theory. In this talk, I will present new methods for efficiently solving the WCSP. Central to these methods is the idea of the constraint composite graph (CCG). The CCG provides a unifying computational framework for simultaneously exploiting the structure of the variable-interactions in a given WCSP as well as the structure of the weighted constraints in it. I will present some important applications of the idea of the CCG in kernelization of combinatorial problems, the revival of message passing algorithms, and in other domains (if time permits).
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: Dr. Satish Kumar Thittamaranahalli (T. K. Satish Kumar) leads the Collaboratory for Algorithmic Techniques and Artificial Intelligence at the Information Sciences Institute of the University of Southern California. He has published extensively on numerous topics in Artificial Intelligence spanning such diverse areas as Constraint Reasoning, Planning and Scheduling, Probabilistic Reasoning, Robotics, Combinatorial Optimization, Approximation and Randomization, Heuristic Search, Model-Based Reasoning, Knowledge Representation and Spatio-Temporal Reasoning. He has served on the Program Committees of many international conferences in Artificial Intelligence and is a winner of the 2016 Best Robotics Paper Award and the 2005 Best Student Paper Award from the International Conference on Automated Planning and Scheduling. Dr. Kumar received his PhD in Computer Science from Stanford University in March 2005. In the past, he has also been a Visiting Student at the NASA Ames Research Center, a Postdoctoral Research Scholar at the University of California, Berkeley, a Research Scientist at the Institute for Human and Machine Cognition, a Visiting Assistant Professor at the University of West Florida, and a Senior Research and Development Scientist at Mission Critical Technologies.
Host: Computer Science Department
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
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George A. Bekey Distinguished Lecture with Professor Stefan Savage (UCSD)
Tue, Feb 27, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Stefan Savage, University of California, San Diego
Talk Title: Modern Automotive Vulnerabilities: Problems, Causes and Outcomes
Series: Computer Science Keynote Series
Abstract: Over the last six years, a range of research has transformed our understanding of automobiles. What we traditionally envisioned as mere mechanical conveyances are now more widely appreciated as complex distributed systems 'with wheels'. A car purchased today has virtually all aspects of its physical behavior mediated through dozens of microprocessors, themselves networked internally, and connected to a range of external digital channels. As a result, software vulnerabilities in automotive firmware potentially allow an adversary to obtain arbitrary control over the vehicle. Indeed, multiple research groups have been able to demonstrate such remote control of unmodified automobiles from a variety of manufacturers. In this talk, I'll highlight how our understanding of automotive security vulnerabilities has changed over time, how unique challenges in the automotive sector give rise to these problems and create non-intuitive constraints on their solutions and the key role played by the research community driving industry and government response.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Stefan Savage is a professor of Computer Science and Engineering at the University of California, San Diego. He received his Ph.D. in Computer Science and Engineering from the University of Washington and a B.S. in Applied History from Carnegie Mellon University. Savage is a full-time empiricist, whose research interests lie at the intersection of computer security, distributed systems and networking. He currently serves as co-director of UCSD's Center for Network Systems (CNS) and for the Center for Evidence based Security Research (CESR). Savage is a MacArthur Fellow, a Sloan Fellow, an ACM Fellow, and is a recipient of the ACM Prize in Computing and the ACM SIGOPS Weiser Award. He currently holds the Irwin and Joan Jacobs Chair in Information and Computer Science, but is a fairly down-to-earth guy and only writes about himself in the third person when asked.
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: Yinzhi Cao (Lehigh University) – Testing and Repairing Machine Learning Systems in Adversarial Environment
Wed, Feb 28, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yinzhi Cao, Lehigh University
Talk Title: Testing and Repairing Machine Learning Systems in Adversarial Environment
Series: Computer Science Colloquium
Abstract: Machine learning (ML) systems are increasingly deployed in safety- and security-critical domains such as self-driving cars and malware detection, where the system correctness for corner case inputs are crucial. Existing testing of ML system correctness depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.
In this talk, I will present the first framework to test and repair ML systems, especially in an adversarial environment. In the first part, I will introduce DeepXplore, a whitebox testing framework of real-world deep learning (DL) systems. Our evaluation shows that DeepXplore can successfully find thousands of erroneous corner case behaviors, e.g., self-driving cars crashing into guard rails and malware masquerading as benign software. In the second part, I will introduce machine unlearning, a general, efficient approach to repair an ML system exhibiting erroneous behaviors. Our evaluation, on four diverse learning systems and real-world workloads, shows that machine unlearning is general, effective, fast, and easy to use.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in RTH 115, seats will be first come first serve.
Biography: Yinzhi Cao is an assistant professor at Lehigh University. He earned his Ph.D. in Computer Science at Northwestern University and worked at Columbia University as a postdoc. Before that, he obtained his B.E. degree in Electronics Engineering at Tsinghua University in China. His research mainly focuses on the security and privacy of the Web, smartphones, and machine learning. He has published many papers at various security and system conferences, such as IEEE S&P (Oakland), NDSS, CCS, and SOSP. His JShield system has been adopted by Huawei, the world's largest telecommunication company. His past work was widely featured by over 30 media outlets, such as NSF Science Now (Episode 38), CCTV News, IEEE Spectrum, Yahoo! News and ScienceDaily. He received two best paper awards at SOSP'17 and IEEE CNS'15 respectively.
Host: Muhammad Naveed
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CAIS Seminar: Dr. Henry Kautz (University of Rochester) – Mining Social Media to Improve Public Health
Wed, Feb 28, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Henry Kautz, University of Rochester
Talk Title: Mining Social Media to Improve Public Health
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: People posting to social media on smartphones can be viewed as an organic sensor network for public health data, picking up information about the spread of disease, lifestyle factors that influence health, and pinpointing sources of disease. We show how a faint but actionable signal can be detected in vast amounts of social media data using statistical natural language and social network models. We present case studies of predicting influenza transmission and per-city rates, discovering patterns of alcohol consumption in different neighborhoods, and tracking down the sources of foodborne illness.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Henry Kautz is the Robin & Tim Wentworth Director of the Goergen Institute for Data Science and Professor in the Department of Computer Science at the University of Rochester. He has served as department head at AT&T Bell Labs in Murray Hill, NJ, and as a full professor at the University of Washington, Seattle. In 2010 he was elected President of the Association for Advancement of Artificial Intelligence, and in 2016 was elected Chair of the AAAS Section on Information, Computing, and Communication. His research in artificial intelligence, pervasive computing, and healthcare applications has led him to be honored as a Fellow of the American Association for the Advancement of Science, Fellow of the Association for Computing Machinery, and Fellow of the AAAI.
Host: Milind Tambe
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
