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Events for November 09, 2017
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CS Colloquium: Li Xiong (Emory University) - Privacy-Preserving Data Sharing and Analytics with Differential Privacy
Thu, Nov 09, 2017 @ 01:30 PM - 02:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Li Xiong, Emory University
Talk Title: Privacy-Preserving Data Sharing and Analytics with Differential Privacy
Abstract: While Big Data promises significant value, it also raises increasing privacy concerns. In this talk, I will describe our efforts towards a comprehensive privacy-preserving data sharing and analytics framework. Following an overview of the framework, we discuss two settings based on state-of-the-art differential privacy techniques: 1) aggregated data sharing for data mining and analytics, and 2) individual location sharing for location based services. For aggregated sharing, I will present several technical solutions for handing different types of data including sequential and time series data, using medical and spatiotemporal data mining applications. For individual data sharing, I will present our approach towards a rigorous and customizable privacy notion extending the differential privacy framework for location protection, with location based applications such as nearest POI search and geospatial crowdsourcing.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in SOS B2, seats will be first come first serve.
Biography: Li Xiong is Professor of Computer Science and Biomedical Informatics at Emory University and holds a Winship Distinguished Research Professorship. She has a PhD from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from University of Science and Technology of China, all in Computer Science. She and her research group, Assured Information Management and Sharing (AIMS), conduct research that addresses both fundamental and applied questions at the interface of data privacy and security, spatiotemporal data management, and health informatics. She has published over 100 papers in premier journals and conferences including TKDE, JAMIA, VLDB, ICDE, CCS, and WWW, and has received four best paper awards. She currently serves as associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE) and on numerous program committees for data management and data security conferences. She is a recipient of a Google Research Award, IBM Smarter Healthcare Faculty Innovation Award, Cisco Research Award, and Woodrow Wilson Fellowship. Her research is supported by NSF (National Science Foundation), NIH (National Institute of Health), AFOSR (Air Force Office of Scientific Research), and PCORI (Patient-Centered Outcomes Research Institute).
Host: Muhammad Naveed
Location: Social Sciences Building (SOS) - B2
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Jimmy Ba (University of Toronto) - Progress and Challenges in Training Neural Networks
Thu, Nov 09, 2017 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jimmy Ba, University of Toronto
Talk Title: Progress and Challenges in Training Neural Networks
Series: Visa Research Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Optimization lies at the core of any deep learning systems. In this talk, I will first discuss the recent advances in optimization algorithms to train deep learning models. Then I will present a novel family of 2nd-order optimization algorithms that leverage distributed computing to significantly shortening the training time of neural networks with tens of millions of parameters. The talk will conclude by showing how our algorithms can be successfully applied to domains such as reinforcement learning and generative adversarial networks.
Biography: Jimmy is finishing his PhD with Geoff Hinton in the Machine Learning group at the University of Toronto. Jimmy will be a Computational Fellow at MIT before returning as full-time faculty to the CS department at UofT, as well as joining the Vector Institute. Jimmy completed his BAc, MSc at UofT working with Brendan Frey and Ruslan Salakhutdinov. He has previously spent time at Google Deepmind and Microsoft Research, and is a recipient of Facebook Graduate Fellowship for 2016 in machine learning. His primary research interests are in the areas of artificial intelligence, neural networks, and numerical optimization.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CAIS Seminar: Dr. John Clapp (University of Southern California) - A Systems Dynamic Approach to Understanding Heavy Drinking Events: Measures, Methods and Models
Thu, Nov 09, 2017 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. John Clapp, University of Southern California
Talk Title: A Systems Dynamic Approach to Understanding Heavy Drinking Events: Measures, Methods and Models
Series: Center for AI in Society (CAIS) Seminar Series
Abstract: Dr. Clapp will discuss a collaborative modeling effort among a team of engineers and social scientists to better understand the complex dynamics underlying heavy drinking at the event level. The goal of this ongoing effort is to develop invivo smart interventions aimed at changing problematic drinking trajectories to prevent event level problems including alcohol poisoning, drunk driving, and sexual assault. His presentation will cover the collaborative modeling effort, computation models, and validation measures and methods. The current state of the models and next steps will be discussed.
Biography: Dr. Clapp is Executive Vice Dean and Professor in the Suzanne Dworak-Peck School of Social Work. His work has focused largely on the etiology and prevention of acute alcohol-related problems.
Host: Milind Tambe
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
