Keynote Speeches and Panels
Monday, August 4th
Keynote: Big Data Science and Social Networks - Accelerating Insights and Building Value
Speaker: Alok N. Choudhary , Northwestern University, USA
Abstract: Knowledge discovery has been driven by theory, experiments and by large-scale simulations on high-performance computers. Modern experiments and simulations involving satellites, high-throughput instruments, sensors, and supercomputers yield massive amounts of data. What has changed recently is that the world is creating massive amounts of data at an astonishing pace and diversity. Concurrently, social communities such as Facebook, Twitter, Google, and others are creating massive amounts of data at an astonishing pace. But, even more complex is the network that connects the creators of data. There is knowledge to be discovered in both for actionable insights.
In this talk I present that processing, mining and analyzing this data effectively is a critical component for knowledge discovery. And we can no longer rely upon traditional ways of dealing with the data due to its scale and speed. In particular, I present approaches and many examples to derive science and business value. Examples include deriving knowledge from massive data for marketing and other applications, understanding sentiments, determining influencers based on dynamic networks. Particular examples from major social networks will be used. Then I connect the applicability of approaches to addressing important science problems including in climate change understanding and medicine.
Biography: Alok Choudhary is the Henry & Isabelle Dever Professor of Electrical Engineering and Computer Science and a professor at Kellogg School of Management. He is also the founder, chairman and chief scientist (served as its CEO during 2011-2013) of 4C insights (formerly Voxsup Inc.)., a big data analytics and social media marketing company. He received the National Science Foundation's Young Investigator Award in 1993. He is a fellow of IEEE, ACM and AAAS. His research interests are in high-performance computing, data intensive computing, scalable data mining, computer architecture, high-performance I/O systems, software and their applications in science, medicine and business. Alok Choudhary has published more than 400 papers in various journals and conferences and has graduated 33 PhD students. Techniques developed by his group can be found on every modern processor and scalable software developed by his group can be found on many supercomputers. Alok Choudhary's work and interviews have appeared in many traditional media including New York Times, Chicago Tribune, The Telegraph, ABC, PBS, NPR, AdExchange, Business Daily and many international media outlets all over the world.
Tuesday, August 5th
Keynote: Sampling Theory of Large Networks
Speaker: John C.S. Lui, The Chinese University of Hong Kong, China
Abstract: Often times, large networks can be represented as graphs. For example, the Internet topology can be represented an an undirected graph while large logical networks (e.g., Facebook, Twitter,..etc) can be represented as either directed or undirected graphs. For these graphs, characterizing node pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large scale nature of such networks, it is infeasible to enumerate all user pairs and so sampling is used. In this talk, we show that it is a great challenge even for OSN service providers to characterize user pair relationships even when they posses the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling (UVS) and random walk (RW)) are naively applied, they can introduce large biases, in particular, for estimating similarity distribution of user pairs with constraints such as existence of mutual neighbors, which is important for applications such as identifying network homophily. Estimating statistics of user pairs is more challenging in the absence of the complete topology information, since an unbiased sampling technique such as UVS is usually not allowed, and exploring the OSN graph topology is expensive. To address these challenges, we present asymptotically unbiased sampling methods to characterize user pair properties based on UVS and RW techniques respectively.
Biography: John C.S. Lui was born in Hong Kong and is currently a full professor in the Department of Computer Science & Engineering at The Chinese University of Hong Kong. He received his Ph.D. in Computer Science from UCLA. After his graduation, he joined the IBM Almaden Research Laboratory/San Jose Laboratory and participated in various research and development projects on file systems and parallel I/O architectures. He later joined the Department of Computer Science and Engineering at The Chinese University of Hong Kong. His current research interests are in network sciences with large data implications (e.g., online social networks, large scale data analytics, etc), network/system security (e.g., cloud security, mobile security, ...etc), network economics, large scale distributed systems and performance evaluation theory. John served as the chairman of the CSE Department from 2005-2011. He received various departmental teaching awards and the CUHK Vice-Chancellor's Exemplary Teaching Award. John received the CUHK Faculty of Engineering Research Excellence Award (2011-2012). John is a co-recipient of the IFIP WG 7.3 Performance 2005, IEEE/IFIP NOMS 2006 and SIMPLEX'14 Best Paper Awards. He is an elected member of the IFIP WG 7.3, Fellow of ACM, Fellow of IEEE, Senior Research Fellow of the Croucher Foundation and is currently the chair of the ACM SIGMETRICS. His personal interests include films and general reading
Wednesday, August 6th
Keynote: Mining Online Social Networks: Opportunities and Privacy Issues
Speaker: Keith Ross
Dean of Engineering and Computer Science, NYU Shanghai
Leonard J. Shustek Distinguished Professor, CSE Dept., NYU
Abstract: Online social networks -- such as Facebook, Twitter, Instagram, WeChat, and many others -- contain a treasure trove of data which can be mined not only for targeted advertising but also for directly improving our lives in many ways. In addition to textual data, this treasure trove includes geographical presence data and massive quantities of image data. Although mining social networks provides numerous business and quality-of-life opportunities, it also can potentially lead to deep intrusions into our privacy.
In this talk I'll survey some recent research in mining social networks and privacy. We will examine tracking user locations using Wechat; uncovering users' purchase history using eBay; inferring sensitive information about Facebook users (including children); mining sensitive information from photo collections, such as from Facebook communities; and exploring anonymity in Twitter.
Biography: Keith Ross is the Dean of Engineering and Computer Science at NYU Shanghai and the Leonard J. Shustek Chair Professor in the Computer Science and Engineering Department at NYU. Professor Ross has worked in social networks, security and privacy, peer-to-peer networking, Internet measurement, video streaming, multi-service loss networks, queuing theory, and Markov decision processes. He is an ACM Fellow, IEEE Fellow, recipient of the Infocom 2009 Best Paper Award, and recipient of 2008 and the 2011 Best Paper Awards for Multimedia Communications (awarded by IEEE Communications Society). His work has been featured in the New York Times, NPR, Bloomberg Television, Huffington Post, Fast Company, Ars Technia, and the New Scientist. Professor Ross is also the co-author (with James F. Kurose) of the popular textbook, Computer Networking: A Top-Down Approach Featuring the Internet, published by Pearson. It is the most popular textbook on computer networking, both nationally and internationally, and has been translated into fourteen languages. Professor Ross is also the author of the research monograph, Multiservice Loss Models for Broadband Communication Networks, published by Springer in 1995. Finally, he was the co-founder of Wimba, which develops multimedia technologies for online learning. Wimba was acquired by Blackboard in 2010.