Date : 14-04-10
[Seminar] Kuansan Wang, Xing Xie, Nicholas(Jing) Yuan
Author : Admin
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Speaker :Kuansan Wang, Xing Xie, Nicholas(Jing) Yuan

Date : 2014. 4. 11, (Fri) 15:30~17:30

Location : 우정정보통신관(Woojung Building), 604 Lecture room, Korea University.

03:30~04:10 : Kuansan Wang, Title: From artificial to augmented intelligence: A case study on intelligent dialog
04:10~04:50 : Xing Xie, Title: Building User Knowledge Graph based on Human Behavioral Data
04:50~05:30 : Nicholas(Jing) Yuan, Title: LifeSepc: Exploring the Spectrum of Urban Lifestyles

Kuansan Wang
Title: From artificial to augmented intelligence: A case study on intelligent dialog
Abstract: For more than half a century, a holy grail for artificial intelligence research is to uncover the nature of intelligence so that hopefully one day computers can be programmed to exhibit intelligent behaviors. As the world wide web evolves, we have seen quite a few use cases where the connected computers on the web can outperform humans in many tasks even though, technically, these computers have yet to possess human-like intelligence. This talk will further illustrate the point by describing a multi-year initiative at Microsoft Research, called Bing Dialog, to fundamentally change Microsoft Bing from keyword based search engine to a semantic based one. The project aims to anticipate and match a user’s information needs to the knowledge harvested from the web. Aside from reactively retrieving information and answering questions, Bing Dialog model includes additional dialog acts, such as confirmation, disambiguation, refinement and digression, that the search engine can execute proactively to expedite the process of getting users with the knowledge they need. Essentially, the search engine becomes a collaborative dialog agent, such as those explored in the AI community but with the scale extended to the entire web. Data collected from the field deployments suggest that, with machine surpassing human performance in gathering web knowledge and conducting intelligent dialog to fulfill user information needs, perhaps equipping computers with human capabilities is no longer an ambitious enough goal.

Bio: Kuansan Wang is a Principal Researcher and manager of the Internet Service Research Center (ISRC) at Microsoft Research (MSR), Redmond. He joined MSR Speech Technology Group in 1998, conducting research in the areas of speech recognition, spoken language understanding and multimodal dialog. From 2004 to 2007, he was a software architect at speech product and business incubation groups, helping create and commercialize a wide range of award winning speech products for Microsoft. Since 2007, he has been with MSR ISRC conducting research on web search and machine learning. Dr. Wang is an active member in both academic and industrial communities. He has published more than 50 peered review articles and 140 patents. He is also the author of 6 ISO and 3 W3C standards in the area of speech processing and voice communications.

Xing Xie
Title: Building User Knowledge Graph based on Human Behavioral Data

Abstract: With the rapid development of positioning, sensor and smart device technologies, a massive amount of human behavioral data have been made available. They reflect various aspects of human mobility and activities in the physical world. The availability of these data provides us an unprecedented opportunity to understand users deeply and provide them a personalized online experience while respecting their privacy. In this talk, I will describe our vision towards building a unified user knowledge graph which supplements the knowledge graph with knowledge generated from both public and personal user data, e.g. check-in trajectories, mobile communication histories and environmental sensor readings. I will also present our recent research efforts along this direction, including user linking across multiple networks, user mobility understanding and prediction, psychological trait inference and life pattern analysis.

Bio: Dr. Xing Xie is currently a senior researcher in Microsoft Research Asia, and a guest Ph.D. advisor for the University of Science and Technology of China. He received his B.S. and Ph.D. degrees in Computer Science from the University of Science and Technology of China in 1996 and 2001, respectively. He joined Microsoft Research Asia in July 2001, working on spatial data mining, location based services, social networks and ubiquitous computing. During the past years, he has published over 140 referred journal and conference papers. He currently serves on the editorial boards of ACM Transactions on Intelligent Systems and Technology (TIST), Springer GeoInformatica, Elsevier Pervasive and Mobile Computing, Journal of Location Based Services, and Communications of the China Computer Federation (CCCF). In recent years, he was involved in the program or organizing committees of over 70 conferences and workshops. Especially, he initiated the LBSN workshop series and served as program co-chair of ACM UbiComp 2011 and program chair of the 8th Chinese Pervasive Computing Conference (PCC 2012). In Oct. 2009, he founded the SIGSPATIAL China chapter which was the first regional chapter of ACM SIGSPATIAL. He is a member of Joint Steering Committee of the UbiComp and Pervasive Conference Series. He is a senior member of ACM, the IEEE, and China Computer Federation (CCF).

Nicholas(Jing) Yuan
Title: LifeSepc: Exploring the Spectrum of Urban Lifestyles
Abstract: An incisive understanding of human lifestyles is not only essential to many scientific disciplines, but also has a profound business impact for targeted marketing. In this talk, we present LifeSpec, a computational framework for exploring and hierarchically categorizing urban lifestyles. Specifically, we have developed an algorithm to connect multiple social network accounts of millions of individuals and collect their publicly available heterogeneous behavioral data as well as social links. In addition, a nonparametric Bayesian approach is developed to model the lifestyle spectrum of a group of individuals. To demonstrate the effectiveness of LifeSpec, we conducted extensive experiments and case studies, with a large dataset we collected covering 1.4 million individuals from 493 cities. Our results suggest that LifeSpec offers a powerful paradigm for 1) revealing an individual’s lifestyle from multiple dimensions, and 2) uncovering lifestyle commonalities and variations of a group with various demographic attributes, such as vocation, education, and place of residence. The proposed method provides emerging implications for personalized recommendation and targeted advertising.

Bio: Nicholas (Jing) Yuan is currently an associate researcher in Microsoft Research Asia. He got a Ph.D. degree in Computer Science and a B.S. degree in Mathematics, both from University of Science and Technology of China. From 2009 to 2012, He worked as a full time research intern in MSR Asia. Currently, his research interests include behavioral data mining, spatial-temporal data mining and computational social science. During the past few years, Nicholas has published a series of papers in top-tier conferences and journals, such as ACM SIGKDD, IEEE TKDE, ACM Ubicomp, WWW, ACM COSN and SIGSPATIAL. His work has been featured by influential media such as MIT Technology Review several times. He was honored with Microsoft Fellowship (2011), Best Paper Runner-up Award of ACM SIGSPATIAL (2010), Distinguished Doctoral Dissertation Award by Chinese Academy of Sciences (2013), and Best Paper Award of IEEE International Conference on Data Mining (2013). He is a member of ACM and IEEE.