Ching-Yung Lin, Ph.D.

PI, Graph Computing and Social Cognitive Analytics Manager, Network Science, IBM T. J. Watson Research Center

1101 Kitchawan Rd., Yorktown Heights, NY 10598

Adjunct Professor, Columbia U. & NYU.; Fellow, IEEE

Phone: (914) 945-1897; Fax: (914) 945-2141

Graph Computing for Connected Big Data

Abstract:

Cognitive machines are emerging to reason and manage rapidly expanding world of Big Data. Many real-world data are linked. Entities are dependent. Processing, storing, analyzing, retrieving, and visualizing connected data has been a major challenge . Traditional technologies are not equipped to handle these non-uniform, semi-structured, and highly interconnected data. Novel graph computing technologies are being invented and are driving potential paradigm shift.

Graphs may be large or small, static or dynamic, topological or semantic, and property-oriented or Bayesian. Graphical models have been also showing importance in sequential event understanding and concept reasoning. Semantic concepts and knowledge as in text, image, video, and audio can be well-represented as graphs. I am going to discuss Graph Database, High Performance Computing, Middleware, Analytics Library, and Visualization, as well example applications on (1) Cognitive Analytics, which utilizes graphical models to understand and predict people's behavior for Security or Commerce, (2) Social Analytics, which analyzes collective behaviors of people in social media, and (3) Brain Analytics, which models neuron's dynamic networks to understand inner function and correlation.

Bio:

Ching-Yung Lin is the Manager of the Network Science Department in IBM T. J. Watson Research Center. He is also an Adjunct Professor in Columbia University since 2005 and in NYU since 2014. His research interest is mainly on fundamental research of multimodality signal understanding, network analytics, and computational social & cognitive sciences, and applied research on security, commerce, and collaboration. Since 2011, Lin has been leading a team of more than 40 Ph.D. researchers in worldwide IBM Research Labs and more than 20 professors and researchers in 10 universities and institutes (Northeastern, Northwestern, Columbia, Minnesota, Rutgers, CMU, New Mexico, USC, UC Berkeley, and SRI).

His team focuses on all aspects of large-scale Graph Computing -- graph database, high performance computing graph infrastructure, network graph analysis and graphical models library, and graph visualization. The goal is to create innovative foundation to solve the biggest challenge of Big Data when data are dependent. It is applied to (1) Social/Economic Networks (2) Information/Knowledge Networks (3) Natural/Bio/Cognition/Brain Networks and (4) Communication/Mobile Networks. On Social Cognitive Analytics, the team's focus is on machine-based people understanding for Cognitive Security, Social Analytics, Behavioral Analytics, Neuron Network Analytics, and Audio-Visual Sensing Analytics.

His research has been featured on more than 120 press articles including 4 times in BusinessWeek, as the Top Story of the Week in April 2009. His invention, SmallBlue, helped IBM Corporation won the 1st place in 2012 Most Admirable Knowledge Enterprise (MAKE) Award in enterprise-wide collaboration knowledge-sharing environment. In May 2013, SmallBlue was selected by APQC, the World Leader in Knowledge Sharing Benchmarking and Practices, as the Industry Leader and Best Practice in Expertise Location. In October 2013, SmallBlue was recognized as having made $100M+ productivity contribution to IBM.

Lin is an author of 160+ publications and 19 issued patents. His team recently won the Best Paper Award in BigData 2013, Best Paper Award in CIKM 2012, and Best Theme Paper Award in ICIS 2011. He is a Fellow of IEEE, a Director of Asia-Pacific Signal and Information Processing Association, and a member of Academy of Management.