Ching-Yung Lin  Ching-Yung Lin photo       

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IBM Chief Scientist, Graph Computing & IBM Distinguished Researcher
Watson Research Center, Yorktown Heights, NY 10598, USA



     Dr. Ching-Yung Lin is the IBM Chief Scientist, Graph Computing, and an IBM Distinguished Researcher. He is the founder and senior manager of the Network Science and Machine Intelligence Department in IBM T. J. Watson Research Center. He has been with IBM Research since 2000, after receiving his Ph.D. from Columbia University. Dr. Lin is also an Adjunct Associate/Full Professor in the Departments of Electrical Engineering and Computer Science, Columbia University since 2005. He was an Affiliate Assistant/Associate Professor in the University of Washington from 2003 to 2009, and an Adjunct Professor in New York University (NYU) in 2014.

     His research interests are on fundamental research of machine learning, artificial intelligence, data mining, multimodality signal understanding, network science, brain analysis, and applied research on security, commerce, and collaboration. Lin was elevated to IEEE Fellow for "contributions to network science and multimedia security and retrieval" in Nov 2011. He was the first IEEE fellow cited for contribution to Network Science. Dr. Lin founded and leads the Linked Big Data direction in IBM Research. Since 2011, he has been leading a team of more than 40 Ph.D. researchers in worldwide IBM Research Labs (Watson, Almaden, Cambridge, Austin, India, China, Brazil, and Australia) and more than 20 professors and researchers in 10 universities and institutes (Northeastern, Northwestern, Columbia, Minnesota, Rutgers, CMU, New Mexico, USC, UC Berkeley, and Stanford Research Institute).

     He is leading a big R&D project of “IBM System G,” which is dedicated to advance science, technology, and industry solutions based on cognitive networks and graph computing. He has been the Principal Investigator of many external funded (~$25M) projects: DARPA Anomaly Detection at Multiple Scales (ADAMS), DARPA Social Media in Strategic Communications (SMISC), ARL Social and Cognitive Network Academic Research Center (SCNARC), DHS Mobile Security, and several other projects with major global companies and worldwide governments, across the industries of Governments & Public Sector, Financial Services Sector, Aerospace Sector, Telecommunication Sector, Healthcare Sector, and Energy Sector. He is also leading IBM worldwide research on cognitive solutions for investment & commercial banks and insurance, especially in the areas of Fraud, Surveillance, Risk, Compliance, Anti-Money Laundering, Espionage, Sabotage, etc, and led to create the first cognitive security product in the financial industry — IBM Surveillance Insight for Financial Services.

     His team focuses on all aspects of large-scale Graph Computing -- graph database, high performance and distributed computing infrastructure, graph analysis library, and graph visualization. The goal is to create innovative foundation to solve the biggest challenge of Big Data when data are dependent. These tools include different types of graph databases, which outperform traditional relational databases in many modern applications. Graph Middleware considers different ways of optimization on platforms and Software Defined Environment. Graph Analytics include topological analysis, graph matching, graph search, graph path & flow tools. Graph visualization provides foundations for static, large-scale, and dynamic visualization for data exploration, visual analytics, and navigation. His team has been providing software that tops the Supercomputer society’s Graph500 benchmark which achieved 38 billion graph traversal per second. Based on the graph computing foundation, on Machine Intelligence, his team has been making fundamental research on (1) Cognitive Networks, including large-scale machine reasoning Markovian & Bayesian networks, Deep Machine Learning tools, and Brain Network Analysis Tools; (2) Cognitive Analytics, including visual semantic & sentiment analysis and text emotion analysis; (3) Spatiotemporal Analytics, including moving objects indexing, retrieval, and optimization; and (4) Behavioral and Mobile Analytics, including anomaly detection (fraud, espionage, and sabotage), and various recommenders. These tools have been applied to 7 Industry Solutions: Enterprise Social Analytics Solution, Insider Threat Solution, Social Media Solution, Entertainment & Media Solution, Healthcare Solution, Home Care Solution, and Financial Services Solution. There are also more than 22 use cases on 5 Big Data categories: Data Exploration, 360 View, Security, Operation Analysis, and Data Warehouse. His team is consisted of researchers with backgrounds of Signal Processing, Network Science, Machine Learning, Information Retrieval, Natural Language Processing, High Performance Computing, Visualization, Economics, Database, etc.

     He invented and created the SmallBlue system, an IBM production system for Enterprise Social Network Analysis, Expertise Search, and Knowledge Recommendation since 2008. 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 $117M+ productivity contribution to IBM.

     Dr. Lin’s innovative Cognitive Security system focuses on anomaly detection of behaviors and had the best performance in the program reviews. It has been used for insider threat detection such as espionage, sabotage, or fraud detection. Another system -- SMISC focuses on social media monitoring, forensics, and predictive analysis. Lin leads 9 universities on this direction, which alone includes 26 research projects on various aspects of social media analysis. Lin demonstrated both systems in Pentagon in May 2014 and attracted more than 200 audiences.

     Ching-Yung is an author of 160+ publications and 23 awarded patents (Google Scholar: 8,000+ citations, hindex: 42, first-author paper citations: 3,500+). In 2010, IBM Exploratory Research Career Review selected Dr. Lin as one of the five researchers "mostly likely to have the greatest scientific impact for IBM and the world.

     He is teaching “Big Data Analytics” and “Advanced Big Data Analytics” graduate courses in Columbia University in the Fall and Spring semesters. In the course evaluations of the last 4 years, in total, 53% of the students rated Prof. Lin's overall teaching as 'excellent' and 28% rated 'very good'. In a 1-5 scale, the mean was 4.25 and the median was 5 (excellent). His classes attract more than 300 students per year, and the “Big Data Analytics” is the largest graduate class in the departments of Electrical Engineering and Computer Science. Since December 2014, Ching-Yung’s course webpage has been ranked within the top 20, out of 66 million webpages on Google search of Big Data Analytics and has been the highest (or near the highest) ranked big data lectures among worldwide academic institutions in Google search.

     He previously taught “Multimedia Security” (2005-2007) and “Network Science” (2010-2013) courses in Columbia. He taught “Complex Social and Cognitive Analytics (2014)” in NYU. During his 6-year tenure as an affiliate professor in the University of Washington, he co-advised two Ph.D. students on recommendations and sensor mining. (They are now working in Google and Intel). He also co-advised a Ph.D. student in National Taiwan University, working on encrypted-domain data mining and knowledge graph for search expansion, and co-advised a Ph.D. student in MIT Sloan Management School on workplace social impact on productivity (now assistant professor in U Penn Wharton Business School).

     Lin was one of the earliest machine learning researchers on large-scale visual understanding & reasoning. In 2003, Lin created and led 111 researchers in 23 worldwide research institutes for the first large-scale collaborative video semantic annotation project. In 2005, he pioneered the design of a semantic filtering framework which detects more ~150 visual concepts in videos. His multimedia semantic mining project team performed best in the annual US National Institute of Standards and Technology (NIST) semantic video concept detection benchmarking 2002-2004. He also pioneered the design of video/image content authentication systems and a watermarking system surviving print-and-scan process.

     He has been serving as panelist, technical committee member, and invited speaker at various IEEE/ACM/SPIE conferences, National Science Foundation (in U.S. and Hong Kong), and U.S. government. He is the Chair of IEEE CAS society Multimedia Systems and Applications Technical Committee 2010-2011 and the General Chair of IEEE Intl. Conf. on Multimedia & Expo (ICME) 2009 and IEEE Intl. Conf. on Semantic Computing 2015. He is also the founding steering committee chair of ACM SIG Health Informatics IHI 2010-2012. He is a keynote/plenary speaker at Web 2.0 Expo 2009 and 14 other conferences/workshops, the Editor of the Interactive Magazines (EIM) of the IEEE Communications Society 2004-2006, an associate editor of the IEEE Trans. on Multimedia 2004-2007, and an editorial board member of Journal of Visual Communication and Image Representation 2005-2009. He is a guest editor of (1) the Proceedings of the IEEE Special Issue on Multimedia Security, June 2004, (2) EURASIP Journal of Applied Signal Processing Special Issue on Visual Sensor Networks, September 2006, (3) IEEE Trans. on Multimedia, Special Issue on Communities and Media Computing, April 2009, (4) IEEE Journal on Selected Areas in Communications (JSAC) Special Issue on Network Science, June 2013, and (5) Journal of Multimedia Special Issue on Social Multimedia Computing, Jan 2014. He was a Technical Program co-chair of IEEE ITRE 2003. He represented IEEE CAS society in the Steering Committee of IEEE Trans. on Multimedia (2010-2011) and represents CAS in the founding Steering Committee of IEEE Trans. on Network Science and Engineering (2013-). He is an Associate Editor of IEEE Trans. on Big Data and IEEE Trans. on Signal and Information Processing in Networks. He was invited by the American Medical Association to be a panelist on Big Data, together with the United States Chief Data Scientist of White House and the Co-Founer of HealthNEXT in Nov. 2015, hosted by the AMA President.

     Lin is a recipient of 2003 IEEE Circuits and Systems Society Outstanding Young Author Award, IBM Invention Achievement Awards in 2001, 2003, 2007, 2010 and 2011 & 2013, IBM Research Division Award 2005 & 2013, IBM Corporate Outstanding Innovation Award 2011, 2013 & 2014, Association of Information Systems (AIS) Intl. Conf. on Information Systems (ICIS) 2011 Best Theme Paper Award, Acer Best EECS Thesis Award 1993, and the Outstanding Paper Award in CVGIP 1993. His work was featured 4 times by the BusinessWeek magazine, including being the Top Story of the Week on April 9th, 2009. His team won the Best Paper Awards in ACM Intl. Conf. on Knowledge and Information Management (CIKM) 2012 and IEEE International Congress on Big Data (BigData) 2013. His extended team’s papers were selected as the cover paper of Proc. of National Academy of Science (Jan 2013), and were on Science and Nature (twice).

     Dr. Lin is a Fellow of IEEE, and IEEE Distinguished Lecturer, a Director of Industrial Governance Board of APSIPA (Asia-Pacific Signal and Information Processing Association), and a Member of the Academy of Management.


A full CV is here