Keynote Speakers

David G. Belanger, PhD, Senior Research Fellow, Stevens Institute of Technology, New Jersey, USA

Dr. David Belanger is currently a Senior Research Fellow at Stevens Institute of Technology. In this role he continues his work in Big Data Technology, Applications, and Governance. He also teaches and helps lead in the Business Intelligence & Analysis Master Degree program and the Center for Big Data Innovation. In addition, he is involved in consulting related to Big Data in areas such as Health Care, security, and Networking.

He was Chief Scientist of AT&T Labs, and Vice President: Information, Software, & Systems Research at AT&T Labs, and creator of the AT&T InfoLab, an early participant in “Big Data” research and practice.

Dave joined Bell Laboratories in 1979. He has led research in software systems and engineering, information mining, information visualization, and development in very large scale data systems. He built the Software Engineering Research Department which provided software tools and techniques used across AT&T Bell Labs, and via open source, across the world.

Prior to AT&T, Dave was associate professor of Mathematics and Computer Science at University of South Alabama, and co-founder of Gulf Coast Data Systems (a computing services company). He received his B. S. from Union College (NY) in Mathematics, and an M. S. & Ph.D., in Mathematics, from Case Western Reserve University.

In 1998, Dave was awarded the AT&T Science and Technology Medal for contributions in very large scale information mining technology. In 2006, he was named an AT&T Fellow for “lifetime contributions in software, software tools, and information mining”.

Dr. Belanger received the 2009 IEEE Communications Society Industrial Innovator Award.

Topic:Big Data - The Next Phase
Abstract

We have been in the realm of Big Data for more than a decade, and have seen a large number of common and valuable application types. These are increasingly supported by widely available tools, both commercial and open source, so that the advantages of Big Data are starting to become accessible to a much wider audience. At the same time, new sources of data and types of applications are ready to dominate the Big Data arena. Many of these are concerned with data generated by devices, rather than individuals, and require applications that are real time, or even predictive. The toolsets are also evolving to satisfy the needs of these applications.

In this talk, we look at where we are going.
 

Mahmoud Daneshmand, PhD, Professor, Stevens Institute of Technology, New Jersey, USA

Dr. Mahmoud Daneshmand is currently Professor of Business Intelligence & Analytics as well as Computer Science Departments at Stevens Institute of Technology. He is Co-Founder of the new Business Intelligence & Analytics Maters Degree program.  In this role, he teaches graduate courses, conducts research and advises PhD students in areas of Big Data and in particular Sensors and Streaming Data. He is co-founder and chair of Steering Committee of the new IEEE Journal of Internet of Things (IoT), and Guest Editor of the IEEE Sensors Journal SI. He is an expert in Big Data Analytics, IoT/Sensors & RFID Data Streams, Data Mining Algorithms, Machine Learning, Probability & Stochastic Processes, and Statistics.

Mahmoud has served as Distinguished Member of Technical Staff (DMTS) at Bell Labs as well as AT&T Shannon Labs-Research; Assistant Chief Scientist of the AT&T Labs; and Executive Director of the AT&T Labs university collaborations program. He has more than 35 years of teaching, research & publications, consultation, and management experience in academia & industry including: Bell Laboratories, AT&T Shannon Labs-Research, University of California at Berkeley, University of Texas at Austin, Sharif University of Technology, University of Tehran, New York University, and Stevens Institute of Technology.

He has published more than 91 Journal and conference papers; authored/co-authored three books; Holds two patents (2009 and 2010).  He has been Guest Editor of several IEEE journals; keynote speaker of many IEEE conferences; Executive Committee of Globecom as well as ICC; Chair of Steering Committee of IEEE ISCC; and General Chair and Technical Chair of many IEEE conferences.

He has a PhD and MS in Statistics from the University of California, Berkeley, and MS and BS in Mathematics from the University of Tehran.

Topic: The Biggest Challenge of the Big Data: Sensor Data Analytics
Abstract
The Big Data of the future is generated by billions of “things” connected to the Internet. The term Internet of Things (IoT), born 2009, may be simplified as “Sensors/RFID devices + Communications”.

It is estimated that by 2015, 25 billion sensors/RFID devices will be generating data on aspects of the human life: health, environment, transportations, security, shopping, home, etc. The Biggest Challenge of the Big Data is management and mining of ever-increasing streams of data generated by these devices

This talk will focus on management and mining of sensors data. Emphasize will be on data accuracy, data mining techniques, and future research direction of real time large scale stream data mining.

 

Jerry Z. Xie, PhD, Scientist-in-Chief, China Telecommunications Corporation, China

Dr. Jerry Xie is currently horned as a Specialist of the National 1000 Plan by Chinese Government. He joined China Telecommunications Corporation in September 2011.He led and founded China Telecom Cloud Computing Company and served as the founding CEO. He is currently Scientist-in-Chief of China Telecommunications Corporation.

Prior to China Telecomm, he worked in the United States and held senior engineering and management positions with Intel, Sony Electronics, and Sun Microsystems. When served as General Manager of IT with Sony Electronics, he was instrumental to build RosettaNet based partner interface processes and the agile supply chain management systems to support Sony’s new VAIO business. When served as Senior Staff Engineering Manager at Intel, he led team delivered the first operational InfiniBand cluster; he was one of the initiators to establish the standard work groups at DMTF in Web service management WS-MAN and WS-CIM, and the contributor to the standard specifications. These works were implemented in Intel’s AMT products and led to the elastic cloud computing via Web service interfaces. He holds a Ph.D. from Virginia Tech.

Topic: Big Data: The Commercial Grade Hadoop
Abstract
As characterized by the definition of big data, the high volume, high velocity, and high variety information assets require new techniques and architecture to capture, curate, manage, and process the data within a tolerable elapsed time to enable enhanced decision making, insight discovery and process optimization. The challenges of the open source Hadoop technology faces are multidimensional. In this talk, we focus on Intel’s Hadoop platform and the roadmap to improvement; we also present a potential use case in relation to our own business as a telecommunication carrier.

 

Dr. Baohong He

Director of the Internet Center, China Academy of Telecommunication Research of MIIT, Member of the Ministry of Science and Technology Committee, Secretary-General of China Cloud Computing Promotion and Policy Forum, Vice Chairman of IP & Multimedia Communication Working Committee of China Communications Standards Association (CCSA), Deputy Secretary General of China Next Generation Internet (CNGI) Expert Committee, Chairman of TGG China Committee.

Dr. He has long been engaged in the research of Internet-related technologies, standards, regulatory, policy and development strategy. Graduated from the Chinese Academy of Sciences in 1999 and obtained aPh.D. of Computer Application Technology.

Topic: (To be posted)

 

Anup Kumar, PhD, Professor, University of Louisville, Kentucky, USA

Anup Kumar (ak@louisville.edu) completed his Ph.D. from North Carolina State University and is currently a Professor of CECS Department at the University of Louisville. He is also the Director of Mobile Information Network and Distributed Systems (MINDS) Lab. His research interests include web services, wireless networks, distributed system modelling, and simulation. He has co-edited a book titled, “Handbook of Mobile Systems: Applications ands Services” published by CRC press in 2012. He is an Associate Editor of IEEE Transactions on Services Computing. He is also the Associate Editor of Internal Journal of Web Services Research and International Society of Computers and Their Application Journal. He is a member of IEEE Distinguished Visitor Program (2006-2008). He was the Chair of IEEE Computer Society Technical committee on Simulation (TCSIM) (2004-2007). He has published and presented over 150 papers. Some of his papers have appeared in ACM Multimedia Systems Journal, several IEEE Transactions, Wireless Communication and Mobile Computing, Journal of Parallel and Distributed Computing, IEEE Journal on Selected Areas in Communications etc. He was the Associate Editor of International Journal of Engineering Design and Automation 1995-1998. He has served on many conference program and organizing committees such as IEEE ISCC 2007, IEEE ICSW-2006, IEEE MASS-2005, IEEE SCC-2005, IEEE ICWS-2005, CIT-2005, IEEE MASCOTS, ADCOM 97 and 98. He has also edited special issues in IEEE Internet Magazine, and International Journal on Computers and Operations Research. He is a Senior Member of IEEE.

Topic: Big Data Analytic and Healthcare
Abstract
The electronic medical record (EMR) initiative has generated mountain of data from all types of patients at the hospital, insurance companies and doctor’s office. The next big question is can we use this data to improve our healthcare system both in terms of patient care and cost as well. This talk will provide a framework of how Big Data Analytics can arm healthcare players and providers with practical knowledge to make better business decisions, streamline operational and assessment processes. The discussion will also include practical application and appropriate tools and technology to leverage Big Data.

 

Bin Xie, PhD, Founder & President, InfoBeyond Technology LLC, USA

Dr. Bin Xie received his M.Sc and Ph.D. degrees in Computer Science and Computer Engineering from the University of Louisville, Kentucky, USA, 2003 and 2006 respectively. His book tiltled Handbook/Encyclopedia of Ad Hoc and Ubiquitous Computing (World Scientific: ISBN-10: 981283348X) is one of the bestselling handbooks at World Scientific Publisher. Dr. Xie is also the author of books:  Handbook of Applications and Services for Mobile Systems (Auerbach Publication, Taylor and Francis Group, ISBN:  9781439801529, 2012) and Heterogeneous Wireless Networks- Networking Protocol to Security (VDM Publishing House: ISBN: 3836419270, 2007). He has published 70+ papers in the IEEE conferences, magazines, official governmental reports, and journals. Some of papers have appeared in the most-cited journals in the areas of Telecommunication and Computer Architecture. His research interests are focused on cyber security, network management, wireless communication, and user performance. In particular, he has performed substantial research works on the fundamental aspects of network deployment, intrusion detection, performance evaluation, and Internet/wireless infrastructure security. He also studied many issues of coding theory, information process, Quality of Service (QoS), clustering, and image reconstruction. 

Dr. Xie is the founder of InfoBeyond Technology LLC and current is the president of the InfoBeyond. InfoBeyond has delivered advanced solutions for wireless mobile ad hoc communications, network security, information process, and data protection in support of unique needs of our customers, maintaining a stable increase of business in the past few years. Dr. Xie is an IEEE senior member. He is an editor member of the Journal of International Journal of Information Technology, Communications and Convergence (IJITCC). He is the Guest edit chair of several journal special issues. Dr. Xie was invited as the program chair or TPC member of a number of international conferences.

Topic: Learning Social Kernel Communities and Behaviors from Big Social Media
Abstract
In the past decade, we have seen the increasingly flourishing of social median such as Twitter, Facebook, Google+, and MySpace. The social networks have all grown up to the top sites globally in terms of the global network traffic. The big data of social media imposes many challenges for us to (i) track the communities, (ii) learn the social interactions of interests, and (iii) predict the social behaviors if necessary. Current approaches are very limited to provide reliable and valid understanding of the meanings that are otherwise never discovered from the data sets we collected. In this talk, we will further explore the technical challenges, review the existing approaches, and particularly present an insight on the semantic understanding of the social media such that the time and geographic properties of the social interactions are precisely revealed.

 

Chung-Min Chen, PhD , Chief Scientist, Applied Communication Sciences, USA

During his career, Chung-Min has been passionate about finding efficient ways to process and make sense of large scale data.  He has spent most of his professional time on developing innovative algorithms and building scalable databases and analytics systems to support telecom operations and other applications.  His research interest spans across areas in database systems, stream processing, machine learning, parallel & distributed computing, mobile data, and mobile ad hoc network.   Currently, he is interested in researching, developing, and applying big data and analytics technologies to problems in cyber security, telecom OSS, and telematics.

Chung-Min has published over 50 research papers in journals and conferences including The Journal of the ACM, IEEE Trans. on Knowledge and Data Engineering, ACM Transactions on Sensor Networks, IEEE Trans. on Mobile Computing, IEEE/ACM Trans. on Networking, IEEE J. SAC, INFOCOM, ACM SIGMOD, KDD, EDBT, ICDE, and ICDCS. He received the Newcomer Paper Award of ACM PODS 2002.

Chung-Min joined Applied Research of Bellcore/Telcordia in 1998. During 2006-2011, he headed Telcordia’s applied research center in Taiwan, overseeing its activities in R&D, professional consulting, and customer engagement. Chung-Min was an Adjunct Professor at National Taiwan University (2008-2011) and an invited speaker at the 2001 Data Mining Summer School at Rutgers University. He also served as the Chair of US ANSI expert group to Working Group 17 (Mobile Devices for ITS Applications) of ISO TC-204 (Intelligent Transportation System). He received PhD in Computer Science from University of Maryland at College Park and B.S. in Computer Science from National Taiwan University.

Topic: Big Data Scientist – How Big is it?
Abstract
As the volume and excitement of big data grow, so does the demand of people who can sift through the mountainous data, crunch the numbers, and find the golden needle in the haystack that provides valuable insight to the business. Big data scientist, a title crowned to people with such skills, is one of the most sought-after in the industry. Yet, there is no consensus on what traits make a good data scientist--computer scientist, statistician, data analyst, just to name a few.  This talk will examine the difference among the related fields, analyze their relevance to different kinds of big data applications, and hopefully lead to guidance on what qualifies a good data scientist.

 
Summit Moderator: David Lu, AVP, IT, AT&T, USA

David Lu is the Assistant Vice president, Enterprise IT, Mobility Network Automation Planning, Network Performance/Testing and Mobility Infrastructure Platforms Engineering and Delivery at AT&T. He is an international recognized expert in network assurance engineering and automation, software architecture, IT development and delivery, network performance and traffic management, large data DB implementation/mining/analytics, software reliability and quality, and network operations process engineering.

David served in lead positions managing network management systems, NM standards, software technology, and network operations process in the past 24 years. Since joining AT&T Bell Labs in 1987, David has had various assignments in software development, platform architecture, systems engineering, business planning, network operations process engineering, and program management. He led and contributed to major AT&T network operation systems evolution, OSS development initiatives, software architecture modernization projects, operation process re-engineering and automation initiatives, customer care systems re-engineering projects, and major AT&T global network evolution initiatives including MPLS IP, VoIP, Ethernet, IPTV, UMTS/LTE networks. David has been instrumental in defining AT&T Network OSS target architecture and implementation that delivered leading edge systems automation with predictive/adaptive capabilities and operations savings in billions of dollars in past 10 years.  He led the advanced network management training and consulting projects for AT&T global clients; served as the member of AT&T Software Platform Intellectual Property Review Board and AT&T Software Excellence Award Selection Committee, was inventor/co-inventor of a number of US patents. He has frequently appeared as a guest speaker at technical and leadership seminars and conferences.

Currently, David leads an organization of over 800 people and is responsible for IT Solution Architecture and Planning of network assurance programs and the implementation of target network management systems across AT&T including IPTV and wireless networks. In addition, he is also responsible for engineering and development of network performance management, network diagnose/testing, and cell site management systems across AT&T.
 

Summit Organizer: Chi-Ming Chen PhD, AT&T Labs, USA

Chi-Ming Chen joined AT&T in 1995. His current responsibility is the operations support system (OSS) architecture. Prior to joining AT&T, Chi-Ming was with Bell Communications Research (Bellcore) from 1985 to 1995. He was a faculty member at TsingHua University, Hsinchu, Taiwan from 1975 to 1979.

He received his Ph.D. in Computer and Information Science from the University of Pennsylvania in 1985; M.S. in Computer Science from the Pennsylvania State University in 1981; M.S. and B.S. in Physics from TsingHua University, Taiwan, in 1973 and 1971 respectively.

Chi-Ming Chen is a senior member of IEEE and ACM. He is an Advisory Board Member of IEEE Communications Society (ComSoc) Technical Committee on Communications Quality & Reliability (CQR) and a member of the IEEE GLOBECOM ICC Management & Strategy (GIMS) Standing Committee. He has chaired several GLOBECOM and ICC Industry Forums.