Keynote Speakers for 2017 IEEE Joint Conferences (IEEE SOSE/MobileCloud/BigDataService)

Bin Yu

Professor, Departments of Statistics and EECS
UC Berkeley

Bin Yu is Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley. Her current research interests focus on statistics and machine learning theory, methodologies, and algorithms for solving high-dimensional data problems. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and remote sensing. She obtained her B.S. degree in Mathematics from Peking University in 1984, her M.A. and Ph.D. degrees in Statistics from the University of California at Berkeley in 1987 and 1990, respectively. She held faculty positions at the University of Wisconsin-Madison and Yale University and was a Member of Technical Staff at Bell Labs, Lucent. She was Chair of Department of Statistics at UC Berkeley from 2009 to 2012, and is a founding co-director of the Microsoft Lab on Statistics and Information Technology at Peking University, China, and Chair of the Scientific Advisory Committee of the Statistical Science Center at Peking University. She is Member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an Invited Speaker at ICIAM in 2011, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She is a Fellow of IMS, ASA, AAAS and IEEE. She served on the Board of Mathematics Sciences and Applications (BMSA) of NAS and as co-chair of SAMSI advisory committee, and on the Board of Trustees at ICERM and Scientific Advisory Board of IPAM. She has served or is serving on many editorial boards, including Journal of Machine Learning Research (JMLR), Annals of Statistics and American Statistical Association (JASA).

Title: Mobile Cloud and Data, One Telekom Perspective

Abstract:

In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. The ultimate importance of prediction lies in the fact that future holds the unique and possibly the only purpose of all human activities, in business, education, research, and government alike. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results. It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. The three principles will be demonstrated in the context of neuroscience and genomics projects, for which "data wisdom" is also indispensable.


Ling Liu

Professor
School of Computer Science at Georgia Institute of Technology.
Distributed Data Intensive Systems Lab

Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale data intensive systems, including performance, availability, security and privacy. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award in 2012. She has published over 300 international journal and conference articles and is a recipient of the best paper awards from a number of top venues, including ICDCS 2003, WWW 2004, 2005 Pat Goldberg Memorial Best Paper Award, IEEE Cloud 2012, IEEE ICWS 2013, ACM/IEEE CCGrid 2015. In addition to serving as general chair and PC chairs of numerous IEEE and ACM conferences in the fields of big data, cloud computing, data engineering, distributed computing, very large databases, Prof. Liu has served on editorial board of over a dozen international journals, including the editor in chief of the IEEE Transactions on Services Computing (2013-2016). Currently Prof. Liu is serving as the PC Chair of IEEE 2017 International Conference on Distributed Computing (ICDCS2017) to be held in Atlanta, GA in June 5-8, 2017. Prof. Liu's current research is primarily sponsored by NSF, IBM and Intel.


Title: Internet of Things and Services Computing: A Marriage Made in Big Data

Abstract:

The term "Internet of Things" (IoT) embraces all kinds of gadgets and devices that can communicate with one another via a connected network. The Internet of "Smart Things" embodies ambient intelligence in gadgets and devices to make things brainy and clever, such as smart phones, smart appliances, smart vehicles and smart cities. Services computing can be seen as a utility-based infrastructure in which computing and communication are delivered. The marriage of Internet of Things and Services computing will enrich our work and life style by turning the dream of smart houses, smart offices, smart cities, and smart planet into reality. However, this marriage will only be made feasible in big data services. Although we have witnessed the success of delivering numerous hardware infrastructures, computing platforms and software applications as outsourced and managed services, big data and data analytics have not yet been packaged and outsourced as large-scale "dialtone" services. In this keynote, I will explore the research opportunities and challenges from multiple dimensions towards making the marriage of Internet of Things and Services Computing in the heaven of Big Data.


Roch H. Glitho

Associate Professor and Canada Research Chair
Concordia University, Montreal, Canada

Roch H. Glitho, PhD, holds a Ph.D. in tele-informatics (Royal Institute of Technology, Stockholm, Sweden), and M.Sc. degrees in business economics (University of Grenoble, France), pure mathematics (University Geneva, Switzerland), and computer science (University of Geneva). He is an associate professor of networking and telecommunications at Concordia University, Montreal, Canada where he holds a Canada Research Chair in End-User Service Engineering for Communication Networks. In the past he has worked in industry for almost a quarter of a century and has held several senior technical positions at LM Ericsson in Sweden and Canada (e.g. expert, principal engineer, senior specialist). In the past he has served as IEEE Communications Society distinguished lecturer, Editor-In-Chief of IEEE Communications Magazine and Editor-In-Chief of IEEE Communications Surveys & Tutorials.

Title: Augmenting Clouds with NFV and Fogs for Cost Efficient, Agile and QoE - Enabled IoT Applications and Services Provisioning

Abstract:

Distributed Data Intensive Systems Lab, School of Computer Science, Georgia Institute of TechnologyThe Internet of Things (IoT) exploits the ubiquity of objects such as sensors and actuators which could be networked and collaborate for meeting specific goals. The expected applications are numerous and cover all aspects of business and everyday life. However provisioning these applications in a cost efficient, agile and Quality of Experience (QoE)-enabled manner remains an uphill task. Provisioning for instance the IoT infrastructure (e.g. sensors, actuators) remains applications/services specific, precluding cost efficiency through re-use by new applications. Virtualizing this infrastructure and offering it as a cloud IoT Infrastructure as a Service (IoT-IaaS) will certainly lead to cost efficiency. When it comes to the provisioning of middle boxes services (e.g. IoT gateways) on which the applications/services rely, inflexibility is generally the rule. Softwarizing these middle boxes and deploying them as VNFs is likely to bring a much higher level of flexibility. The distance between the virtualized IoT - IaaS and the end-users and/or IoT devices might also adversely affect QoE. Fogs are the ideal approach to tackle this QoE issue since they are "closer to the ground" (compared to clouds). Cloud computing, NFV, and fog computing are indeed poised to change the current state of affairs in IoT applications provisioning by enabling cost efficiency, agility and QoE, respectively. This keynote speech discusses the state of the art and sketches the research directions.


Peter Ruppel

Senior Researcher
Telekom Innovation Laboratories (T-Labs) and Technische University at Berlin, Germany

Peter Ruppel is Senior Researcher at the Service-centric Networking group of Telekom Innovation Laboratories (T-Labs) and Technische University at Berlin, Germany. He holds a diploma and a doctoral degree in computer science from Ludwig-Maximilians-University Munich, Germany, and he is a co-founder of Bitplaces, a company that provides technologies for geofencing and location-based services for millions of users in Europe. Over the past ten years he has conducted various research projects in the domain of mobile computing, location-based services, and location data analytics.

Title : Mobile Cloud Computing and Operations

Abstract:

In this talk I would like to discuss the implications of mobile cloud computing for the advancement of cloud-related protocols, frameworks, and architectures as well as underlying networks by using the example of cellular network infrastructures and location data. The increasing amount and diversity of mobile-originated data as well as the globalization of mobile services is calling for progression in the way mobile cloud computing systems are built and interconnected. Besides overall architectural and legal challenges there are several engineering issues to be addressed, for example, the operation of mobile clouds, the distributed analysis and decision making based on mobile-originated data, and the interconnection between heterogeneous mobile infrastructures. This talk gives a summary of recent developments in the field of mobile cloud computing and operations and outlines open research questions.


Ashok N. Srivastava

VP, Data and Artificial Intelligence Systems
Chief Data Scientist, Verizon

Ashok N. Srivastava, Ph.D. is the VP of Data and Artificial Intelligence Systems and the Chief Data Scientist at Verizon. His global team focuses on building new revenue-generating products and services powered by big data and artificial intelligence. He is a Consulting Professor at Stanford in the Electrical Engineering Department and is the Editor-in-Chief of the AIAA Journal of Aerospace Information Systems. Ashok is a Fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA).

Title : Evolution of Data Science

Abstract:

This talk will provide an overview of the technical and business aspects of building a big data platform with numerous vertical applications. We highlight the need for algorithms and data pipelines that can scale to massive data volumes and velocities. In addition we discuss the key approaches to building business cases around new products and considerations regarding the marketing and sales of data products.


Kwei-Jay Lin

Professor
University of California, Irvine

Kwei-Jay Lin is a Professor at the University of California, Irvine. He was an Associate Professor at the University of Illinois, Urbana-Champaign 1985-1993. He is an Adjunct Professor at the National Taiwan University and National Tsinghua University, Taiwan; Zhejiang University, China; and Nagoya Institute of Technology, Japan. He is the Chief Scientist of the NTU IoX Research Center at the National Taiwan University. He was a Visiting Research Fellow at the Academia Sinica, Taiwan in Spring 2016.
Prof. Lin is an IEEE Fellow, and Editor-In- Chief of the Springer Journal on Service-Oriented Computing and Applications (SOCA). He was the Co-Chair of the IEEE Technical Committee on Business Informatics and Systems (TCBIS) until 2012. He was an Associate Editor of IEEE Transactions on Parallel and Distributed Systems, 2002-2006, and an Associate Editor of IEEE Transactions on Computers, 1996-2000. He served as the External Examiner for the Hong Kong University's Master Program on E-Commerce and Internet Computing during 2006-2009.
Prof. Lin has published more than 200 papers in journals and conferences. He has served on many international conferences, recently as conference co-chairs of IEEE SOCA 2016 and CBI 2015. He has given keynote speeches in the 22nd IEEE Conference on Embedded and Real-Time Computing Systems and Applications in August 2016, and the 6th IEEE Symposium on Cloud and Service Computing, in December 2016. His research interest includes service-oriented systems, intelligent IoT applications, middleware and kernel, real-time computing, and distributed computing.

Title : Building Smart Homes and Buildings using Service-Oriented Things

Abstract:

With the advance of sensing, actuation and wearable technology, Internet of Things (IoT) has become the next IT research focus. Many IT companies have started to invest heavily into this area by developing and inventing innovative IoT products. However, due to the diversity of embedded devices, application domains, and networking requirements, building IoT applications requires a stiff learning curve for most teams due to a significant customization effort on sensors, embedded software, cyber-physical integration, and cloud service support.
We have developed a flexible IoT platform and middleware, called WuKong, to ease the development of new IoT applications. The WuKong middleware supports the flow-based programming (FBP) paradigm for users to easily define data and control workflows among virtual, conceptual sensing classes. Users can also specify simple execution policies on when, where, and how an FBP should run in a user's environment. The WuKong middleware can intelligently decide how to deploy an IoT application by mapping an FBP to physical devices in order to meet the functionality requirements. We have recently built smart building applications using "evacuation sensors" that can guide people to find safe exit paths when fires are detected in a building. Algorithms and techniques can be integrated for real time detection of fire events and situations. Combined with data analytics, evacuation sensing can make predictions on dangerous areas for people to avoid. In this talk, the issues, techniques and challenges for evacuation sensing will be discussed.