BDIOT 2018

2018 2nd International Conference on Big Data and Internet of Things

BDIOT2018 Invited Speakers | 大会主讲人

Dr. Yong Woon Park

Agency for Defense Development(ADD), Korea

Biography: Yong Woon Park is currently a AI research director at Agency for Defense Development(ADD) in Korea. He has served as director of IDAR(Institute of Defense Advanced Research) which is very similar to DARPA in USA during 2016-2018, and director of Autonomous Technology Directorate during 2011-2016. He also served the group and team leader of autonomous technology during 2005-2011. He has been a leader position on unmanned ground vehicle(UGV) and robotic technology for military and field applications. In addition, he has been involved in command and control system of many robotics and vehicle systems projects since 1995. Dr. Park received BS and MS degree in mechanical engineering from Pusan National University and Yonsei University, Korea, in 1981 and 1987, respectively, and his Ph.D degree in mechanical engineering from University of Utah, USA. His Park’s major researches covers from autonomous vehicle to intelligent drone applications. His interests include deep and reinforcement learning for autonomous vehicle mostly applicable to field robotics. Dr. Park published more than 170 paper with over 40 journal and 110 refereed conferences. His major achievement is developing unmanned autonomous vehicle first in Korea and he also leaded Korean military robotic program since 2004. Currently he is leading advanced perception of terrain for UGV to guarantee more stability and safety on the rough terrain and adverse situation of pavement. He awarded 6 best papers and two top 100 research award in 2011 and 2014 in Korea. He is also awarded President Prize in 2007 for successful developing and demonstrating several UGV in Korea. He is also recipient of many awards including 100 best and leading science and technology in Korea in 2012, top 10 mechanical achievement in Korea in 2014. He is leading member of Korean Field Robotics Society. He is also chair of research group on Autonomy and its Standard for unmanned vehicle in Korea. He is actively participate several society and delivered more than 14 international keynote speaking.

Title of Speech: Autonomous Technology Progress and Special  Applications in South Korea 


Prof. Yi Pan

Georgia State University, USA

Biography: Yi Pan is currently a Regents' Professor and Chair of Computer Science at Georgia State University, USA. He has served as an Associate Dean and Chair of Biology Department during 2013-2017 and Chair of Computer Science during 2006-2013. He is also a visiting Changjiang Chair Professor at Central South University, China. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan's research interests include parallel and cloud computing, wireless networks, and bioinformatics. Dr. Pan has published more than 200 journal papers with over 80 papers published in various IEEE journals. In addition, he has published over 150 papers in refereed conferences. He has also co-authored/co-edited 43 books. His work has been cited more than 8000 times. Dr. Pan has served as an editor-in-chief or editorial board member for 15 journals including 7 IEEE Transactions. He is the recipient of many awards including IEEE Transactions Best Paper Award, several other conference and journal best paper awards, 4 IBM Faculty Awards, 2 JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship. He has organized many international conferences and delivered keynote speeches at over 60 international conferences around the world.

Title of Speech: Deep Learning for Big Data Applications - Challenges and Future Directions  

Abstract: Due to improvements in mathematical formulas and increasingly powerful computers, we can now model many more layers of virtual neurons (deep neural networks or deep learning) than ever before. Deep learning is now producing many remarkable recent successes in computer vision, automatic speech recognition, natural language processing, audio recognition, and medical imaging processing. Although various deep learning architectures and novel algorithms have been applied to many big data applications, extending deep learning into more complicated applications such as bioinformatics or medical images will require more conceptual and software breakthroughs, not to mention many more advances in processing power. In this talk, I will outline the challenges and problems in deep learning research. They include design of new architectures, handling high dimensional data, encoding schemes, mathematical proofs, optimization of hyperparameters, logic and reasoning, result explanation and hardware support for deep learning. Some solutions and preliminary results in these areas will be presented. 


Prof. Jiang Yan

North China University of Technology, China

Biography: Prof. Jiang Yan is the Dean of College of Electronic and Information Engineering at North China University of Technology, Beijing, China. He got the PhD degree from the University of Texas at Austin, Texas, USA, in 1999, and Master degree from the Institute of Semiconductors of Chinese Academy of Sciences in 1986 and Bachelor degree from the University of Science and Technology of China in 1983 respectively. Prof. Yan worked at Infineon Technologies from 1999 to 2009 and the Institute of Microelectronics of Chinese Academy of Sciences from 2009 to 2017. The projects finished in last near 20 years cover 14nm, 22nm, 32nm and 45nm node technologies. Prof. Yan’s current research areas include the integrated process, process simulation, Silicon photonics, and MEMS. Prof. Yan hold 39 US patents and 22 China patents. He has published more than 50 papers.

Prof. Shui Yu

University of Technology Sydney, Australia

Biography: Shui Yu is currently a full Professor of School of Software, University of Technology Sydney, Australia. Dr Yu’s research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 33. Dr Yu actively serves his research communities in various roles. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, IEEE Internet of Things Journal, IEEE Communications Letters, IEEE Access, and IEEE Transactions on Computational Social Systems. He has served many international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, TPC chair for IEEE BigDataService 2015, and general chair for ACSW 2017. Dr Yu is a final voting member for a few NSF China programs in 2017. He is a Senior Member of IEEE, a member of AAAS and ACM, the Vice Chair of Technical Commuittee on Big Data of IEEE Communication Society, and a Distinguished Lecturer of IEEE Communication Society.

Assoc. Prof. Simon Fong

University of Macau, Macau SAR

Biography: Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau, as an Adjunct Professor at Faculty of Informatics, Durban University of Technology, South Africa. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Australia and Asia. Dr. Fong has published over 432 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SCIE-indexed journals. Simon is also an active researcher with leading positions such as Vice-chair of IEEE Computational Intelligence Society (CIS) Task Force on "Business Intelligence & Knowledge Management", and Vice-director of International Consortium for Optimization and Modelling in Science and Industry (iCOMSI).

Title of Speech: Optimizing Deep Learning by Metaheuristics Algorithms 

Abstract: Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. Hyper-parameter optimization is a model selection problem that attempts to find the best solution among a combination set of parameters with an aim to attain high accuracy. However, the trial and error methods to optimize the hyper-parameters take a very long time to test the accuracy given that there are many variables and some of their numerical data types are continuous. In this talk, we discuss an architecture that can be used for optimizing the hyper-parameters of neural network using metaheuristics. The comparison results of using meta-heuristic methods and traditional exhaustive search method would be presented. The novelty of applying the swarm search methods for finding the best hyper-parameters would be demonstrated. The pros and cons of swarm search methods for hyper-parameter optimization would be discussed, as well as pointing out some future directions of the hyper-parameters optimization.​ 

Liangliang Song

ThingPark China, China

Biography: Steven Song is the solution & Technology VP of ThingPark China, focusing on Low power and wide area (LPWA) network and IoT solution development. Formerly he is the co-founder of Innolinks company, with proven track records of enabling cross-functional teams to define and deliver complex products and systems. With 18+ years of working experience in telecommunication and consumer electronics industry. He used to take multiple leading position in R&D and production management depts. in Multi National Corporation. From Mar, 2017 to Present, he is the VP of Solution & Technology, ThingPark China company. He is in charge of setting up the initial R&D team, development environment and efficient working process. Delivered LoRa network and network management system for operator customers and developed “smart community” application based on LoRaWAN technology. From Apr, 2015 to Mar,2017, he is the Co-founder & VP, Beijing Innolinks Technology company. Define “Smart Power Management System” (SPMS) roadmap and detailed product specifications, including GZ5100 IoT gateway, AP3100 Air-con power outlet, AC3100 thermostat, etc. Working with R&D team to evaluate and select technical solutions for Hardware, embedded software, cloud server, as well as iOS and Android applications. Drive and win AP3200 Air-con power outlet crowdfunding on Xiaomi’s platform, successfully sold 5K unit of products within 3 days, and completed distribution process. Manage external manufacturing companies for product NPI and Mass-production. From Aug, 2004 to Apr, 2015, he is the Senior Product Manager in Technicolor (China) Technology company. He is in charge of marketing research for Tablet and define product roadmap and product specifications. Took lead in TVA200 and TVA300 Tablet definition and development and won mass orders from clients such as China Telecom, Hongkong PCCW and Australia Telstra customers. Led TCA200 security Tablet definition and cooperated with system integration company – iControl in U.S., delivered over 2M unit of products in U.S. and Canadian market.

Li Tengyue

Zhuhai Institute of Advanced Technology (珠海中科先进技术研究院), Chinese Academy of Science, China

Biography: Li Tengyue, 李腾跃, PhD University of Macau, is the Head of Data Analytics and Collaborative Computing Laboratory, Zhuhai Institute of Advanced Technology (珠海中科先进技术研究院), Chinese Academy of Science, Zhuhai, China. Ms Li is leading and managing the laboratory, in R&D as well as technological transfer and incubation. She is an entrepreneur with experiences in innovative I.T. contest, with her award-winning team in the Bank of China Million Dollar Cup competition. Her latest winning work includes the first unmanned supermarket in Macau enabled by the latest sensing technologies, face recognition and e-payment systems. She is also the founder of several Online2Offline companies in trading and retailing both online and office. Ms Li is also an active researcher, manager and chief-knowledge-officer in DACC laboratory at the faculty of science and technology, University of Macau. She holds a senior membership at at IEEE Computational Intelligence Society (CIS) Task Force on "Business Intelligence & Knowledge Management", and secretary position at International Consortium for Optimization and Modelling in Science and Industry (iCOMSI).

Title of Speech: Big Data Stream Mining and Fast Machine Learning in R&D Applications 

Abstract: In this modern big data era, when data are generated in huge amount and their underlying patterns are ever changing in real-time, traditional machine learning algorithms that were designed in the grandfather generations may no longer be sufficient to satisfy the real-time requirements. In situations where you need to make well-informed decisions in real-time, the data and insights must also be timely and immediately actionable. The real-time demands from data stream mining and machine learning algorithms escalates especially in unmanned or machine-to-machine applications. In this talk, the platforms, techniques and the pros and cons of various data stream mining algorithms which are collectively known as fast machine learning algorithms are reviewed. In particular a research methodology called "Stream-based Holistic Analytics and Reasoning in Parallel (SHARP)" is presented. Case studies of the latest sensing R&D applications such as atmospheric IoT sensors, human activities recognition monitors, surveillance UAV/drones, are discussed. The data generated and used by such sensing applications are in real-time, and the control is unmanned. Hence the AI behind the intelligent and autonomous control needs to be empowered by fast machine learning algorithms. This talk sheds some light on the importance of fueling the ever complex IoT and sensing applications with the right data stream mining and machine learning algorithms.