Leverage Big Data for Telecom
This panel explores different opportunities that big data brings to wireless ecosystems. The issues may include big data analytics in 4G wireless network performance, network mining, network security, traffic management, network security, monetization, and business development opportunities. In particular, the panel focuses on five questions for mobile operators, vendors, and OTTs:
- How does big data help them make 4G networks more reliable and more profitable?
- How does big data help them better understand networks, subscribers, devices, and apps subscribers’ use?
- How does big data help them discover opportunities for launching services that meet unaddressed subscriber needs?
- How does big data help them learn how customers want to be engaged and find out which areas could be most profitable for new offerings?
- How does big data help them gain insights from the network increasing the ability to innovate and capitalize on innovations sooner?
- Jin Yang, Director of Wireless Data Analytics Research, Huawei Technologies USA
- Mahmoud Daneshmand, Assistant Chief Scientist & DMTS, AT&T Labs; Industry Professor, Stevens Institute of Technology
- Dr. Rong Duan, Principle Member of Technical Staff at AT&T Labs
- Prof. Hong Man, Stevens Institute of Technology
- Prof. Guilin Wang, New Jersey Institute of Technology
Dr. Jin Yang
Dr. Jin Yang is now the Director of Wireless Data Analytics Research team, aiming to combine the strength of machine learning algorithms and years of experience and insight of wireless communication system. He joined Huawei USA in 2011. Prior to that, he worked for Motorola (UK and China) as the Director of Communication Lab in Beijing, and Lucent (UK). His expertise includes wireless systems (RAN and CN), Data Analytics, IP based communication and multimedia applications. He holds 10+ patents. Jin obtained his BSc and MSc degrees from Tsinghua University (Beijing) and PhD from Imperial College (London).
Dr. Mahmoud Daneshmand
Dr. Mahmoud Daneshmand is a Distinguished Member of Technical Staff, AT&T Labs Research; Executive Director of University Collaborations Program and Assistant Chief Scientist of the AT&T Labs; Professor of CS Department as well as Howe School of Technology Management at the Stevens Institute of Technology.
With more than 35 years of research & publications, teaching, consultation, and management experience, Dr. Daneshmand is well recognized as an expert in Probability & Stochastic Processes, Statistics, Big Data Analytics, Data Mining, and Machine Learning.
His experience spans teaching, research & publications, and management experience in academia and industry including Bell Laboratories, AT&T Labs, and University of California at Berkeley, University of Texas at Austin, Tehran University, Sharif University of Technology, National University of Iran, New York University, and Stevens Institute of Technology. He has published more than 70 journal/conference papers and book chapters. Co-authored two books, and has given several keynote talks, and served as general chair and TPC chair of many IEEE conferences. His current areas of teaching and research include Artificial Intelligence; Knowledge Discovery and Data Mining; Complex Networks Analysis, Sensor Networks and RFID Systems reliability & performance and data mining of sensor and RFID data. He has a PhD and MA in Statistics from the University of California, Berkeley, and MS and BS in Mathematics from the University of Tehran.
Dr. Rong Duan
Dr. Rong Duan, Principle Member of Technical Staff at AT&T Labs, New Jersey, USA. She received her Ph.D. in Computer Engineering from Stevens Institute of Technology. Rong has extensive experience in data mining, statistical learning and business intelligence for various business applications. Her main research areas include statistical learning theory and methods, Spatial-Temporal data modelling, Cause-effect modelling, data integration and quality assessment on big data. Rong served as Secretary/Treasurer, Vice Chair and Chair for the Data Mining Section of INFORMS (Institute of Operations Research and Management Sciences) in 2006-2008, 2008-2009, 2009-2010 respectively. She was the Data Mining cluster co-chair for INFORMS Annual Conference in 2008 and INFORMS International Beijing in 2012. Rong also served as a program co-chair for the First and Second International Symposium on System Informatics and Engineering in 2011 and 2013.
Dr. Grace Guiling Wang
Dr. Grace Guiling Wang received her BS degree from Nankai University, China. She received the PhD degree in Computer Science and Engineering and a minor in Statistics from the Pennsylvania State University at May 2006. She is currently an associate professor in the Computer Science Department at New Jersey Institute of Technology. Her research interests include mobile computing, vehicular networks, and wireless sensor networks.
Dr. Ye Ouyang
Dr. Ye Ouyang is a Distinguished Staff Scientist-Mobile Network & Device Analytics at Verizon Wireless USA Headquarters. He has over 12 years experience in telecommunications industry, working on the forefront of cutting edge wireless and big data analytics field.
Dr. Ouyang’s research lies in big data analytics and quantitative modeling for wireless networks, with a focus on 2G/3G/4G LTE network performance, network capacity, traffic patterns, user behaviors, and network and device service quality etc. by leveraging data analytics, network simulation, statistical modeling, machine learning, and data mining techniques.
He holds a Bachelor of Engineering from Southeast University in Nanjing, China, a Master of Science from Tufts University in Massachusetts, USA, and a Doctor of Philosophy from Stevens Institute of Technology in New Jersey, USA.
In 2012-2013, Dr. Ouyang as Co-Principal was awarded telecom research funding by White House, the office of Science and Technology and National Science Foundation (NSF). He authored 20+ academic papers, 3 book chapters, and 8 US Patents. Dr. Ouyang is also a columnist of SINA Technology (新浪科技), which is the largest online media portal in China. He serves as Chair for Big Data Analytics Session in IEEE WOCC and WTS Conference, and TPC and reviewer for many leading academic journals and transactions.