6th Annual IEEE North Jersey Advanced Communications Symposium (NJACS-2018)


The 6th Annual IEEE North Jersey Advanced Communications Symposium (NJACS-2018) will be held at the Babbio Center, Stevens Institute of Technology, in Hoboken, NJ, on Saturday, September 15, 2018. The symposium consists of several keynote presentations and a parallel poster session. The symposium program will cover advanced topics in AI, big data, machine learning, deep learning, and applications. The posters will be presented by graduate students and postdocs. Poster presentations will be on display all day and special dedicated exhibition times are scheduled for all attendees. There will be plenty of opportunities to interact with presenters and network with peers.

Symposium Program

09:30-10:00     Registration, Meet and Greet, Poster Set-Up

10:00-10:10     Welcome Remarks

                       Dr. Adriaan van Wijngaarden, Nokia Bell Labs

                       Amit Patel, IEEE ComSoc North Jersey Chapter Chair

10:10-10:15     Opening Remarks - Another Day of Deep Learning

                       Prof. Yu-Dong Yao, Stevens Institute of Technology

10:15-11:00     A Machine Learning Assisted Method of CCO (Coverage & Capacity Optimization) for 4G LTE Networks

                       Dr. Ye Ouyang and Mr. Thomas Li, Verizon Wireless

11:00-11:45     AlphaGo Zero and Beyond: Deep Reinforcement Learning for Machine Intelligence

                       Prof. Haibo He, University of Rhode Island

11:45-13:00     Lunch and Poster Presentations

13:00-13:45     Towards More Autonomous UAVs Using Deep Learning

                       Dr. Marcus Pendleton, Air Force Research Lab

13:45-14:30     Drone Video Analytics Using Deep Neural Network

                       Dr. Zhu Liu, AT&T Labs - Research

14:30-14:45     Poster Competition and Awards

                       Prof. Hong Zhao, Fairleigh Dickinson University

14:45-15:00     Closing Remarks 

                       Dr. Adriaan van Wijngaarden, Nokia Bell Labs

15:00-15:30     Networking



IEEE member $ 10.00
Non-member $ 20.00
IEEE Student/Graduate Student/Life Member   $ 5.00
Non-IEEE Student/Graduate Student $ 10.00


This event has limited seating and registration will close once the event reaches capacity.

CEUs (continuing education units) are available for this event as a separate fee of $ 9.00, payable at registration desk.


This symposium is being organized by the IEEE North Jersey Section and its Communications, Computer, Information Theory and Vehicular Technology Chapters. Technical support is provided by IEEE METSAC.


Organizing Committee

Symposium Chair:    Adriaan van Wijngaarden, Nokia Bell Labs

Organization Chair:  Amit Patel, Chair, IEEE North Jersey COMSOC Chapter

Program Chair:        Yu-Dong Yao, Stevens Institute of Technology

Poster Chair:           Hong Zhao, Fairleigh Dickinson University

Registration Chair:   Michael Newell, IEEE North Jersey Section

  Date and Time




  • Stevens Institute of Technology
  • 525 River Street
  • Hoboken, New Jersey
  • United States 07030
  • Building: Babbio Center
  • Click here for Map

Staticmap?size=250x200&sensor=false&zoom=14&markers=40.742756%2c 74
  • For more information, please contact: Yu-Dong Yao, Program Chair, yyao@stevens.edu

    Adriaan van Wijngaarden, Symposium Chair, avw@ieee.org


  • Starts 10 June 2018 02:48 PM
  • Ends 15 September 2018 05:00 PM
  • All times are EST
  • Admission fee ?
  • Register


Presentation 1: A Machine Learning Assisted Method of CCO (Coverage & Capacity Optimization) for 4G LTE Networks

Dr. Ye Ouyang and Mr. Thomas Li, Verizon Wireless

Abstract — Self-Organizing Network (SON) has been introduced more than a decade by the Next Generation Mobile Networks (NGMN) and later standardized by the 3rd Generation Partner-ship Project (3GPP). However, SON has ever never fully met the expectation from Mobile Network Operators (MNOs) since day one due to lack of suitable wireless based Machine Learning (ML) techniques which can empower SON with intelligence in the old days. The authors propose, validate, and productize a wireless ML based scheme ISO-SON to mitigate cell coverage and interference problems in 4G Long Term Evolution (LTE) networks. ISO-SON algorithm portfolio targets at maximally optimizing cell weak coverage and over-coverage collectively. ISO-SON is now up and running on a tier-1 MNO’s 4G LTE networks. Reference Signal Received Power (RSRP), the coverage performance indicator, has been improved by 15% for the problem cells after ISO-SON is deployed.

Dr. Ye Ouyang is the youngest Fellow in Verizon history, working on the forefront of cutting edge wireless technologies, artificial intelligence, and data science space. Dr. Ouyang is leading the Wireless Artificial Intelligence and Big Data team in Verizon Headquarters. His research lies in wireless data science and artificial intelligence, with a focus on 3G/4G LTE/5G networks & device performance, network capacity, traffic patterns, user behaviors, and network & device service quality through simulation, data mining, statistical modeling, machine learning & deep learning techniques. Dr. Ouyang serves as a member of the IEEE Big Data Standard Standing Committee (IEEE BDSC), as a Chair of the Device Analytics Working Group of the IEEE Big Data Standard Committee, as a Chair of Industry Relations of IEEE 5G Summit, Corporate Representative in ETSI, 3GPP and other standard bodies, and as a Technical Program Committee member for many leading journals, transactions, and magazines. Dr. Ouyang authored over 20 academic papers, three book chapters, two books, and he holds more than 30 patents. He holds a Master of Science from Tufts University in Massachusetts, USA, a Master of Science from Columbia University, New York, USA, and a Doctor of Philosophy from Stevens Institute of Technology in New Jersey, USA.

Thomas (Zhongyuan) Li is a data scientist at Verizon Wireless in its New Jersey headquarters. He was previously a research engineer at LSIS (Formerly LG Industrial Systems) in Korea, and a software engineer at the State Grid Corporation in China. Zhongyuan received his Master’s degree in Computer Engineering from Stevens Institute of Technology in New Jersey, USA, and holds a Master of Science in Electrical and Computer Engineering from Sungkyunkwan University, Korea. Zhongyuan has published more than 10 papers focusing on machine learning, artificial intelligence and ubiquitous computing. He serves as technical program committee member in IEEE Wireless Telecommunications Symposium and IEEE International Conference on Industrial Internet.

Presentation 2: AlphaGo Zero and Beyond: Deep Reinforcement Learning for Machine Intelligence

Prof. Haibo He, University of Rhode Island

Abstract — The recently advancements in artificial intelligence, especially the mastering of the Go game from Google AlphaGo/AlphaGo Zero, has witnessed tremendous excitements worldwide from academia, industry, and government. This impressive progress not only demonstrated the power of machine learning over complicated tasks, but also provided the opportunity of artificial intelligence/computational intelligence to play a critical role in a wide range of applications. This talk aims to discuss the recent research developments in integrated learning and control based on reinforcement learning, one of the core foundations that AlphaGo/AlphaGo Zero was develop upon. Specifically, I will introduce a new deep reinforcement learning/adaptive dynamic programing framework for improved decision-making capability, and further explore its wide applications in wireless communication systems. This framework integrates a hierarchical goal generator network to provide the system a more informative and detailed internal goal representation to guide its decision-making. Compared to the existing methods with a manual or “hand-crafted” reinforcement signal design, this framework can automatically and adaptively develop the internal goal representation over time. Under this framework, I will present numerous applications ranging from smart grid to communication networks and cognitive radio systems to demonstrate its broader and far-reaching applications. As a multi-disciplinary research area, I will also discuss the future research challenges and opportunities in this field.

Haibo He (IEEE Fellow) is the Robert Haas Endowed Chair Professor at the University of Rhode Island. He has published one sole-author book, edited one book and six conference proceedings, and authored more than 300 peer-reviewed journal and conference papers. He has delivered more than 80 invited/keynote/plenary talks around the globe. He is currently the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems. He was a recipient of the IEEE International Conference on Communications (ICC) “Best Paper Award”, IEEE CIS “Outstanding Early Career Award”, and National Science Foundation CAREER Award. More information can be found at: http://www.ele.uri.edu/faculty/he/

Presentation 3: Towards More Autonomous UAVs Using Deep Learning

Dr. Marcus Pendleton, Air Force Research Lab

Abstract: Towards More Autonomous UAVs Using Deep Learning

Dr. Marcus Pendleton is a former combat systems and cyberspace operations officer (CSO/-COO) for the United States Air Force. He is currently a cybersecurity researcher at the Air Force Research Laboratory in Rome, New York. There, he will continue to leverage his experiences in operations from the military, high performance computing as an administrator at Ames Laboratory (Iowa State University), and cybersecurity as a research assistant for the Institute of Cyber Security (The University of Texas at San Antonio) to help develop state-of-the-art cyber solutions to protect our critical infrastructures. 

Presentation 4: Drone Video Analytics Using Deep Neural Network

Dr. Zhu Liu, AT&T Labs - Research

Abstract — Using drones to perform visual inspection of cell towers instead of dispatching technicians to climb the tall towers is a major step towards risk reduction and automation. This talk will introduce some activities within AT&T Labs on using advanced visual analytics to partially automate the cell tower inspection task. Specifically, we will present a video analytics platform powered by deep neural networks, which provides a set of useful functions, including video summarization, image classification, component detection/search, etc.

Zhu Liu is a Principal Inventive Scientist at AT&T Labs - Research. He received the Ph.D. degree in Electrical Engineering from NYU Tandon School of Engineering in 2000. His research interests include video and multimedia content analysis, machine learning, big data, and natural language understanding. In 2017, he was awarded the AT&T Science & Technology Medal for his contributions and leadership in video analytics. He holds 133 granted U.S. patents and has published more than 70 technical papers.