7th Annual IEEE North Jersey Advanced Communications Symposium (NJACS-2019)

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The 7th Annual IEEE North Jersey Advanced Communications Symposium (NJACS-2019) will be held at the Babbio Center, Stevens Institute of Technology, in Hoboken, NJ, on Saturday, September 14, 2019. 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

Registration

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 $ 5.00, contact ben.meyer@ieee.org for additional information and CEU fee payable at registration desk.

FREE parking available for the day's event in the parking deck located under the building. 

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

• Date: 14 Sep 2019
• Time: 09:00 AM to 07:00 PM
• All times are (GMT-05:00) US/Eastern
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• Stevens Institute of Technology
• 525 River Street
• Hoboken, New Jersey
• United States 07030
• Building: Babbio Center (Babbio building GARAGE: Entrance is in the back of the building (on the Frank Sinatra Drive))
• Click here for Map

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• Contact Event Host

• For more information, please contact:
  Yu-Dong Yao, Program Chair
  yyao@stevens.edu

  Adriaan van Wijngaarden
  Symposium Chair
  avw@ieee.org

   

• Starts 01 June 2019 01:00 AM
• Ends 14 September 2019 06:00 PM
• All times are (GMT-05:00) US/Eastern
• Admission fee ?

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Audio Recognition with Lighting IoT Systems
Dr. Jin Yu, Signify (Philips Lighting)

Abstract - Connected lighting IoT systems have sufficiently dense network to provide many services by sensing and learning the environment, with the equipped sensors. This talk uses audio as one example modality and presents a common platform for audio event detection and localization, with deep learning and signal processing techniques.

Jin Yu received his PhD in Electrical Engineering from Stevens Institute of Technology, Hoboken, NJ, in 2005. He is now with Signify (formerly known as Philips Lighting) Research, leading IoT data analytics in collaboration with Computer Science Artificial Intelligence Lab (CSAIL, Massachusetts Institute of Technology). His current research focuses on inference, fusion, and learning algorithm design and optimization for large-scale sensor networks. He was an adjunct professor with Stevens Institute of Technology from 2008 to 2009 and served as vice president for Wireless and Optical Communications Conference (WOCC) from 2010 to 2012 when he was living at NJ. He has published over 30 academic papers.

Deep Learning and Computer Vision for Radio Access Network (RAN) Planning
Dr. Shang Li, Dr. Velin Kournev, and Dr. Gaurav Thakur, AT&T Labs - Research

Abstract - The advent of 5G calls for massive investment in small cells that bring wireless access closer to the consumers. Traditional process of finding poles suitable for cell sites entails manual site inspection. With deep learning and computed vision techniques, we develop a framework that automatically detects and annotates poles to aid site planners, and has been used to classify millions of poles around the country.

Shang Li completed his Ph.D. in Electrical Engineering at Columbia University in 2017. His research interests lie in the intersection of signal processing, statistical modeling, information theory, and sequential decision theory. He has been at AT&T Labs since 2017, and has been working on machine learning research with applications to network security and mobile network planning.

Gaurav Thakur has been at AT&T Labs since 2017 and works on designing machine learning systems for planning wireless networks. His research spans a range of areas in time series analysis, Bayesian methods, information theory, econometrics and other fields. He was previously a quantitative analyst at an investment firm, a data scientist at a technology startup company and a researcher at a government lab. He completed a Ph.D. in applied mathematics at Princeton University in 2011 and a B.S. in mathematics at the University of Maryland, College Park in 2007.

Velin Kounev received his doctorate in Information Science from the University of Pittsburgh, Pittsburgh, PA, USA in 2015, and the M.S. degree in telecommunications from the same, in 2007.  From 2007 to 2011, he was a Software Engineer and a Communication System Architect for driverless real-time train control systems. He is currently working as Principle Inventive Scientist at AT&T Labs Research, focusing on Machine Learning-based 5G network planning and modeling, geospatial-temporal data systems, and real-time software design.

Towards More Autonomous UAVs Using Deep Learning
Dr. Marcus Pendleton, Air Force Research Lab

Abstract - This presentation addresses fundamental issues in autonomous UAVs and the applications of deep learning techniques in the development of autonomous UAVs.

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.

An Introduction to Blockchain-Based Distributed Learning
Prof. Shucheng Yu, Stevens Institute of Technology

Abstract - Blockchain or distributed ledger, thanks to recent advancements of cryptocurrency, has become a promising technique for "security by design" of distributed applications. Through distributed consensus, blockchain aims to offer transparent, publicly verifiable and immutable records of transactions without the involvement of any central trust party. In addition to cryptocurrency, blockchain has also found potential applications in a wide spectrum of systems. In this talk we first provide an overview to recent progresses in blockchain. Then we will discuss the potential application of blockchain in distributed learning. In particular, we will elaborate on some challenges with the marriage, for example, how to protect data privacy, how to achieve efficiency for learning, how blockchain helps in addressing typical attacks such as model poisoning attacks?

Shucheng Yu is an Associate Professor of Electrical and Computer Engineering at Stevens Institute of Technology, where he directs the Analytics and Information Security for Complex Systems Lab (AISecLab). Before he joined Stevens, Dr. Yu was an associate professor of Computer Science at the University of Arkansas at Little Rock. He received his PhD in Electrical and Computer Engineering from Worcester Polytechnic Institute in 2010. His research interest is on cybersecurity in general, with recent focuses on security and privacy in machine learning and smart systems, wireless networking and security, and distributed trust. He has published over sixty impactful research articles in academic journals and conference proceedings. He has been the editor or guest editor for five international journals, and at the organizing committee for over fifteen international conferences including IEEE Infocom and IEEE Globecom. He serves at the board of trustee for Wireless and Optical Communication Conference (WOCC) and is a Senior Member of the IEEE.