AI/ML Track

Artificial Intelligence (AI) and Machine Learning (ML) is a perfect match for 5G. While 5G offers capabilities to support low latency and very high speeds (e.g., eMBB), massive number of devices (e.g., mMTC), heterogenous mix of traffic types from a diverse and demanding suite of applications (e.g., URLLC), AI/ML complements by learning from complex patterns to provide scope for autonomous operation, transforming 5G into a scalable real-time network that is data-driven.

AI/ML is being used for 5G network planning, automation of network operations (e.g., provisioning, optimization, fault prediction, security, fraud detection), network slicing, reducing operating costs, and improving both the quality of service and customer experience based on chatbots, recommender systems, and techniques such as robotic process automation (RPA). Further, AI and ML is being used across all layers – from disaggregated radio access layer (5G RAN), to integrated access backhaul (IAB) to the distributed cloud layer (5G Edge/Core) to fine tune performance.

For 5G distributed cloud layer, AI and ML is being used for optimizing use of system resources, autoscaling, anomaly detection, predictive analytics, prescriptive policies, and so on. Further, 5G distributed cloud layer provides acceleration technologies for AI/ML workloads to support federated and distributed learning.

Besides the above, AI/ML is also being used for customer experience management and business support systems to support a multitude of emerging applications (e.g., AR/VR, Industrial IoT, autonomous vehicles, drones, Industry 4.0 initiatives, Smart Cities, Smart Ports).

In all the above cases, aspects of data integrity, legal rights to data, data collection, data pipeline management, data lake design, and data science project cycles are considered. Further, aspects of model development, model training, model validation, model deployment, model monitoring and life cycle management including model libraries and model update/upgrade in service are also considered.

Several learning approaches such as supervised learning, unsupervised learning, reinforcement learning, federated learning, distributed learning, transfer learning, and deep learning based on algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) are utilized to train data models based on target use cases.

Motivated by this progress and to further advance related discussions, the AI/ML topical will bring together leading experts from telecom service providers, system OEMs, software service providers, silicon vendors, open source network automation projects as well as leading researchers from academia to share their perspectives on opportunities and challenges to the operation of 5G using AI/ML. It will provide a unique forum for practitioners and researchers to share perspectives on recent developments, evolving landscape of AI/ML technologies, deployment use cases in various 5G scenarios and business benefits. Architects, Developers, Engineers, Testers, and Business Leaders as well as Students and Researchers from academia will surely find it useful to listen and have an opportunity to network with experts and innovators from industry and academia.

Speakers’ Profiles:

Mazin Gilbert, AT&T Labs

Dr. Mazin Gilbert is the Vice President of Advanced Technology and Systems at AT&T Labs. He leads AT&T’s Research and Advanced Development of its network and access transformations. In this role, Mazin oversees advancements in artificial intelligence, software-defined networking and access, digital transformation, cloud technologies, open source software platforms and big data. Mazin holds 176 U.S. patents in communication and multimedia processing and has published over 100 technical papers in human-machine communication. He is the author of the book titled, “Artificial Neural Networks for Speech Analysis/Synthesis,” 1992, and an editor of a recent book on “Artificial Intelligence for Autonomous Networks,” 2018. With more than three decades of experience under his belt, Mazin’s previous work includes Bell Labs, BBC and British Telecom. He’s also worked in academia at Rutgers University, Princeton University and Liverpool University. He became an IEEE Fellow in 2012. Mazin earned a bachelor’s and a doctoral degree, with first-class honors, in electrical engineering from the University of Liverpool. He also earned an MBA for Executives from the Wharton Business School of the University of Pennsylvania.

 

Harish Viswanathan, Nokia Bell Labs

Harish Viswanathan is Head of the Radio Systems Research Group at Nokia Bell Labs. He leads an international team of researchers investigating various aspects of wireless communication systems. In his prior role as a CTO Partner, he was responsible for advising the Corporate CTO on Technology Strategy through in-depth analysis of emerging technology and market needs. Harish Viswanathan joined Bell Labs in 1997 and has worked on multiple antenna technology for cellular wireless networks, mobile network architecture, and IoT. He received the B. Tech. degree from the Department of Electrical Engineering, Indian Institute of Technology, Madras, India and the M.S. and Ph.D. degrees from the School of Electrical Engineering, Cornell University, Ithaca, NY. He holds more than 50 patents and has published more than 100 papers. He is a Fellow of the IEEE and a Bell Labs Fellow. He has served as an associate editor for the IEEE Communications Letters and as an adjunct faculty at Columbia University.

 

Chandra R. Murthy, Indian Institute of Science, Bangalore

Prof. Chandra R. Murthy is with the Electrical Communication Engineering Department, Indian Institute of Science, Bangalore. His research interests include signal processing, information theory, estimation theory, compressive sensing, and performance analysis and optimization of wireless systems.

 

Joshua Ness, Verizon

Joshua Ness is a Senior Manager at Verizon 5G Labs in New York City. He partners with enterprise, startups, and academic teams to drive innovation around 5G and co-create new 5G concepts that take advantage of complementary technologies like spatial computing, edge computing, and artificial intelligence. Joshua sits at the intersection of the application of emerging technology and the nitty-gritty under the hood that makes it all work. He uses this to create compelling stories that use historical contexts combined with current and future technology advancements in order to educate, inspire, and connect the dots for individuals and businesses.

 

Navid Abedini, Qualcomm

Navid Abedini is a Senior Staff Systems Engineer at Qualcomm Technologies Inc., where he is working on the design and implementation of wireless systems. He has been involved in the design and standardization of various technologies including LTE sidelink, CV2X, NB-IoT, 5G NR, and NR-IAB (integrated access and backhaul). He has received his Ph.D. in electrical and computer engineering from Texas A&M University in 2012. He is the recipient of the 2010 Capocelli prize for the best paper at IEEE Data Compression Conference. He holds more than 120 US and international patents and more than 1000 pending patent applications.

 

Seong-Hwan Kim, Xilinx

Seong Kim is a Sr. Director Datacenter Architect and leads the datacenter architect and specialist team at Xilinx. His key focus areas include Data Center application acceleration and offload for Compute,
Network and Storage platforms, and NFV acceleration solutions. His recent acceleration objectives are machine learning, video transcoding, database acceleration and smart NIC. Prior to his current role, he served as a system architect at AMD. He has more than 20 years of industry and research experience in the field of networking, and holds a Ph.D. degree in Electrical Engineering from the State University of New York at Stony Brook and an M.B.A from Lehigh University.

 

Workshop Co-Chairs:

Dr. Deepak Kataria
Chair,  IEEE Princeton Central Jersey Section (Region 1)

Dr. Anwar Walid
Director of Network Intelligence and Distributed Systems Research, Nokia Bell Labs