Arun Iyengar

Title: Data Analysis for Improving Investments: A Critically Important Application for the Edge


Arun Iyengar is Co-Founder and Partner of Intelligent Data Management and Analytics, LLC. He has worked at both IBM’s T.J Watson Research Center and Hewlett-Packard and consulted for several other companies. Arun’s work has been incorporated into products and services by many companies as well as commonly used open source software. His caching and load balancing techniques are widely used to improve performance for Web sites and distributed applications. He has also developed widely used techniques for dynamically generating Web content which have been incorporated into Web content management systems as well as a widely used method for preserving state on the Web without using cookies. Arun’s techniques for dynamic memory allocation and pricing are widely used in serverless (cloud) computing.

Arun is an IEEE Fellow and a Fellow of the Asia-Pacific Artificial Intelligence Association. He has published 10 award-winning research papers and received the IFIP Silver Core Award. He has PhD and Master’s degrees from MIT where he was a National Science Foundation Graduate Fellow and a Bachelor’s degree Summa Cum Laude and with Distinction from the University of Pennsylvania.


We present a software system for analyzing historical data on financial securities as well as past economic data to make better investments and improve investment returns. A key aspect is picking the right financial securities based on current macroeconomic and financial conditions. Financial investing is a critically important application benefiting from ubiquitous access. As such, it benefits from edge computing wherein at least part of the application runs on a personal or mobile device in close proximity to the user. This talk presents an overview of key aspects of financial markets as well as techniques for making better investments.

Yan Zhang
University of Oslo, Norway

Title: Machine Learning in Digital Twin Edge Networks


Yan Zhang is currently a Full Professor with the Department of Informatics, University of Oslo, Norway. His research interests include next-generation wireless networks leading to 6G, green and secure cyber-physical systems. Dr. Zhang is an Editor for several IEEE transactions/magazine. He is a program/symposium chair in a number of conferences, including IEEE IWQoS 2022, IEEE ICC 2021, IEEE SmartGridComm 2021. He is the Chair of IEEE Communications Society Technical Committee on Green Communications and Computing (TCGCC). He is an IEEE Communications Society Distinguished Lecturer and IEEE Vehicular Technology Society Distinguished Speaker. He was an IEEE Vehicular Technology Society Distinguished Lecturer during 2016-2020. Since 2018, Prof. Zhang was a recipient of the global “Highly Cited Researcher” Award (Web of Science top 1% most cited worldwide). He is Fellow of IEEE, Fellow of IET, elected member of Academia Europaea (MAE), elected member of the Royal Norwegian Society of Sciences and Letters (DKNVS), and elected member of Norwegian Academy of Technological Sciences (NTVA).


In this talk, we mainly introduce our proposed new research direction:  Digital Twin Edge Networks (DITEN). We first present the concept and model related to Digital Twin (DT) and DITEN. Then, we focus on new research challenges and results when machine learning is exploited in DITEN, including federated learing, deep reinforcement learning and transfer learning. Edge association and DT mobility, as unique research questions, will be defined and analyzed. We are also expecting that the talk will help the audience understand the future development of edge computing, e.g., digital twin edge networks in the context of Metaverse.