Biography
Xuan Nam Tran (Member, IEEE) received the Master of Engineering (M.E.) degree in telecommunications engineering from the University of Technology Sydney, Sydney, NSW, Australia, in 1998, and the Doctor of Engineering degree in electronic engineering from The University of Electro-Communications, Japan in 2003. He is currently a Full Professor and the Head of a Strong Research Group on advanced wireless communications, Le Quy Don Technical University, Hanoi, Vietnam. From November 2003 to March 2006, he was a Research Associate with the Information and Communication Systems Group, Department of Information and Communication Engineering, The University of Electro-Communications, Tokyo, Japan. His research interests include space-time signal processing for communications such as adaptive antennas, space-time coding, MIMO, spatial modulation, and cooperative communications. Prof. Tran was the recipient of the 2003 IEEE AP-S Japan Chapter Young Engineer Award, and a co-recipient of two best papers from The 2012 International Conference on Advanced Technologies for Communications and The 2014 National Conference on Electronics, Communications and Information Technology. He is the founding Chair and is currently the chapter Chair of the Vietnam Chapter of IEEE Communications Society. He is a Member of IEICE and the Radio-Electronics Association of Vietnam (REV).
Title : How Generative AI and the Internet of Things Can Complement Each OtherAssociate Professor, Offenburg University, Germany
It is increasingly accepted that to an important degree, “innovation” is a process of combining pre-existing ideas and technologies in novel ways. Generative AI and the Internet of Things can be seen as one successful example of such “Innovation by Combination” combining two megatrends. The Internet of Things connects anything, anywhere, anytime. Thus, it provides a platform for a truly pervasive and intelligent environment. Artificial Intelligence and most notably Edge AI use this platform and make devices intelligent.
However, training AI models for IoT applications can be a challenge, as in many cases, devices come with very limited ressources, are spatially distributed, and sample only smaller amounts of data. Thus, we are often suffering from a “small data challenge”. Generative AI may help to overcome this challenge. The keynote disccusses the manifold IoT applications, which potentially could benefit from Generative AI, shows basic architectures and approaches for such solutions on the different IoT layers, and presents several examples from the community and from the author’s own research.
Biography
Dr. Axel Sikora is an associate professor at Offenburg University, Germany, where he serves as Scientific Director of the Institute of Reliable Embedded Systems and Communication Electronics, a leading R&D institute for IIoT connectivity solutions. He is also deputy director of Hahn-Schickard Association of Applied Research, where he manages the division “Software Solutions”, including several research groups around AI.
Dr. Sikora is also engaging in several standardization activities around secure and efficient IIoT connectivity. Since many years, he is serving as chairman of the embedded world Conference, the world’s largest event on the topic. In parallel, he is engaged in some deep-tech spinoff companies.
Title : “AI for Science and Systems for AI”Ph.D. Rajkumar Kettimuthu
Argonne National Laboratory
The University of Chicago
United States of America
Biography
Dr. Rajkumar Kettimuthu is a Computer Scientist and Group Leader at Argonne National Laboratory, a Senior Scientist at The University of Chicago and a Senior Fellow at Northwestern University. His research interests include AI for science, advanced wired and wireless communications for science, and Quantum networks. Data transfer protocol and tools developed by him and his colleagues at Argonne have become the de facto standard for file transfers in many science environments. With 60K+ installations in six continents, these tools perform 50M+ file transfers & move 5 Petabytes+ of data every day. AI for science tools developed by his team at Argonne are being used in many science environments. These tools have been highlighted by top scientific journals and have won multiple awards at prestigious venues. He has co-authored 150+ peer-reviewed articles most of which appeared in premier journals and top IEEE/ACM conferences, and several of which won best paper award. His work has featured in 20+ news articles. He is a recipient of the prestigious R&D 100 award. He is a distinguished member of ACM and a senior member of IEEE.
Abstract Deep learning techniques use multi-layer (“deep”) neural networks (DNNs) to learn representations of data with multiple levels of abstraction. These techniques can discover intricate structure in a dataset by using a back-propagation algorithm to set the internal parameters that are used to transform data as they flow between network layers. We have applied deep learning methods to accelerate various science applications including ones from light sources and climate science. In this talk, I will provide an overview of this work. We have also developed systems such as FairDMS (Findable, Accessible, Interoperable and Reusable Data and Model Service) and FreeTrain (A Framework to Utilize Unused Supercomputer Nodes for Training Neural Networks) to accelerate deep learning training. I will discuss these systems as well in my talk.
