Jun Wang is a Chair Professor of Computational Intelligence in the Department of Computer Science and the School of Data Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, and Shanghai Jiao Tong University. He received a B.S. degree in electrical engineering and an M.S. degree from Dalian University of Technology and his Ph.D. degree from Case Western Reserve University. He was the Editor-in-Chief of the IEEE Transactions on Cybernetics. He is an IEEE Life Fellow, IAPR Fellow, and a foreign member of Academia Europaea. He is a recipient of the APNNA Outstanding Achievement Award, IEEE CIS Neural Networks Pioneer Award, CAAI Wu Wenjun AI Achievement Award, and IEEE SMCS Norbert Wiener Award, among other distinctions.

Title : The State of the Art of Collaborative Neurodynamic Optimization

Abstract : The past four decades witnessed the birth and growth of neurodynamic optimization, which has emerged as a potentially powerful problem-solving tool for constrained optimization due to its inherent nature of biological plausibility and parallel and distributed information processing. Despite the success, almost all existing neurodynamic approaches a few years ago worked well only for optimization problems with convex or generalized convex functions. Effective neurodynamic approaches to optimization problems with nonconvex functions and discrete variables are rarely available. In this talk, the advances in neurodynamic optimization will be presented. Specifically, In the proposed collaborative neurodynamic optimization framework, multiple neurodynamic optimization models with different initial states are employed for scattered searches. In addition, a meta-heuristic rule in swarm intelligence (such as PSO) is used to reposition neuronal searches upon their local convergence to escape local minima toward global optima. Problem formulations and experimental results will be elaborated to substantiate the viability and efficacy of several specific paradigms in this framework for supervised/semi-supervised feature selection, supervised learning, vehicle-task assignment, model predictive control, energy load dispatching, and financial portfolio selection.

Dr. Lovekesh Vig leads the Deep Learning and Artificial Intelligence research area at TCS Research, New Delhi where he is currently serving as a Distinguished Chief Scientist. He completed his PhD in Computer Science from Vanderbilt University, USA and has since worked at Bloomberg, R&D, New York and has served as a faculty at the School of Computational and Integrative Sciences, Jawaharlal Nehru University before joining TCS Research. His principal research interests are in Neuro-Symbolic integration, Document Understanding, NLP for real world conversational systems, Deep Causal Inference and Program synthesis. Dr. Vig's research team at TCS Research develops AI solutions for enterprise problems in the domains of Personalization, Robotics and Sensor Analytics, Medical Imaging, Information Extraction, Knowledge Management and AI for Science contributing to commercial assets in these areas. He has over 100 papers and 25 international patents to his name with an h-index of 31.

Title : Knowledge Inference with LLMs for the Enterprise

Abstract : The spectrum of knowledge oriented tasks that AI can competently perform is rapidly changing, providing new avenues for automation and efficiency. However enterprise knowledge work often proceeds in stages with each stage entailing a subtle interplay between AI and human intervention to maximize the benefits of AI enabled efficiency gains. A futher requirement is the ability to query diverse proprietary knowledge sources such as Enterprise knowledge graphs, Databases, Image, Video and Document repositories and assimilate the retrieved knowledge into a coherent response. Large Language models offer new possibilities for inference from these diverse sources, however several research challenges remain to ensuring provenance, reliability, and performance for enterprise knowledge applications leveraging LLMs. In this talk, I will try to shed light on these research problems, our endeavour to overcome these research challenges and speculate on the future of Enterprise knowledge management.

Dr. John Ashley currently leads the solution architecture function for our global, mission-focused public sector organization, and is part of the leadership team for the worldwide NVIDIA Technology Centers. He is especially focused on helping governments improve the security, safety, health, climate resilience and economic well-being of the people they serve while developing their own soveriegn AI Capability John was formerly the General Manager of the Global Financial Services and Technology team at NVIDIA, focused on global trends and directions in accelerated compute and AI for the entire sector – from hedge funds, fintech and banking to insurance. He also started and led NVIDIA’s Professional Services (Consulting) Deep Learning Practice and the NVIDIA Deep Learning Professional Services Partner program; managed the relationship with IBM’s Software and Cognitive groups, was a Senior Solutions Architect covering Financial Services based in New York and then London and supported NVIDIA’s work with the Square Kilometer Array radio astronomy programs.  He has been with NVIDIA for over 13 years. He holds a doctorate in Computational Sciences and Informatics, and both BS and MS degrees in Electrical Engineering. Prior to joining NVIDIA, his experience can best be described as varied – he has been a data scientist, project manager, systems architect, DBA, and developer – working in vendor, consulting, and end user firms in utilities, government, and finance. He holds a US Patent in predictive analytics.

Title : Sovereign AI 101

Abstract:

This Keynote focuses on :

1. Sovereign AI is about it all -- Data, AI Factories, Models, Talent, Ecosystem, Deployment
2. When does it matter that my AI is Sovereign – is it about Science or Culture, Here or Everywhere?
3. What happens when science meets culture?