Dr. Chockalingam Aravind Vaithilingam
Director, Made Ecosystems LLP
Adjunct Professor, Batangas State University
Phillippines
From Equations to Intelligence: Mathematical Foundations Driving Real-World AI Systems
Artificial Intelligence is often seen as code, data, and computation—but at its core, it is a story written in mathematics. This keynote reimagines mathematical foundations not as abstract theory, but as the invisible force shaping the intelligence of machines and the future of human decision-making. From probability-driven language models to optimization-powered autonomous systems, the talk journeys through how equations evolve into adaptive, learning systems that transform industries and societies. Moving beyond technical boundaries, it explores a future where mathematical thinking fuels not just algorithms, but ecosystems of innovation—enabling personalized healthcare, resilient supply chains, intelligent agriculture, and inclusive digital experiences. As AI transitions from tools to collaborators, this session challenges researchers, educators, and innovators to rethink the role of foundational knowledge in building ethical, scalable, and human-centric AI systems that create meaningful real-world impact.
Prof. Lipo Wang
School of Electrical and Electronic Engineering
Nanyang Technological University
Singapore
Progress in Deep Learning for Medical Image and EEG Classification
In recent years, deep learning has been enjoying many successful applications in the entire spectrum of technology. This talk highlights some of our recent research results in deep learning for medical image and EEG classification. Our algorithms include 3D convolutional neural networks (CNNs) with thresholding and attention, a transformer-based multilevel filtering framework, batch normalization with domain-matching, and a time-frequency transformer (TFormer). We demonstrate our algorithms in various challenging problems, such as brain trauma diagnosis, glaucoma diagnosis, emotion and fatigue recognition based on multi-subject EEG signal classification.