Professor Mamitsuka started machine learning research in 1990s, when he was working with NEC Research Laboratories in Japan. At that time, machine learning was a small but promising research community, into which reseachers with a variety of disciplines were gradually coming to satisfy their interests in realizing artificial intelligence. He developed new techniques in machine learning, motivated by a lot of real-world problems like telecom churn, campaign management and web access pattern mining. Since then, he has been in the machine learning field, although his applications have been shifted into more scientific areas, particularly in life or medical sciences, after he moved to academia. His current research focus in machine learning is on graphs, high dimensional data, such as tensors, and integrative learning of different types of data.

She leads the Human-AI Lab (https://shiernee.com),
where her research focuses on applying artificial intelligence and machine learning in healthcare, particularly in maternal and fetal medicine.
With a Ph.D. from the National University of Singapore and prior experience as a research scientist at A*STAR, Singapore,
Dr. Saw has published extensively on AI-driven medical diagnostics and biomedical engineering.
She is also an active member of the Young Scientists Network–Academy of Sciences Malaysia.

Title: AI in Healthcare: From Bench to Bedside – Translating Research into Impact

Description: Artificial Intelligence holds immense promise in transforming healthcare —
but translating research into real-world clinical impact remains a complex journey.
In this keynote, Dr. Saw Shier Nee will share insights from her work at the intersection of machine learning,
biomedical engineering, and clinical collaboration. Drawing from case studies in pregnancy monitoring,
neonatal diagnostics, and oral cancer lesion detection, she will explore how AI models can be designed,
validated, and deployed with purpose. The talk will highlight lessons learned in navigating data challenges,
model generalization, clinician engagement, and regulatory pathways, offering a practical perspective
on moving from lab innovation to bedside application.