Professor Mamitsuka’s journey into bioinformatics began during his undergraduate studies in biochemistry, where he developed a strong interest in computer programming despite the limitations of early computing environments. After earning his bachelor’s degree in Biochemistry and Biophysics, he pursued graduate studies in computer science, obtaining a Master’s in Information Engineering. He began his research career at NEC Research Laboratories in Japan, where he worked in the Machine Learning group. There, he contributed to the development of novel techniques in machine learning and data mining, addressing practical challenges such as telecom churn, campaign management, web access pattern analysis, and sequence analysis in bioinformatics. During his tenure at NEC, he also completed his PhD in information sciences in the area of machine learning and bioinformatics.  Then he moved to Institute for Chemical Research (ICR) in Kyoto University to focus more on biological applications and have built data mining techniques for solving problems in life sciences, particularly those over graphs and networks, such as gene networks, medical pathways and molecular structures. Professor Mamitsuka is currently a faculty of the Bioinformatics Center of ICR.

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.