Keynote Speakers
In order to deepen the communication in all the participants, ICCBB 2025 have invited
professors from all over the world to have speeches about
Computational Biology and Bioinformatics and related fields.
Keynote Speaker I
Prof. FangXiang Wu
University of Saskatchewan
Dr. FangXiang Wu is currently a full professor in the Departments of
Computer Science, Division of Biomedical Engineering, and the Department
of Mechanical Engineering at the University of Saskatchewan. He is a
Fellow of the Institute of Electrical and Electronics Engineers (IEEE),
a Fellow of the Institution of Engineering and Technology (IET), and a
Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA).
He is a recipient of the University of Saskatchewan Distinguished
Researcher Award (the institution’s highest research honour). His
research interests include Artificial Intelligence, Machine/Deep
Learning, Computational Biology, Health Informatics, Medical Image
Analytics, and Complex Network Analytics. Dr. Wu has published over 500
journal or conference papers. His total Google scholar citations are
over 18,400 and h-index is 74. He is among top 2% world’s scientists
ranked by Stanford University. Dr Wu is serving as the editorial board
member of several international journals (including IEEE TCBB,
Neurocomputing, etc.) and as the guest editor of numerous international
journals, and as the program committee chair or member of many
international conferences.
Speech Title: "Artificial Intelligence for Biomarker Discovery"
Abstract: A biomarker, or biological marker is a measurable indicator
of some biological state or condition. Molecular biomarkers, including genes,
proteins, all sorts of non-coding RNAs (e.g., microRNAs, circRNAs, etc), have a clinical role in diagnosis,
treatment, and prognosis of diseases. With the availability of various validated biological/ biomedical data,
artificial intelligence approaches become powerful tools to predict various molecular biomarkers.
In this talk, after brief introductions to artificial intelligence, I present several machine learning or
deep learning models developed by my research group in past years for biomarker discovery,
including network energy-based methods and deep belief networks for predict disease genes,
deep matrix factorization, variational autoencoder, deep factorization machine for predicting disease-related microRNAs.
Keynote Speaker II
Prof. Wing-Kin Sung
The Chinese University of Hongkong, and
Hongkong Genome Institute
Professor Wing-Kin Sung is a Global STEM Professor in the Department of
Chemical Pathology, the Chinese University of Hong Kong. He is the
director of the JC STEM Lab of Computational Genomics. His recent
research focuses on identifying genomic mutations from high-throughput
sequencing data and on understanding the relationship between mutations
(in particular, structural variations) and diseases. Prof. Sung received
both the B.Sc. and the Ph.D. degree in the Department of Computer
Science from the University of Hong Kong in 1993, 1998, respectively. He
has over 25 years of experience in Algorithm and Bioinformatics
research. Prior to joining CUHK, Professor Sung was a Professor in the
Department of Computer Science at the National University of Singapore
(NUS) and was a senior group leader at the Genome Institute of
Singapore. He is an expert in the field of bioinformatics, who has been
leading the development of a number of bioinformatics software and has
over 290 high impact papers published in renowned academic journals,
including Bioinformatics, Cell, Nature, Nature Genetics and Nucleic
Acids Research. In recognition of his research contributions, Professor
Sung was conferred the FIT Paper Award (Japan) in 2003, the National
Science Award (Singapore) in 2006, and the Young Researcher Award (NUS)
in 2008. He has also served in the programming committee for over 70
international conferences.
Invited Speakers
Dr. Mohd Faizal Ali Akbar
Universiti Malaysia Terengganu
Dr. Mohd Faizal Ali Akhbar is a researcher and lecturer at Universiti
Malaysia Terengganu, where he serves under the Maritime Technology and
Naval Architecture programs. His current research focuses on bone
drilling simulation and experimentation, surgical drill bit design, and
simulation of related processes, with the goal of improving surgical
precision, reducing thermal damage, and enhancing patient safety. He is
the Principal Investigator of a research project funded by the Ministry
of Higher Education, Malaysia, and also contributes as a co-investigator
in several interdisciplinary grants involving materials engineering and
fluid mechanics. Dr. Akhbar has published in reputable international
journals and presented at numerous academic conferences. His work
integrates computational modeling, advanced manufacturing, and
biomedical engineering, bridging the gap between engineering innovation
and clinical applications. His broader research interests include
biomaterials for surgical tools, sustainable materials in biomedical
applications, and fluid–structure interaction simulations.
Speech Title: "Coupling Simulation and Experiment for Optimized Bone
Drilling in Surgical Applications"
Abstract: Bone drilling is a common procedure in many surgical
departments, where excessive heat generation and poor tool design can
lead to thermal necrosis and postoperative complications. This talk
presents an integrated approach that combines computational simulation
and experimental validation to study thermal and mechanical responses
during bone drilling. Finite element models were developed to capture
temperature distribution, cutting forces, and stress fields, while
experiments validated simulation predictions under various cutting
conditions. Particular emphasis is given to surgical drill bit design,
exploring geometrical modifications that improve cutting efficiency and
minimize heat generation. The findings demonstrate that optimized drill
geometry and process parameters can significantly enhance surgical
safety and efficiency, providing valuable guidelines for tool design and
clinical practice. By linking simulation, materials science, and
biomedical engineering, this research establishes a systematic framework
to improve surgical drilling technology and outcomes.
