Invited Speakers
Prof. Antonio Alarcón-Paredes
National Polytechnic Institute, Mexico
Bio: Dr. Antonio Alarcón-Paredes is a
professor at the Intelligent Computing
Laboratory of the Computer Research Center
at the National Polytechnic Institute, where
he also earned his Ph.D. in Computer
Science. With over a decade of experience in
the field, he has authored multiple articles
in high-reputed journals and international
conferences. He holds one granted patent and
three others pending. Since 2020, he has
been recognized as a Level 1 National
Researcher by the Ministry of Humanities,
Science, and Technology of the Mexican
government and is also a member of the
Mexican Society for Artificial Intelligence.
His research interests include the
development of algorithms and applications
in areas such as image analysis, computer
vision, machine learning, deep learning,
intelligent computing applications, and
biomedical applications.
Prof. Chinthaka Premachandra
Shibaura Institute of Technology, Japan
Bio:
Chinthaka Premachandra (Senior Member, IEEE)
was born in Sri Lanka. He received his B.Sc.
and M.Sc. degrees from Mie University, Tsu,
Japan, in 2006 and 2008, respectively, and
his Ph.D. degree from Nagoya University,
Nagoya, Japan, in 2011. He is a Professor in
the Department of Advanced Electronic
Engineering, School of Engineering and
Graduate School of Engineering, Shibaura
Institute of Technology, Tokyo, Japan, where
he currently serves as the Director of the
Image Processing and Robotics Laboratory.
His research interests include AI, UAVs,
image processing, audio processing,
intelligent transport systems (ITS), and
mobile robotics. He has authored or
co-authored over 200 publications in reputed
journals and conferences related to these
fields.
He is currently an Associate Editor for IEEE
Robotics and Automation Letters (R-AL) and
IEICE Transactions on Information and
Systems. He is a member of IEEE, IEICE
(Japan), SICE (Japan), RSJ (Japan), and SOFT
(Japan). He has received numerous awards,
including the IEEE SENSORS LETTERS Best
Paper Award from the IEEE Sensors Council in
2022 and the IEEE Japan Medal from the IEEE
Tokyo Section in 2022.He is also the
Founding Chair of the International
Conference on Image Processing and Robotics
(ICIPRoB), which is technically co-sponsored
by IEEE.
Prof. Abril Uriarte Arcia
CIDETEC - IPN, Mexico
Bio: Dr. Uriarte is a
teacher/researcher at the Center for
Innovation and Development in Computing
Technology (CIDETEC) of the Instituto
Politécnico Nacional (IPN), México, since
2016. His areas of work include topics
related to machine learning, pattern
classification, neural networks, deep
learning, associative memories, time series
prediction, and data stream classification.
She has participated in the development of
projects where intelligent computing methods
are applied to problems of social impact
such as pre-diagnosis of diseases and
environmental monitoring.
Dr. Uriarte earned a BSc in Computer
Engineering from the National University of
Engineering (UNI) in Managua, Nicaragua. She
received her MSc and PhD in Computer Science
from The Computing Research Center, IPN.
She is a professor in Artificial
Intelligence Engineering and Master's in
Computing Technology programs at the IPN, in
Bioinspired Algorithms and Machine Learning
topics. She has participated in the creation
of academic programs such as the Artificial
Intelligence Engineering program and the
graduate program (master's and doctorate) in
Artificial Intelligence Science and
Technology and Data Science. Member of the
IPN research networks in Computing and
Artificial Intelligence and Data Science.
Assoc. Prof. Kezhi Mao
Nanyang Technological University, Singapore
Bio: Mao Kezhi obtained his BEng,
MEng and PhD from Jinan University,
Northeastern University, and University of
Sheffield in 1989, 1992 and 1998
respectively. He is now an Associate
Professor at School of Electrical and
Electronic Engineering, Nanyang
Technological University, Singapore. His
research covers a couple of subfields of
artificial intelligence (AI), including
machine learning, computer vision, natural
language processing, and information fusion.
Over the past 25 years, he has developed
novel algorithms and frameworks to address
various issues in the field of artificial
intelligence. He has published over hundred
research papers on top journals and
conferences, which have received 10000+
citations (Google Scholar).
As a strong advocate of translational
research, he has collaborated with
government agencies and hospitals and
developed a couple of prototypes of AI
systems for image processing and natural
language processing. He served as consultant
for a number of companies such as Deloitte &
Touche, ST Engineering, Zhuyi Technologies,
and Rakuten Group etc, advising on R&D of AI
and machine learning.
He now serves as Member of Editorial Board
of Neural Networks, Academic Editor of
Computational Intelligence and Neuroscience,
and General Chair, General Co-Chair, Invited
Panelist, and Invited Speaker of a number of
international conferences.
Asst. Prof. Sook Shin
Virginia Tech, USA
Bio: Dr.
Sook Shin is a Collegiate Assistant
Professor in the Department of Electrical
and Computer Engineering at Virginia Tech.
Her primary area of expertise is
bioinformatics, particularly in the analysis
of gene and disease data. Through her
research, she applies advanced computational
methods to better understand complex
biological systems and their implications
for human health. In addition to her work in
bioinformatics, Dr. Sook Shin has recently
expanded her research into the area of
precision livestock management, using
Artificial Intelligence (AI) to improve the
monitoring and management of livestock. This
work focuses on automated behavior analysis
and predictive modeling, with the goal of
optimizing livestock health and
productivity. By predicting factors such as
weight and body condition, her research aims
to provide farmers with better tools for
managing livestock efficiently while
ensuring animal well-being. Dr. Sook Shin
earned her Ph.D. from Virginia Tech in 2012,
which laid the foundation for her current
research in both bioinformatics and AI. Her
interdisciplinary approach to combining
computational methods with agriculture and
health has opened up new possibilities for
both fields, contributing valuable insights
to the growing intersection of technology,
biology, and farming.
Asst.
Prof. Isaac Kofi Nti
University of Cincinnati, USA
Bio: Dr. Isaac Kofi Nti is an Assistant Professor
and Co-lead of the Information Technology
Analytics Center (ITAC) at the School of
Information Technology, University of
Cincinnati, Ohio, USA. He holds a Ph.D. in
Computer Science from the University of
Energy and Natural Resources (UENR) and
brings over 16 years of experience in higher
education. Dr. Nti has published over 60
research papers in highly peer-reviewed
journals, garnering more than 2,400
citations worldwide. Building on his
extensive experience, Dr. Nti's research
interests include applied machine learning
in cybersecurity, education, health
informatics, energy systems, agriculture,
finance, and data privacy. As a seasoned
academician and researcher, Dr. Nti is
dedicated to advancing the field of applied
machine learning.
Assoc. Prof. Jiaxin Cai
Xiamen University of Technology, China
Bio:
Jiaxin Cai received his Ph.D. degree in
Information and Computation Science from Sun
Yat-Sen University in 2014. He also received
his M.S. degree and B.Sc. degree in
Bio-medical Engineering from Southern
Medical University in 2011 and 2008
respectively. Currently, he is an associate
professor in the School of Mathematics and
Statistics at Xiamen University of
Technology. He has authored over 40
peer-reviewed papers at academic journals
and conferences. His current research
interests include machine learning, computer
vision and bio-medical engineering.
Assoc. Prof. Weibin Wang
South China Normal University, China
Bio:
Weibin Wang received his B.E. degree in 2017
from Northeastern University, China, and his
M.E. in 2019 and D.E. degree in 2022 both
from Ritsumeikan University, Japan. He
served as an Assistant Researcher and
Postdoctoral Fellow at Zhejiang Lab,
Hangzhou, China, from 2022 to 2024. He is
currently an Associate Research Fellow at
the School of Mathematical Sciences, South
China Normal University, China, and a core
member of the Machine Learning and
Optimization Computing Laboratory.His
research interests span big data governance,
Multimodal Large Language Models, AI
algorithm design, and their applications in
intelligent healthcare. His work focuses on
advancing medical image processing, 3D
medical imaging reconstruction, and mixed
reality interaction for medical systems,
addressing critical challenges at the
intersection of medicine and artificial
intelligence.
Weibin Wang published over 20 papers in
international conferences and journals,
including ACCV, EMBC, and others, and holds
6 awarded national invention patents. He
actively contributes to the academic
community as a reviewer for leading journals
such as Neurocomputing and Frontiers in
Oncology, as well as prestigious conferences
including MICCAI and EMBC.He was honored
with the Excellence Award at the 2020
Chunhui Cup Innovation & Entrepreneurship
Competition for Overseas Chinese Scholars, a
national event jointly organized by the
Chinese Ministry of Education and Ministry
of Science and Technology.
Title:
"Artificial Intelligence and Mathematical
Methods in Pancreatic Cancer Evolutionary
Modeling and Liver Cancer Auxiliary
Diagnosis"
Abstract:
Recently, deep learning (DL) has become a
transformative force across academic and
industrial fields, particularly in medical
image analysis. Despite its success in
achieving cutting-edge performance, the
integration of DL into clinical practice
remains limited. A key challenge lies in the
gap between computational models and the
domain-specific anatomical knowledge
inherent to medical expertise. In this
presentation, we introduce novel frameworks
that bridge this divide through
knowledge-guided AI methodologies, focusing
on two interconnected research directions:
(1) Evolutionary Modeling of Pancreatic
Cancer: We propose a hybrid computational
framework that synergizes mathematical
modeling with Physics-Informed Neural
Networks (PINNs). By integrating phase-field
equations—a physics-based approach for
simulating tumor growth dynamics—with PINNs,
our method captures the spatiotemporal
evolution of pancreatic cancer. This
approach combines experimental data with
tumor growth modeling simulations, enabling
dynamic predictions of tumor progression.
(2) AI-Agent Systems for Liver Cancer
Diagnosis: We develop an AI-Agent
architecture that combines multimodal large
language models (LLMs) with structured
medical knowledge. Leveraging clinical
guidelines and clinician expertise, the
system processes imaging and clinical text.
A key innovation is the incorporation of
diagnostic guidelines and expert consensus
as semantic constraints, ensuring strict
adherence to clinical protocols while
maintaining decision stability.
Dr. Vishnu S. Pendyala
San Jose State University, USA
Bio: Vishnu S. Pendyala, PhD is a faculty member
in Applied Data Science and an Academic
Senator with San Jose State University,
current chair of the IEEE Computer Society
Santa Clara Valley Chapter, and IEEE
Computer Society Distinguished Contributor.
During his recent 3-year term as an ACM
Distinguished Speaker and before that as a
researcher and industry expert, he gave
numerous (80+) talks in various reputed
forums. Some of these talks are available on
YouTube and IEEE.tv. He is a senior member
of the IEEE and ACM and has over two decades
of experience in the software industry in
the Silicon Valley, USA. His book, “Veracity
of Big Data,” is available in several
libraries, including those of MIT, Stanford,
CMU, the US Congress and internationally. In
2023, Dr. Pendyala served on the US
government's National Science Foundation
(NSF) proposal review panel.
Dr. Rahul Kumar JAIN
Tiwaki Co., Ltd. Japan
Bio: Rahul Kumar JAIN received BCA
degree from Dr. H. S. Gour Central
University, Sagar, India, and MCA degree
from RGPV University, Bhopal, India in 2011
and 2015, respectively. He received PhD
degree from Ritsumeikan University, Japan in
2022. He was a starting assistant professor
and a researcher fellow at Ritsumeikan
University, Japan. He is currently an AI
researcher at Tiwaki Co., Ltd. Japan. His
current research interests include computer
vision, machine learning, and image
processing
Title:
"Multimodal Learning and Domain Adaptation
for Generalized Detection in Medical and
Real-World Images"
Abstract:
Accurate and
generalizable detection in medical imaging
is essential for developing reliable
computer-aided diagnosis (CAD) systems,
particularly in complex tasks such as liver
lesion detection using various medical
imaging modalities. In this talk, two
complementary research directions are
introduced to address key challenges: domain
adaptation and multimodal learning for
detection tasks across diverse medical
imaging scenarios. First, domain adaptation
techniques are presented to address the
problem of domain shift, which arises due to
variations in imaging protocols, scanner
types, and multi-center data distributions.
A recognition framework is employed that
incorporates adversarial learning and
divergence-based feature alignment to
minimize domain gaps across CT phases and
acquisition centers. In addition, a
multimodal detection approach is discussed,
in which image and text features are jointly
utilized to enhance anomaly recognition
across different medical imaging modalities.
The framework enriches semantic
understanding through textual attributes,
such as category, location, and size,
extracted from existing annotations, without
requiring additional labelling. During
inference, the model operates in a text-free
mode, ensuring efficiency and eliminating
reliance on text prompts. These approaches
collectively demonstrate how domain
adaptation and multimodal learning
strategies can be applied to develop
generalizable and efficient detection
systems in medical imaging. Experimental
results across multiple datasets are
presented to validate the effectiveness of
the proposed methods in addressing
challenges in both medical and real-world
imaging scenarios.