Invited Speakers
2026 11th International Conference on Intelligent Information Technology (ICIIT) aims to gather professors, researchers, scholars and industrial pioneers all over the world. ICIIT is the premier forum for the presentation and exchange of past experiences and new advances and research results in the field of theoretical and industrial experience. The conference welcomes contributions which promote the exchange of ideas and rational discourse between educators and researchers all over the world. We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. ICIIT 2026 is welcome qualified persons to delivery a speech in the related fields. If you are interested, please send a brief CV with photo to the conference email box: iciit@cbees.net.
Assoc. Prof. Mian Zhou
Xi’an Jiaotong–Liverpool University, China
Dr. Mian Zhou is a Senior Associate Professor and Director of
Research at the School of Artificial Intelligence and Advanced
Computing, XJTLU Entrepreneur College (Taicang). He also serves
as Director of the XJTLU–Deepleaper Instant Knowledge Engine
Joint Laboratory. Dr. Zhou received his Ph.D. in Computer
Science from the University of Reading, United Kingdom. His
research lies at the intersection of artificial intelligence,
computer vision, and continual learning, with a particular focus
on multimodal intelligence, autonomous systems, and the
sustainable evolution of large-scale models. He has proposed a
series of innovative methods in continual learning, including
dynamic network architectures, adaptive knowledge transfer
mechanisms, and semantics-driven learning strategies. His work
has appeared in prestigious journals such as Pattern Recognition
and Information Processing & Management, and in top
international conferences including ICCV, AAAI, ICME, and WACV,
where he has also received recognition for excellence. Dr. Zhou
has led and contributed to multiple national and provincial
research projects, including grants from the National Natural
Science Foundation of China. His interdisciplinary research
promotes the integration of AI technologies in autonomous
driving, biomedical intelligence, and industrial applications.
He has supervised students who achieved distinction in the
CyberC International Big Data Competition and the SURF Research
Program. As Program Chair of the International Conference on Big
Data and Artificial Intelligence (BDAI), Dr. Zhou is committed
to advancing international collaboration and fostering the next
generation of AI researchers.
Dr. Thanh Tung Khuat
University of Technology Sydney, Australia
Dr. Thanh Tung Khuat is a Research Fellow at the Complex Adaptive Systems Lab and the ARC Digital Bioprocess Development Hub at the University of Technology Sydney, Australia, as well as the Co-founder and Chief Research Officer of NuverxAI Co., Ltd., Vietnam. With over a decade of experience in artificial intelligence (AI), machine learning (ML), and predictive modeling, he has made outstanding contributions to both research and industry applications, particularly in biotechnology, pharmaceutical manufacturing, logistics, finance and economics, smart agriculture, and business data analytics. As a leading expert in adaptive machine learning, explainable AI, and process optimization, Dr. Khuat has developed advanced ML models to enhance productivity and monitor biopharmaceutical manufacturing processes, optimize supply chains, and deliver highly accurate forecasts for complex financial and business intelligence systems. He has also made significant contributions to the advancement of Responsible and Explainable AI, promoting transparency and fairness in commercial AI systems. One of his key achievements is the development of Hyperbox-Brain, an open-source library for neuro-fuzzy machine learning designed for small-data learning, dynamic environments, and high interpretability. Hyperbox-Brain has been widely applied across fields such as biomedicine, smart manufacturing, and real-time time-series analytics, with tens of thousands of downloads from the global research and industrial community via GitHub, PyPI, and Conda. With a strong publication record of over 40 scientific papers in top-tier international journals and conferences, along with multiple prestigious awards for research and innovation, Dr. Khuat is not only an outstanding researcher but also a strategic advisor to enterprises and startups in their AI adoption and digital transformation journeys. He has led numerous academia–industry collaboration projects, successfully translating advanced AI solutions into practical manufacturing and business operations.
Speech Title:“Machine Learning in Biopharma 4.0”
Abstract: The biopharmaceutical industry is entering a transformative era, Biopharma 4.0, where machine learning (ML), advanced analytics, and automation converge to revolutionize how we design, monitor, and control bioprocesses. Yet, despite decades of data generation in cell culture, upstream, and downstream processes, much of this data remains underutilized. This talk explores how ML-driven intelligence is redefining the biopharma value chain, from cell line development to real-time process monitoring and control and digital twins. We will uncover how process analytical technology (PAT) data, especially from Raman spectroscopy, are being integrated into predictive ML models to enable self-optimizing bioprocesses and adaptive control strategies. Case studies from industry leaders will also illustrate how these technologies accelerate process understanding, enhance product consistency, and reduce time-to-market. Finally, we will discuss the emerging paradigm of explainable and automated ML in bioprocessing, emphasizing trust, transparency, and regulatory alignment with Quality by Design principles. As we move toward intelligent biomanufacturing ecosystems, this talk will outline the roadmap to unlock the full potential of Machine Learning in Biopharma 4.0, where data truly drives discovery, development, and delivery.