Prof. Guoping Qiu
The University of Nottingham, UK & The University of Nottingham Ningbo, China
Speech Title: From Camera to AI: The Future of Visual Content Creation
Abstract: Vision plays a fundamental role in human
perception, learning, and cognition, with over 80% of our
interactions with the world being mediated through sight. It
is no surprise, then, that computational visual content
creation—from capturing the beauty of the natural world with
digital cameras to generating entirely artificial scenes
using AI—has seen remarkable advancements in recent
decades. However, despite significant progress in digital
imaging and artificial intelligence, critical challenges
remain. Traditional digital photography still struggles with
mismatches between the dynamic range of natural scenes and
the limitations of display and print media, leading to
compromised visual fidelity. Meanwhile, ensuring that
AI-generated content (AIGC) adheres to physical realism
while maintaining creative flexibility remains an ongoing
challenge. Moreover, even when breakthroughs are made in
visual content creation theory, translating these
advancements into practical, user-friendly tools that cater
to real-world needs is an equally formidable task. In this
talk, I will explore key technical hurdles in both natural
and AI-driven visual content creation, discussing
state-of-the-art solutions that bridge the gap between
theory and practice. I will also introduce an AI-powered
digital creativity platform designed to empower users in
seamlessly crafting high-quality visual content, blending
the precision of science with the boundless possibilities of
artificial intelligence.
Bio: Professor Guoping Qiu researches neural networks and their applications in image processing. He pioneered application of neural networks to image feature extraction, introducing one of the earliest representation learning methods that leveraged unsupervised competitive neural networks for image representation. He also spearheaded learning-based super-resolution techniques and developed early neural network solutions for compression artifact removal, well before deep learning became mainstream in these applications. Professor Qiu has been at the forefront of HDR imaging, pioneering tone-mapping methods that have fundamentally transformed how HDR content is processed and displayed. Innovations from his research group have been successfully transferred to award-winning digital photo editing software such as HDR Darkroom and Fotor, which are used by hundreds of millions of consumers worldwide. His recent research focuses on deep learning, visual-language modeling, and large language models (LLMs), applying these cutting-edge technologies to some of the most complex challenges in digital imaging. As Chief Scientist at Everimaging (www.everimaging.com), the company behind HDR Darkroom and Fotor, he is driving advancements in imaging technologies to solve real-world problems. With a distinguished career spanning academia and industry, Professor Qiu’s contributions have had a lasting impact on both fundamental research and real-world applications in imaging technology.
Professor Qiu currently holds the position of Chair Professor of Visual Information Processing at the School of Computer Science, University of Nottingham, UK. Additionally, he serves as the Vice Provost for Education and Student Experience at the University of Nottingham Ningbo China (UNNC), overseeing the education and student experience of a diverse academic community of over 10,000 students and 1,000 staff from more than 70 countries and regions. UNNC delivers all its teaching in English and offers undergraduate, Master's, and PhD programs across business, humanities, social sciences, and science and engineering, awarding degrees from the University of Nottingham.
Dr. Sajjad Hussain Chauhdary
Huzhou Normal University, China
Speech Title: Navigating the AI-Cybersecurity Landscape:
Opportunities and Risks
Abstract: Artificial Intelligence has emerged as an
essential resource for cybersecurity, which enables
exceptional solutions for “threat detection”, “incident
response” and “vulnerability assessments”.
However, the interoperability of AI with cybersecurity
systems raises serious challenges. These challenges include
“technological limits”, “ethical considerations”, and
“adversarial threats”.
The technological limitations or challenges includes the
lack of quantitative, high-quality datasets, the
interpretability by AI models, and the possibilities of
adversarial attacks may manipulate AI systems. Ethical
considerations or concerns raise questions about privacy,
bias, and responsibility, as AI-powered systems may
unintentionally perpetuate existing social biases or make
decisions with unintended consequences. Adversarial threats
pose significant concerns because hostile actors can exploit
AI system potential to launch large scale assaults to
breakdown digital infrastructures of a country or even
globally. To overcome these difficulties, a comprehensive
“AI powered Cybersecurity “strategy is required, which
includes thorough research and development, rigorous “AI
enabled Cybersecurity” frameworks, and ethical standards.
By proactively tackling these concerns, we may fully realize
AI's potential for improving cybersecurity and safeguarding
digital systems.
Sajjad Hussain Chaudhary is currently an Associate Professor
at the College of Information Engineering, Huzhou Normal
University. Prior to his current role, he served as an
Associate Professor at the College of Computer Science and
Engineering (CCSE), University of Jeddah from September 2016
to September 2024.
Before entering academia, Dr. Chaudhary held the position of
Senior Research Engineer at LG/LSIS Co., Ltd., Advance
Technology R&D Center in 2011. During his tenure, he was
recognized with the "Best Research Award" (2012) and the
"Best Project Award" (2014).
Dr. Chaudhary earned his Ph.D. degree from Korea University,
ranked 69th in the QS World University Rankings 2021. He was
awarded a scholarship by the Korean Government to pursue his
doctoral studies. In 2006, he obtained his M.S. degree from
Ajou University, South Korea.
With a strong research focus on Cybersecurity, Industrial
Internet of Things, and Communication, Dr. Chaudhary has
authored over 40 publications in international journals and
conferences.
Throughout his career, Dr. Chaudhary has been actively
involved in standardization efforts, serving as a member of
major organizations such as SAE International (standard
J2847/1-5), ZigBee Alliance (SEP 2.0), and ISO/IEC (15118).
Dr. Haozhi Huang
Macau University of Science and Technology, China
Speech Title: Object Tracking in autonomous driving: Past,
Present and Future
Abstract: Object tracking is one of the fundamental tasks in
computer vision, and it is essential to a robust autonomous
driving system. Human drivers analyze the surrounding
environment of the vehicle to anticipate the occurrence of
danger and take corresponding measures in advance. However,
referencing the occurrence of danger is challenging. It
requires a model to analyze a time series sequence of
observing data from different types of sensers. Object
tracking is the key to extract motion sequences from
different objects, as it estimating their trajectories
simultaneously. We will provide a comprehensive summary of
the evolution of object tracking technology, offering an
in-depth analysis of the current state-of-the-art tracking
frameworks, encompassing both deep learning-based and
traditional, non-deep learning approaches.
Haozhi Huang, received his M.Sc and Ph.D. degree from Macau University of Science and Technology, Macau, China, in 2015 and 2020, respectively. He is now an assistant professor at the school of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China. His current research interests include computer vision for 3D visions of point cloud processing, person re-identification and object recognition and tracking. He is also study the data mining problems in traditional Chinese medicine prescriptions using knowledge graph and self-organizing map during his postdoctoral period in Zhuhai Fudan Innovation Institute and Fudan University.
Dr. Zhiyao Liang
Macau University of Science and Technology, China
Speech Title: Advancing Chinese Traditional Medicine with
AI: Multi-Modal Innovations and Literary Insights
Abstract: Chinese Traditional Medicine (CTM) represents a
rich tapestry of diagnostic practices, herbal knowledge, and
literary traditions. The AI era provides exciting
opportunities to advance the study of CTM.
This presentation explores how AI can enhance CTM practices,
combining multi-modal capabilities with the rich insights
embedded in CTM texts. Multi-modal AI systems can digitize
and optimize the four diagnostic methods—望 (observing), 闻
(listening), 问 (inquiring), and 切 (palpating)—through
innovations like computer vision for tongue and facial
analysis, natural language processing for understanding
patient narratives, and sensor technologies for pulse
detection. These technologies provide precision while
honoring CTM's holistic approach.
Equally vital is the role of AI in managing and discovering
insights from CTM literature. By processing centuries of
written texts, AI can uncover hidden patterns, improve
access to herbal formulations, and facilitate
cross-referencing between traditional and modern medical
knowledge. Challenges such as precise language
understanding, preserving cultural authenticity, managing
data complexity, and ensuring ethical AI applications will
also be addressed.
This talk highlights the synergy between tradition and
innovation, bridging the power of AI to the needs of CTM
study and illustrating how AI can unlock the potential of
CTM while safeguarding its legacy for future generations.
Zhiyao Liang is an Assistant Professor at the School of Computer Science and Engineering, Macau University of Science and Technology, in Macau SAR. Dr. Liang obtained his Master’s and Ph.D. degrees in Computer Science in the USA from The University of Texas at Austin and the University of Houston. He is involved in research topics on AI and language-related computations.
Speakers in 2026 to be announced soon......
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