2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE 2024)




Professor CHEN Minghua

IEEE Fellow

ACM Distinguished Scientist

City University of Hong Kong, China


Minghua received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He is now a Professor at School of Data Science, City University of Hong Kong. Minghua received the Eli Jury award from UC Berkeley in 2007 (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and The Chinese University of Hong Kong Young Researcher Award in 2013. He also received several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, IEEE INFOCOM Best Poster Award in 2021, and ACM e-Energy in 2023. Coding primitives co-invented by Minghua have been incorporated into Microsoft Windows and Azure Cloud Storage, serving hundreds of millions of users. He currently serves as Award Chair of ACM SIGEnergy and Senior Editor of IEEE Systems Journal. His recent research interests include online optimization and algorithms, machine learning in power system operation, intelligent transportation, distributed optimization, and delay-critical networking, and capitalizing the benefit of data-driven prediction in algorithm/system design. He is an ACM Distinguished Scientist and an IEEE Fellow.

Title: Synthesizing Distributed Algorithms for Combinatorial Network Optimization

Abstract:  Many important network design problems are fundamentally combinatorial optimization problems. A large number of such problems, however, cannot readily be tackled by distributed algorithms. We develop a Markov approximation technique for synthesizing distributed algorithms for network combinatorial problems with near-optimal performance. We show that when using the log-sum-exp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of time-reversible Markov chains. Selected Markov chains among this class, or their carefully-perturbed versions, yield distributed algorithms that solve the log-sum-exp approximated problem. The Markov Approximation technique allows one to leverage the rich theories of Markov chains to design distributed schemes with performance guarantees. By case studies, we illustrate that it not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in other problem domains with provable performance, including cloud computing, edge computing, and IoT scheduling.


Professor Jian Huang

Huazhong University of Science and Technology, China


Huang Jian, a full professor at Huazhong University of Science and Technology, serves as the head of the Department of Intelligent Science and Technology in the School of Artificial Intelligence and Automation, and the director of the Hubei Key Laboratory of Brain-inspired Intelligent Systems. He has been selected as a leading talent in scientific and technological innovation under the National Special Support Program for High-Level Talents and supported by the Ministry of Education's "New Century Excellent Talent Support Plan," receiving funding from the Hubei Provincial Natural Science Foundation's Distinguished Young Scholars Program. He holds positions such as the chairman of the IEEE Computational Intelligence Society Wuhan Chapter, vice-chairman of the Wuhan Automation Society, and vice-chairman of the Intelligent Robotics Professional Committee of the China Artificial Intelligence Society. He has previously served as a visiting professor at Nagoya University in Japan, a visiting professor at Université Paris-Est-Créteil Val de Marne in France, and a JSPS Fellow funded by the Japan Society for the Promotion of Science.

Professor Huang has led over twenty significant national and provincial-level research projects, including key projects of the National Key Research and Development Program of China, National Natural Science Foundation of China, International Cooperation Key Projects of the Ministry of Science and Technology, and Hubei Province Major Technology Innovation Projects. He has published over 120 SCI-indexed journal articles and has been cited more than 6,000 times on Google Scholar. He holds over 30 national invention patents, one U.S. invention patent, and one Japanese invention patent. His research achievements have been awarded one provincial-level science and technology grand prize (first place), three first-class awards, and one gold and one silver medal at the Geneva International Exhibition of Inventions.

Professor Huang also serves as an editorial board member for internationally renowned journals such as IEEE Transactions on Fuzzy Systems and IEEE Transactions on Automation Science and Engineering.

Title: Human-vision-driven upper limb assistive robotic system

Abstract: Gaze-based Human-Robot Interaction offers a novel way for individuals with disabilities to independently perform daily activities. However, most current gaze-based upper limb assistance systems still face challenges such as poor signal quality, unnatural intention recognition, and limited grasping capabilities. To address the issue of gaze signals being affected by noise and outliers, we proposed an outlier-robust gaze signal set-membership filtering algorithm. In tackling the problem of grasp intention recognition from gaze signals, we designed various intention recognition features. Additionally, to solve the issue of insufficient integration of gaze, environmental, and temporal information during grasp intention recognition, we developed a deep neural network for end-to-end recognition and optimization of grasp intention. Ultimately, we designed a vision-driven upper limb assistive robotic system with two auxiliary modes. Patient experiments have demonstrated the effectiveness of the designed system.


Professor Xiangjian (Sean) He

University of Nottingham Ningbo, China


Professor Xiangjian (Sean) He is a National Talent of China with a Chair Professor title, and in list of the 'World Top 2% Scientists' reported by Stanford University in 2022, 2023, etc. He is currently the Deputy Head of Computer Science School, the Lead of Research Groups of the Faculty of Science and Engineering and the Director of Computer Vision and Intelligent Perception Laboratory at the University of Nottingham, Ningbo, China (UNNC). 

He was the Professor of Computer Science and the Leader of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC) at the University of Technology Sydney (UTS) from 2011-2022.  He led the UTS and Hong Kong Polytechnic University (PolyU) joint research project teams winning the 1st Runner-Up prize for the 2017 VIP Cup, and the champion for the 2019 VIP Cup, awarded by IEEE Signal Processing Society. 

He has been carrying out research mainly in the areas of computer vision, data analytics and machine learning in the previous years. He has recently been leading his research teams for deep-learning-based research for various applications. He has played various chair roles in many international conferences such as ACM MM, MMM, ICDAR, IEEE BigDataSE, IEEE BigDataService, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE ICPR and IEEE ICARCV.

Title: Human Density Estimation

Abstract: Crowd counting, i.e., estimating the number of people in crowded areas, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast density variations and severe occlusion within the interested crowd area. In this talk, novel deep-learning models for crowd counting are presented. The networks extract features of different scales while focusing on the relevant information and suppressing the misleading information. We address the variation of crowdedness levels among different images with a Density-Aware decoder. We generate an overall density map by considering the summation of low and high crowdedness density maps. 


Professor Rui Fan

IEEE Senior Member

Tongji University, China


Rui Fan received the B.Eng. degree in Automation from the Harbin Institute of Technology in 2015 and the Ph.D. degree (supervisors: Prof. John G. Rarity and Prof. Naim Dahnoun) in Electrical and Electronic Engineering from the University of Bristol in 2018. He worked as a Research Associate (supervisor: Prof. Ming Liu) at the Hong Kong University of Science and Technology from 2018 to 2020 and a Postdoctoral Scholar-Employee (supervisors: Prof. Linda M. Zangwill and Prof. David J. Kriegman) at the University of California San Diego between 2020 and 2021. Rui began his faculty career as a Full Research Professor with the College of Electronics & Information Engineering at Tongji University in 2021, and was then promoted to a Full Professor in the same college, as well as at the Shanghai Research Institute for Intelligent Autonomous Systems in 2022. Rui served as an associate editor of ICRA'23 and IROS'23, and as a senior program committee member of AAAI'23/24. Rui is the general chair of the AVVision community and organized several impactful workshops and special sessions in conjunction with WACV'21, ICIP'21/22/23, ICCV'21, and ECCV'22. Rui was named in the Stanford University List of Top 2% Scientists Worldwide in 2022 and 2023, as well as in the Forbes China List of 100 Outstanding Overseas Returnees in 2023. Rui's research interests include computer vision, deep learning, and robotics. 

Title: Computer Vision and Machine Learning for Intelligent Road Inspection

Abstract: Computer vision and machine learning techniques have been extensively used for intelligent road inspection, including road surface 3D reconstruction and road defect detection. In this talk, I will first discuss several general-purpose algorithms for 3D driving perception, such as stereo matching, surface normal estimation, and data-driven free space detection. Afterward, I would like to introduce our recent achievements in applying these techniques to solve intelligent road inspection problems, particularly focusing on stereo vision-based road 3D imaging and road pothole detection. 


Associate Professor

Ata Jahangir Moshayedi

IEEE Senior Member

Jiangxi University of Science and Technology, China


Dr. Ata Jahangir Moshayedi is an  Associate Professor at Jiangxi University of Science and Technology, China, holding a PhD in Electronic Science from Savitribai Phule Pune University, India. With a distinguished career spanning academia and research, Dr. Moshayedi is recognized as an IEEE Senior Member and holds esteemed memberships including the Instrument Society of India as a Life Member and Lifetime Member of the Speed Society of India.

Dr. Moshayedi's expertise extends beyond membership, as he actively contributes to the advancement of knowledge in his field. He serves on the editorial boards of several esteemed conferences and journals, including Biomimetic Intelligence and Robotics (BIRob), EAI Endorsed Transactions on AI and Robotics, International Journal of Robotics and Control, JSME, Bulletin of Electrical Engineering and Informatics, and International Journal of Physics and Robotics Applied Electronics.

Throughout his illustrious career, Dr. Moshayedi has made significant scholarly contributions, with more than 80 papers published in national journals and conferences. Additionally, he has authored three books, further solidifying his reputation as a thought leader in his field. His innovative spirit is reflected in his ownership of two patents and nine copyrights, demonstrating his commitment to pushing the boundaries of knowledge and technology.

Dr. Moshayedi's research interests are diverse and impactful, focusing on Robotics and Automation, Sensor Modeling, Bio-inspired Robots, Mobile Robot Olfaction, Plume Tracking, Embedded Systems, Machine Vision-based Systems, Virtual Reality, and Artificial Intelligence. His multidisciplinary approach and dedication to excellence continue to inspire colleagues and students alike, shaping the future of robotics and technology.

Title: Food Delivery Robot as the Service Robot Evolution, Efficiency, and Performance Enhancement

Abstract: Service robots boost efficiency and safety by automating tasks, offering 24/7 availability, and adapting to diverse environments. They complement human workers, reduce costs, and enhance customer experiences, while also addressing societal needs such as accessibility and healthcare support. This keynote addresses the evolution and improvement of the service robot as a food delivery robot. 

In this talk, the service of robots will be examined first, then their components and types will be discussed. After that, an example designed for the food delivery mission is discussed from modelling design to the implementation of various optimization methods. This research examines the operational efficiency of designed food delivery robot (FOODIEBOT) in different ways, using optimization methods such as BAS, PSO, POA and EO. he does. and shows the efficiency of each one against the routes. This talk provides comprehensive insights into the design elements of designed food delivery robot (FOODIEBOT), elucidating the profound impact of optimization methods on its performance and highlighting their practical implications for automated service development.


Assistant Professor

Chaochao Chen

Zhejiang University, China


Chaochao Chen is currently an Assistant Professor at the College of Computer Science and Technology, Zhejiang University. Before that, He was a Staff Algorithm Engineer at Ant Group. He was also a visiting scholar at the Computer Science Department of UIUC. His research mainly focuses on trustworthy machine learning, privacy-preserving data mining, and recommender systems. He has published more than 80 papers in peer reviewed journals and conferences. He received the Best Paper Award of The 1st International Workshop on FedGraph and the Best paper Runner-up Award of CIKM 2022.  He has also applied for more than 200 International and Chinese patents, and more than 100 of them have been issued.

Title: Cross Domain Recommendation: Directions and Techniques

Abstract: Recommender systems aim to provide users with personalized information filtering services, which inevitably requires kinds of user data. In practice, users alway participant in different domains (systems) for various purposes. Thus, how to simultaneously leverage the data from multiple domains to improve the recommendation performance, i.e., achieving cross-domain recommendation (CDR), is a hot research topic. This report will talk about the overview of CDR, typical approaches of CDR, and privacy-preserving CDR.