Li Xueliang is an assistant professor and associate researcher at the Pengcheng Peacock Distinguished Post.
Email：lixueliang@szu.edu.cn
He is an Executive Member of the Architecture Committee and a Member of the High-Performance Computing Committee of the China Computer Federation (CCF), and a member of IEEE. He has led multiple research projects funded by the National Natural Science Foundation of China (NSFC), the Ministry of Education, and the Guangdong Provincial Natural Science Foundation. As a key member, he participated in large-scale EU computer system energy optimization projects such as ENTRA–Whole-Systems Energy Transparency and Coordination and Support Action ICT-Energy, and also participated in several NSFC general and joint projects. He is a key member of the Shenzhen Nobel Prize/Turing Award Laboratory, the "Sfakis Trusted Autonomous Systems Research Institute."
In 2022, he was shortlisted for the German Federal Ministry of Education and Research's International Cross-Disciplinary Innovation Award for his outstanding contributions to sustainable computing. His research findings have been published in top international conferences such as HPCA, MICRO, and ISSTA, as well as internationally renowned journals such as IEEE TODAES. He serves as a reviewer for top journals and conferences such as AAAI and TPDC.
My main research areas are:
1. Artificial Intelligence and Artificial Empathy
Artificial intelligence is a discipline that studies how to enable computers to simulate human intelligent behavior. It encompasses multiple subfields, such as machine learning, natural language processing, and computer vision. By leveraging big data and powerful computing capabilities, AI can extract patterns and rules from complex data and use algorithms for reasoning and decision-making. It has already demonstrated enormous potential in numerous fields, including healthcare, finance, transportation, and social media. AI has a wide range of applications, such as self-driving cars, intelligent assistants, and smart homes, providing not only convenience and efficiency but also changing our lifestyles. In the future, AI is expected to further drive technological and social development, bringing us more innovation and change.
Artificial empathy is a relatively new field that aims to enable computers to understand, recognize, and simulate human emotions and feelings. Human emotions are an important part of our interaction and communication with the world; therefore, enabling computers to understand and respond to emotions can enhance the human-computer interaction experience. Research on artificial empathy includes emotion recognition, emotion generation, and emotion expression. By utilizing various sensors, pattern recognition, and machine learning technologies, artificial empathy can capture human emotional states and provide personalized services and feedback based on those emotions. This field has broad application potential, encompassing areas such as intelligent customer service, mental health assistance, and virtual reality. In the future, artificial empathy is expected to help us better understand and process emotions, creating more intelligent, caring, and personalized technological experiences for humanity.
2. User-Centered Architecture
User-centered computer architecture is a design approach that focuses on user needs and experiences, aiming to create computer systems that meet user expectations and needs. This approach uses user contexts, behaviors, and preferences as design criteria to ensure that the system provides an efficient, reliable, and satisfying user experience. Through user-centered computer architecture, we can create more user-friendly, high-performance, and reliable computer systems, providing an excellent user experience. This design approach prioritizes user needs, thereby meeting user expectations and driving technological progress.
3. Social Computing, Sustainable Computing
Social computing aims to explore and understand human social behavior and interactions through the intersection of computer science and social sciences. Social computing combines computer science techniques and methods with social science theories and observations to study and simulate the structure, dynamics, and evolution of human social systems. It focuses on individual behavior and group interactions within social systems, utilizing technologies such as big data analytics, machine learning, and network analysis to analyze and understand patterns and trends in social behavior. It studies social phenomena such as social networks, information dissemination, social dynamics, cooperative behavior, and group decision-making to reveal the complexity and dynamism of human societies.
Sustainable computing is a computational approach and practice focused on environmental sustainability and resource efficiency. It aims to reduce the consumption of energy, water, and other natural resources, as well as the negative environmental impacts of computer science and information technology. Sustainable computing involves multiple aspects, including hardware design, software development, data center management, and computing resource utilization. In hardware design, sustainable computing encourages the development of more energy-efficient computing devices, using low-power components and energy-saving technologies to reduce energy consumption. In software development, sustainable computing encourages the optimization of algorithms and programs to reduce the use of computing and storage resources. In data center management, sustainable computing encourages the adoption of energy management and thermal management strategies to improve the energy efficiency of data centers. In terms of computing resource utilization, sustainable computing encourages the sharing and reuse of computing resources to reduce resource waste.
Please refer to my personal homepage.：https://sites.google.com/view/xueliangli
Undergraduate and graduate students who are interested in pursuing further studies in the above research areas, please contact me.
Representative works:
(1) Xueliang Li, Zhuobin Shi, Junyang Chen, Yepang Liu, Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures. In 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO’22). (Top academic conferences，CCF A，CSRankings)
(2) Xueliang Li, Shicong Hong, Junyang Chen, Guihai Yan, and Kaishun Wu. Using Psychophysics to Guide Power Adaptation for Input Methods on Mobile Architectures. In Proceedings of the 28th IEEE International Symposium on High-Performance Computer Architecture (HPCA’22).（Top academic conferences，CCF A，CSRankings）
(3) Xueliang Li, Junyang Chen, Kaishun Wu, Yepang Liu and John P. Gallagher, Combatting Energy Issues for Mobile Applications, ACM Transactions on Software Engineering and Methodology (TOSEM). (Top academic journals，CCF A)
(4) Xueliang Li, Yuming Yang, Yepang Liu, John P. Gallagher, and Kaishun Wu. Detecting and Diagnosing Energy Issues for Mobile Applications. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA’20), July 18–22, 2020, New York, NY, USA, 13 pages.（Top academic conferences，CCF A，CSRankings）
(5) Xueliang Li, Shicong Hong, Junyang Chen, Junkai Ji, Chengwen Luo, Guihai Yan, Zhibin Yu and Jianqiang Li. Satisfying Energy-Efficiency Constraints for Mobile Systems. InIEEE Transactions on Mobile Computing (TMC), Early Access. (Top academic journals，CCF A)
