جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Evolutionary Algorithms
• Automatic Machine Learning
• +۴ مورد دیگر

Shengcai Liu is an Assistant Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). Holding a PhD in Engineering, he has established himself as a researcher with over 10 publications as first or corresponding author in top artificial intelligence conferences and journals. His academic journey includes research positions at Singapore's Agency for Science, Technology and Research (A*STAR) before joining SUSTech's faculty. Education: PhD in Computer Science and Engineering, University of Science and Technology of China (2014-2020) Bachelor's in Computer Science and Engineering, University of Science and Technology of China (2010-2014) Dr. Liu's research centers on automatic algorithm design and evolutionary approaches to machine learning. He investigates how AI systems can autonomously construct efficient algorithms for complex optimization problems while maintaining robustness and reliability. His work bridges theoretical foundations with practical applications in combinatorial optimization, adversarial machine learning, and large language model development, with particular focus on making algorithm design processes more systematic and effective. His publication record demonstrates a clear trajectory from foundational work on algorithm portfolios to sophisticated integration of evolutionary methods with large language models. Recent work shows increasing focus on textual adversarial attacks, combinatorial optimization, and leveraging large language models as optimization tools themselves. This progression reflects both theoretical depth and practical relevance to current AI challenges. Professional Activities: Project Leader for Huawei-funded research on combinatorial optimization solvers (CNY 400,000) Member of IEEE, IEEE CIS, and AAAI Reviewer for top journals including TPAMI, TEVC, TCYB Conference reviewer for NeurIPS, ICML, AAAI, IJCAI Dr. Liu maintains active research collaborations through the Artificial Intelligence RAMS Technology Innovation Laboratory at SUSTech, where his team focuses on developing next-generation automatic machine learning systems with applications in optimization and security domains.

Xuyang Wuمشاهده پروفایل

دانشیار

• Distributed Optimization
• Large-scale Optimization
• Machine Learning
• +۳ مورد دیگر

Xuyang Wu serves as Associate Professor at the School of Automation and Intelligent Manufacturing (AiM), Southern University of Science and Technology (SUSTech) in Shenzhen, China. He was promoted from Assistant Professor in June 2025 after joining SUSTech in February 2024, following postdoctoral research at KTH Royal Institute of Technology (2020-2023) and a visiting position at The Chinese University of Hong Kong (2023-2024). His educational background includes: Ph.D. in Communication and Information Systems, University of Chinese Academy of Sciences (2015-2020) B.S. in Applied Mathematics, Northwestern Polytechnical University (2011-2015) Wu's research centers on distributed and large-scale optimization with applications in machine learning and networked systems. His work develops asynchronous algorithms that handle communication delays and coupling constraints, bridging control theory with modern machine learning. Key contributions include barrier function methods for safe resource allocation and delay-agnostic coordinate updates, emphasizing convergence guarantees in decentralized settings. Analysis of his 15 most recent publications (2021-2025) reveals consistent focus on asynchronous distributed optimization across top venues including IEEE Transactions on Automatic Control and ICML. His work demonstrates increasing sophistication in handling non-convex objectives and time-varying networks while maintaining theoretical convergence guarantees, with recent expansion into high-performance decentralized training frameworks. Scientific recognition includes: Best Student Paper Finalist at IEEE International Conference on Control & Automation (2019) While specific advised students and grant details aren't documented in available sources, Wu's publication record indicates active collaboration with researchers at KTH Royal Institute of Technology and The Chinese University of Hong Kong. His work shows clear progression from theoretical foundations toward practical distributed learning applications. Current research focuses on enabling violation-free optimization under constraints and developing communication-efficient decentralized training frameworks for real-world deployment scenarios.

Derong Liuمشاهده پروفایل

استاد

• Adaptive Dynamic Programming
• Reinforcement Learning
• Intelligent Control
• +۵ مورد دیگر

Derong Liu is a Chair Professor at Southern University of Science and Technology (SUSTech) in Shenzhen, China, holding dual appointments as Full Professor of Electrical and Computer Engineering and Computer Science at the University of Illinois at Chicago. He is a distinguished scholar with multiple prestigious recognitions including Member of Academia Europaea, Fellow of IEEE, Fellow of International Neural Network Society, and Fellow of International Association for Pattern Recognition. His academic journey spans multiple institutions across China and the United States, with significant contributions to control theory and artificial intelligence. Ph.D. in Electrical Engineering from University of Notre Dame (1994) M.Sc. in Automatic Control Theory from Chinese Academy of Sciences (1987) B.Sc. in Mechanical Engineering from East China Institute of Technology (1982) Liu's research focuses on Adaptive Dynamic Programming and Reinforcement Learning, Intelligent Control and Information Processing, Modeling and Control of Complex Industrial Processes, Neural Networks and Computational Intelligence, and Smart Grid technologies. His work bridges theoretical foundations with practical applications in industrial control systems, particularly in automotive engine control and energy management. He has pioneered significant methodologies in neural network design and adaptive control systems that have become foundational in the field. His publication record demonstrates a consistent trajectory of high-impact research, with recent articles focusing on event-triggered control systems, neural architecture search, fault tolerant control, and multi-agent game theory applications. The research spans both theoretical advances in control algorithms and practical implementations in complex industrial systems, showing a clear evolution from foundational neural network research to sophisticated adaptive control frameworks. Member, Academia Europaea (2021) IEEE Computational Intelligence Society Neural Network Pioneer Award (2022) Dennis Gabor Award from International Neural Network Society (2018) Highly Cited Researcher by Clarivate (2017-present) Editor-in-Chief of Artificial Intelligence Review (2014-present) Liu has mentored numerous students and researchers throughout his career, serving as Editor-in-Chief for major journals and leading significant research initiatives. His work has been supported by multiple grants from the National Science Foundation of the United States and the National Natural Science Foundation of China. His research group continues to push boundaries in adaptive control systems and neural network applications. Currently, Liu leads a research group at SUSTech focusing on intelligent control systems, with active projects in adaptive dynamic programming, reinforcement learning applications, and smart grid technologies. His laboratory serves as a hub for interdisciplinary research connecting theoretical control frameworks with practical industrial implementations.