Zhilin Guo is an Associate Professor at the School of Environmental Science and Engineering of the Southern University of Science and Technology (SUSTech), Shenzhen. She earned her Ph.D. in Environmental Science (minor in Hydrology) from the University of Arizona and completed postdoctoral work at UC Davis. Research Leadership : Vice Chair of UNESCO Groundwater Youth Network, member of AGU and IAHR committees, and associate editor for top journals like Water Resources Research . Research Focus : Groundwater pollution mechanisms, non-Fickian transport modeling, contamination risk assessment, and sustainability under global change. Her work spans numerical simulation, upscaling methods, and reactive transport analysis. Scientific Awards : 2024 Ministry of Education Young Talents, 2024 Guangdong Environmental Science Society Gold Award, and multiple journal-specific honors. Her funded projects include national Key R&D programs and NSFC grants, totaling over 10 million RMB. Academic Contributions : Published 15+ papers in 2024 alone, covering groundwater modeling, pollution risk, and climate-water interactions. Her teaching includes bilingual courses on water resources and contaminant transport.
Dr. Han Feng is a Research Associate Professor at the School of Environmental Science and Engineering, Southern University of Science and Technology, collaborating with Professor Yi Zheng. He has been with the institution since 2015, progressing from Post-doctoral Research Fellow (2015-2018) to Research Assistant Professor (2018-2021), and currently holds his present position since 2021. His educational background includes a Ph.D. in Mechanics (Energy and Resources Engineering) from Peking University (2008-2015) and a Bachelor's degree in Theoretical and Applied Mechanics from the same institution (2004-2008). Dr. Han specializes in eco-hydrological and water environmental simulation, with research focusing on numerical simulation of hydrological processes, ecological processes, and water quality processes at watershed scale . His work encompasses development of new computational models, exploration of efficient model-data fusion methods, and investigation into coupling paradigms of deep learning and process-based models. His expertise spans eco-hydrological modeling, non-point source pollution simulation, data assimilation, uncertainty analysis, and machine learning applications in environmental systems. His publication record demonstrates significant contributions to the field, with over 40 papers including 38 SCI-indexed articles in top journals such as Water Research, Water Resources Research, and Journal of Hydrology. His recent work shows a clear trend toward integrating artificial intelligence with traditional hydrological modeling, particularly in applying physics-encoded deep learning to distributed hydrological modeling and using Bayesian approaches for assimilating sensor data in water quality modeling. National Natural Science Foundation of China青年科学基金 project (41807164) as PI (2019-2021) NSFC-CONICYT International Cooperation project (5191101522) as main participant (2020-2024) Chinese Academy of Sciences Strategic Priority Research Program (XDA20100104) as main participant (2018-2023) Shenzhen Municipal Engineering Lab of Environmental IoT Technologies as key member (2018-2020) China Postdoctoral Science Foundation project (2017M612505) as PI (2017-2018) As one of the main developers of the three-dimensional distributed eco-hydrological model HEIFLOW (Hydrological-Ecological Integrated watershed-scale FLOW), Dr. Han's research has been applied across diverse geographical contexts including the Heihe River Basin, Pearl River Basin, Luanhe River Basin, Miho Basin (South Korea), Skjern Basin (Denmark), and San Joaquin Basin (United States).
Professor Hisao Ishibuchi is Chair Professor of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China, a role he has held since April 2017. Previously, he spent nearly three decades at Osaka Prefecture University, progressing from Research Associate (1987-1993) to Assistant Professor (1993), Associate Professor (1994-1999), and full Professor (1999-2017). He is an IEEE Fellow , served as Vice-President of the IEEE Computational Intelligence Society (2010-2013) , and is currently President of the Japan Society for Evolutionary Computation (2016-2018) . He is Editor-in-Chief of IEEE Computational Intelligence Magazine (2014-2019) and the Journal of the Japan EC Society (2014-2018). Education: Ph.D. in Engineering, Osaka Prefecture University, 1992 M.S. in Engineering, Kyoto University, 1987 B.S. in Engineering, Kyoto University, 1985 Research Focus: Professor Ishibuchi is internationally recognised as a pioneer of computational intelligence , with seminal contributions to evolutionary multi-objective optimisation , evolutionary machine learning , fuzzy systems , neural networks , and hybrid intelligent systems . He introduced the first multi-objective memetic algorithm and early methods for multi-objective fuzzy rule-based classifier design that balance accuracy and interpretability. Publications & Impact: With over 100 journal papers in top-tier venues such as IEEE Transactions on Evolutionary Computation and nearly 500 conference papers, his work has attracted more than 24 000 Google-Scholar citations and an h-index of 68. His recent articles concentrate on many-objective optimisation, fuzzy machine learning, and transfer learning techniques. Honours & Awards: IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award 2019 IEEE Fellow 2014 JSPS Prize 2007 (Japan’s most prestigious mid-career award) Multiple Best Paper Awards from GECCO, FUZZ-IEEE, SCIS & ISIS, WAC, ACIIDS, HIS-NCEI, and others Teaching & Mentoring: At SUSTech he teaches Advanced Algorithms and Advanced Optimization Algorithms , covering greedy algorithms, hyper-heuristics, memetic algorithms, multi-objective optimisation, and performance assessment. His research group actively recruits post-doctoral fellows and research assistants in evolutionary computation, fuzzy systems, and neural networks. Labs & Teams: He leads the Computational Intelligence Research Group at SUSTech, maintaining active collaboration networks across Asia, Europe, and North America, and supervising several post-doctoral researchers and graduate students working on next-generation intelligent systems.
Jiang Xuejun is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). He has been with SUSTech since 2013, initially as a Tenure-Track Assistant Professor and promoted to Associate Professor in 2019. Prior to joining SUSTech, he served at Zhongnan University of Economics and Law. His educational background includes: Ph.D. in Statistics from The Chinese University of Hong Kong (2009) M.Sc. from Yunnan University B.Sc. from National University of Defense Technology Jiang Xuejun's research focuses on advanced statistical methodologies with applications in various domains. His work spans statistical theory development and practical applications in finance, economics, and risk assessment. He has made significant contributions to quantile regression, variable selection, survival analysis, and nonparametric regression methods. His research publications demonstrate a strong trend toward developing innovative statistical methods for high-dimensional data analysis, Bayesian modeling approaches, and applications in financial econometrics and disaster risk assessment. Many of his recent papers focus on quantile regression techniques, dimension reduction methods, and robust statistical testing procedures. Jiang Xuejun has received several prestigious awards: Shenzhen Outstanding Teacher (2018) Southern University of Science and Technology "Outstanding Teaching Award" (2018) "Excellent Mentor Award" from Southern University of Science and Technology (2016) Selected for Shenzhen's "Peacock Plan" for overseas high-level talents He has successfully secured multiple research grants as Principal Investigator, including projects funded by the National Natural Science Foundation of China (both General and Youth programs), Guangdong Provincial Natural Science Foundation, and Shenzhen Science and Technology Innovation Commission. His research portfolio includes work on likelihood inference for high-dimensional models, statistical methods for epidemic disease control, and quantitative trading systems using machine learning. Jiang maintains an active research group focusing on statistical methodology development and applications, with particular emphasis on financial statistics and econometrics. His team collaborates with researchers across multiple disciplines to address complex data analysis challenges in economics, finance, and public health.
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.
Dong Xiaojing is a Tenured Associate Professor at Santa Clara University, USA, specializing in Bayesian statistical methodologies and their practical implementations. She was invited to deliver a lecture series at Tsinghua University's School of Economics and Management, Department of Marketing, in June 2025. Her research centers on advanced statistical modeling with emphasis on Bayesian inference systems, probabilistic programming, and cross-disciplinary applications. She explores methodological innovations in uncertainty quantification and decision-theoretic frameworks, contributing to both theoretical statistics and real-world problem solving across scientific domains. As an active academic presenter, she engages with international research communities through specialized workshops and university collaborations, demonstrating commitment to advancing statistical education and methodology dissemination.
