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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.

• Artificial Intelligence
• Evolutionary Algorithms
• Automatic Machine Learning
• +4 more

Yuhui Hu is an Associate Professor and Doctoral Supervisor in the Department of Pharmacology, School of Medicine at Southern University of Science and Technology (SUSTech) in Shenzhen, China. She joined SUSTech in August 2016 as an independent PI and was promoted to Associate Professor in the Department of Pharmacology in March 2022, having previously served as a Research Associate Professor in the Department of Biology, School of Life Sciences at SUSTech from 2016-2022. Dr. Hu earned her Ph.D. in Functional Genomics from the Max Planck Institute for Molecular Genetics in Berlin, Germany in 2006. Prior to joining SUSTech, she led her own research group on pharmacogenomics and chemical genetics at the same institute from 2007-2009, then moved to the Berlin Institute for Medical Systems Biology (BIMSB) at the Max Delbrück Center for Molecular Medicine where she served as a Senior Research Scientist until 2016. Her research focuses on integrating diverse systems biology approaches in transcriptomics, genomics/epigenomics, proteomics, CRISPR gene editing, single-cell and spatiotemporal omics to decipher gene regulations in cancer precision medicine and developmental biology. She has pioneered high-throughput platforms for protein subcellular localization, apoptosis detection, and protein-protein interaction analysis. Her work bridges traditional Chinese medicine compounds with modern systems biology approaches to develop new drugs, particularly through the investigation of natural compounds for anticancer and anti-COVID applications. Her recent publications show a strong focus on cutting-edge single-cell and spatial transcriptomics technologies, with multiple high-impact papers in journals like Cell, Nature Communications, and Developmental Cell. Her research group has developed several innovative protocols for sample multiplexing and cell death quantification compatible with single-cell sequencing. Excellent Patent Award, Southern University of Science and Technology (2020) Industrial Development and Innovative Talent Award, Shenzhen (2021) Shenzhen Peacock Talent Award As a Doctoral Supervisor, Dr. Hu leads a research group that has secured significant funding, including six national/provincial projects totaling over ¥7.14 million. She serves as a core PI in the Shenzhen Key Laboratory of Gene Regulation and Systems Biology and holds several academic society positions, including committee member of the division of anti-inflammatory and immunological pharmacology of the Chinese Pharmacological Society, council member of Guangdong Pharmacological Society, and executive director of Guangdong Overseas Returnees Association. Her lab continues to develop novel systems biology approaches for gene regulation and pharmacology research, with a particular focus on cancer genomics and chemical genomics for early diagnosis, therapy, and prognosis.

• Functional Genomics
• Systems Biology
• Pharmacogenomics
• +11 more

Dan HUANG is an Assistant Professor in the Department of Basic Immunology and Microbiology at the School of Life Sciences, Southern University of Science and Technology. He obtained his Ph.D. in Chemical Pathology from The Chinese University of Hong Kong (2014-2018) and a Bachelor's degree in Bioinformatics from Harbin Medical University (2008-2013). His professional journey includes postdoctoral research at SUSTech (2021-2023) and The Chinese University of Hong Kong (2019-2020), plus a Chief Scientist role at Cytomics Limited (2020-2021). Research Interests: Genomic Evolution Tumor Metabolism Transcriptional Regulatory Mechanisms Publication Trends (2020-2023): His work focuses on leveraging multi-omics data to uncover mechanisms in cancer resistance, including RNA methylation (m6A), microbiome interactions, and evolutionary adaptations of tumor suppressor genes. Key themes include immunotherapy optimization, epigenetic regulation, and cross-disciplinary applications of bioinformatics to clinical and environmental contexts. Academic Background: Ph.D., Chemical Pathology, The Chinese University of Hong Kong (2014-2018) Bachelor's, Bioinformatics, Harbin Medical University (2008-2013)

• Genomic Evolution
• Tumor Metabolism
• Transcriptional Regulatory Mechanisms
• +3 more

Xiyun Jiao is an Assistant Professor at the Department of Statistics and Data Science, Southern University of Science and Technology (SUSTech). She earned her Ph.D. in Statistics from Imperial College London (2016) and held a postdoctoral research fellowship at University College London (2017–2020). Education : Ph.D. (Imperial College London), Master’s (KU Leuven), Bachelor’s (Tsinghua University) Research Interests : Computational statistics, Bayesian statistics, Markov chain Monte Carlo algorithms, and statistical methods in population genetics Her publications focus on integrating Bayesian frameworks with computational techniques for phylogenetics, cosmology, and statistical modeling. No scientific awards or student advisees are listed in the provided text.

• Computational Statistics
• Bayesian Statistics
• Markov Chain Monte Carlo Algorithms
• +1 more

Hao Wang is an Assistant Researcher in the Department of Computer Science and Technology at Nanjing University, China. He is affiliated with the Reasoning & Learning Group and conducts research at the intersection of data management and machine learning. Bachelor of Mathematics, Nanjing University (2005) Master of Computer Science, Nanjing University (2008) Ph.D. in Computer Science, The University of Hong Kong (2014) His research interests span Data Management and Machine Learning , with recent focus on rank-aware query processing, recommender systems (especially location-based), reinforcement learning, and transfer learning. His work often integrates user behavior modeling, efficient indexing, and scalable algorithms for large-scale data. Hao Wang's publication record shows a consistent trend in solving practical problems in data-intensive AI systems. His recent articles focus on personalized location recommendation , crowdsourced data labeling , distributed learning for imbalanced data , and reinforcement learning transfer . These works appear in top venues such as VLDB, SIGMOD, ICDM, AAAI, and journals like TKDE and GeoInformatica, indicating strong technical depth and interdisciplinary impact. Hao Wang has not been explicitly mentioned as receiving scientific awards in the provided text. He has collaborated extensively with researchers such as Yang Gao, Nikos Mamoulis, and David Cheung. While no formal advisees are listed, his involvement in supervising PhD work (as co-supervisor) and publishing with students suggests an active mentoring role. His research has been supported through academic collaborations and institutional affiliations, though specific grant details are not provided. Hao Wang is a member of the Reasoning & Learning Group at Nanjing University, where he contributes to advancing intelligent systems that combine logical reasoning with statistical learning. The group focuses on foundational and applied aspects of AI, particularly in data mining, knowledge discovery, and autonomous decision-making.

• Data Management
• Machine Learning
• Recommender Systems
• +7 more

CHENG Qingsha is an Associate Professor at the Department of Electronic and Electrical Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China. He also holds an Adjunct Professorship at the University of Regina, Canada. With over 26 years of experience, he is a globally recognized expert in surrogate modeling, space mapping, and microwave device optimization. Education: Ph.D. in Electrical and Computer Engineering, McMaster University, Canada (2004) M.Eng. in Automation, Chongqing University, China (1998) B.Eng. in Automation, Chongqing University, China (1995) Research Interests: Dr. Cheng's research spans a wide range of topics in electromagnetic engineering and computational design. His primary focus includes surrogate modeling , space mapping optimization , rapid prototyping , and 3D printing for microwave and antenna applications. He has made foundational contributions to the Space Mapping algorithm , which has become a cornerstone in microwave CAD. His work also extends to AI-assisted optimization , metasurface antennas , millimeter-wave systems , and electromagnetic compatibility . Scientific Awards & Honors: IEEE Senior Member Member of Professional Engineers Ontario Associate Editor, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields Co-organizer and Chair of multiple IEEE workshops and symposiums Teaching & Mentorship: Dr. Cheng has taught a broad range of courses including Signals and Systems , Advanced Microwave Engineering , Antenna Theory and Techniques , and Engineering Ethics . He has supervised over 30 graduate students and postdocs, many of whom have co-supervised with international collaborators from HKUST, NUS, CityU, University of Birmingham, and University of Regina. Lab & Team: He leads the Cheng Lab at SUSTech, which focuses on surrogate-based modeling and optimization for microwave and antenna systems. The lab is actively recruiting postdocs, PhD students, and research engineers.

• Surrogate Modeling
• Space Mapping
• Microwave Device Optimization
• +3 more

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.

• Computational Intelligence
• Evolutionary Multi-objective Optimization
• Evolutionary Machine Learning
• +5 more

Dr. LIU Quanying is an Associate Professor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech), where she has been a faculty member since September 2019. She serves as the Principal Investigator of the Neural Computing and Control Laboratory (NCC lab) and is a doctoral supervisor. Prior to joining SUSTech, she earned her PhD in Biomedical Engineering from ETH Zurich and conducted postdoctoral research at Caltech. Education: PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Research Interests: Dr. Liu’s research integrates neuroscience, machine learning, and control theory. Her work focuses on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for neuroscience, and optimization techniques for neuromodulation (tES, TMS). She has developed high-density EEG source localization algorithms and data-driven brain network modeling frameworks, aiming to enhance precision in neural stimulation and control. Scientific Awards: The New Brain 30 (2023) AAIC Travel Award (2019) Estes Stars Award (2018) 深圳市孔雀人才计划C类 Laboratory and Team: As the PI of the NCC lab, Dr. Liu leads a team focused on machine learning algorithms, neurocomputational modeling, and neurofeedback control. The lab actively recruits graduate students, postdocs, and visiting researchers, emphasizing interdisciplinary collaboration in neuroscience and AI.

• Neuroscience
• Machine Learning
• Control Theory
• +4 more

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.

• Adaptive Dynamic Programming
• Reinforcement Learning
• Intelligent Control
• +5 more

Dr. Wang Zhenkun is an Assistant Professor at the School of System Design and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), with a joint appointment in the Department of Computer Science and Engineering. He earned his PhD in Circuits and Systems from Xidian University in 2016 and held postdoctoral and research fellow positions at Nanyang Technological University (NTU), City University of Hong Kong, and its Shenzhen Research Institute. Research Interests: Multiobjective optimization, artificial intelligence-assisted design automation, supply chain management, deep learning, and UAV traffic scheduling. Grants & Recognition: Principal Investigator (PI) for a National Natural Science Foundation of China (NSFC) youth project and multiple grants; selected as a high-level C-category overseas talent in Shenzhen. Leadership Roles: Associate Editor of Swarm and Evolutionary Computation (JCR Q1); Chair of IEEE CIS Shenzhen Chapter Student Activities; Organizer of EMO 2021 Competition. Teaching: Courses include Fundamentals of Systems Engineering , Machine Learning System Design , and Intelligent Optimization Algorithms . Group Leadership: Founder of the Computational Intelligence and Advanced Manufacturing (CIAM) group, focusing on developing AI-driven optimization algorithms for logistics, manufacturing, and aerospace applications.

• Multiobjective Optimization
• Supply Chain Management
• Artificial Intelligence Assisted Optimal Design
• +13 more