جستجوی استاد راهنمای دانشگاهی

• Computational Intelligence
• Evolutionary Multi-objective Optimization
• Evolutionary Machine Learning
• +۵ مورد دیگر

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.

• Statistics in Financial Econometrics
• Quantile Regression
• Variable Selection
• +۴ مورد دیگر

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.

• Robotics
• Mobile Robotics
• Robot Manipulation
• +۶ مورد دیگر

Jia Zhenzhong is an Assistant Professor in the Department of Mechanical and Energy Engineering at Southern University of Science and Technology (SUSTech), where he has been employed since September 2019. His research focuses on robotics, control systems, and intelligent automation with applications spanning medical robotics, mobile robots for extreme environments, and advanced manufacturing. Dr. Jia received his academic training from prestigious institutions: Ph.D. in Naval Architecture and Ocean Engineering (Control Engineering) from University of Michigan (2014) Master's in Applied Mathematics from University of Michigan (2014) Master's in Mechanical Engineering from University of Michigan (2009) Master's in Mechanical and Electronic Engineering from Tsinghua University (2007) Bachelor's in Measurement and Control Technology and Instrumentation with a minor in Computer Science from Tsinghua University (2005) Dr. Jia's research spans three main interconnected domains that reflect his expertise in both theoretical foundations and practical applications of robotics and control systems. His work in robotic manipulation addresses fundamental challenges in dexterous manipulation, assembly tasks, and logistics automation. In mobile robotics , he has made significant contributions to terrain interaction modeling, particularly for extraterrestrial environments like Mars and the Moon, as well as for autonomous vehicles operating under challenging conditions. His research in intelligent control bridges traditional control theory with modern machine learning techniques, with particular focus on model predictive control, reinforcement learning, and their applications in energy systems and autonomous decision-making. His publication record demonstrates a consistent trajectory of impactful research that bridges theoretical innovation with practical engineering applications. The publications show increasing sophistication in modeling techniques, particularly in terrain-vehicle interactions and robotic manipulation. There's a clear evolution from fundamental modeling work to more complex system integration and application-specific implementations. His research consistently addresses real-world challenges in robotics, from medical applications to space exploration and industrial automation. Dr. Jia has received several notable honors throughout his career: 2007 Department Fellowship, Department of Mechanical Engineering, University of Michigan, USA 2005 Outstanding Graduate of Tsinghua University Dr. Jia has led and participated in numerous research projects funded by prestigious organizations including the Beijing Science and Technology Commission, the National Natural Science Foundation of China (NSFC), the 863 Program, the US Army/Navy, the National Science Foundation (NSF), and Foxconn Technology Group. His research portfolio demonstrates strong interdisciplinary connections between robotics, control theory, and practical engineering applications across multiple domains. He has published over 20 academic papers, obtained numerous invention patents, and translated four renowned textbooks, including "Modern Robotics." His laboratory work focuses on developing advanced robotic systems for manipulation and mobility challenges, with particular emphasis on applications requiring high precision in unstructured environments. Current research directions include improving robotic dexterity for assembly tasks, enhancing mobile robot navigation in challenging terrains, and developing more efficient control algorithms that integrate learning with traditional control frameworks.

• Neuroscience
• Machine Learning
• Control Theory
• +۴ مورد دیگر

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.

Guanhua Chenمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Applied Machine Learning
• +۴ مورد دیگر

Dr. Guanhua Chen is an Assistant Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). His research focuses on Natural Language Processing (NLP), Large Language Models (LLMs), and Multimodal AI, with applications in drug discovery and urban planning. He previously earned his Ph.D. from the University of Hong Kong and B.S./M.S. from Tsinghua University, with internships at Microsoft Research Asia and Huawei Noah’s Ark Lab. Education: Ph.D. in Computer Science (University of Hong Kong, 2022), B.S. and M.S. in Computer Science (Tsinghua University, 2012 & 2014) Dr. Chen's research explores LLMs, data synthesis, multimodal systems, and model compression. He leads a lab equipped with 16 RTX 4090 GPUs, 16 L40 GPUs, and 4 A100 GPUs, providing resources for students to experiment with open-source and proprietary LLM APIs. His recent publications highlight innovations in LLM jailbreaking, parameter-efficient tuning (MiLoRA), cross-lingual transfer (mCLIP), and evaluation frameworks for robustness testing. Notably, he was awarded the Microsoft Research Asia StarTrack Scholar in 2025. Key Publications: mCLIP (ACL 2023), ImPart (ACL 2025), MiLoRA (NAACL 2025) Dr. Chen actively seeks motivated PostDoc, PhD, and Master's students. He serves as an Area Chair for ACL and EMNLP and contributes to advancing AI through open-source projects like mCLIP on GitHub.

• Bipedal Robotics
• Humanoid Robotics
• Powered Prosthesis
• +۵ مورد دیگر

Prof. Chenglong Fu is the Head of the Department of Mechanical and Energy Engineering at Southern University of Science and Technology (SUSTech) and Director of the Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems. He obtained his B.S. in Mechanical Engineering from Tongji University (2002) and Ph.D. in Precision Instruments and Mechanology from Tsinghua University (2007). His academic roles include serving as an Associate Editor for Robotica and leadership positions in IEEE conferences. Education : Tsinghua University (Ph.D.), Tongji University (B.S.) Current Positions : Head of Department, SUSTech; Director of Shenzhen Key Lab As a leading researcher in humanoid and assistive robotics , Prof. Fu investigates predictive principles of dynamic walking in complex environments. His work bridges robotics, biomechanics, and rehabilitation engineering , focusing on powered prostheses, exoskeletons, and SuperLimbs for load carriage efficiency and stability. His recent publications highlight innovations in: Unsupervised cross-subject adaptation for locomotion intent prediction Nonlinear series elastic actuators for hip exoskeletons 3D gaze fusion for terrain perception Variable stiffness suspension systems Stroke patient gait trajectory personalization He has received multiple scientific awards , including: Shenzhen Science and Technology Progress First Prize (2022) Chinese Association of Automation Science and Technology Second Prize (2022) IEEE Conference Best Paper Finalists (2019-2021) Shenzhen Peacock Plan (2018) Prof. Fu leads the Human-Augmented Robotics Lab (HARL) at SUSTech, directing over 30 research projects and holding >30 patents. His teaching contributions include multiple Excellent Mentor Awards at Tsinghua and SUSTech.

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.

• Multiobjective Optimization
• Supply Chain Management
• Artificial Intelligence Assisted Optimal Design
• +۱۳ مورد دیگر

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.

• Fintech
• Crowdfunding
• Mobile Marketing
• +۳ مورد دیگر

HU Wei is an Assistant Professor in the Department of Management Science at the School of Economics and Management, Tongji University , where he has worked since March 2023. His research spans topics in fintech, crowdfunding, and blockchain, with a focus on data-driven approaches. Education : Ph.D. in Information Systems (City University of Hong Kong, 2017-2022), M.S. in Mathematical Finance (City University of Hong Kong, 2015-2016), B.S. in Applied Physics (Tongji University, 2010-2015) HU Wei employs methodologies like Applied Econometrics and Machine Learning to study topics at the intersection of technology and business. His work on crowdfunding success factors, NFT market disparities, and investor sentiment analysis has appeared in top conferences like ICIS and PACIS. Scientific Awards : College of Business Ph.D. Student Teaching Excellence Award Research Tuition Scholarship (RTS) and Outstanding Academic Performance Award (OAPA) at City University of Hong Kong Outstanding Graduates in Shanghai and multiple Tongji University scholarships HU Wei has contributed to grants related to blockchain security and sentiment-driven portfolio optimization. He also serves as a reviewer for journals like Information Systems Research and conferences including ICIS and PACIS.

Jiang Ruoqingمشاهده پروفایل

استادیار

• Econometrics
• Statistical Machine Learning
• Empirical Finance
• +۳ مورد دیگر

Jiang Ruoqing serves as an Assistant Professor in the Department of Economics at Tsinghua University's School of Economics and Management since 2025. His office is located in Room B626, Lihua Building, with contact email jiangrq@sem.tsinghua.edu.cn. He teaches undergraduate courses including 'Artificial Intelligence Foundations and Data Thinking' and 'Principles of Economics'. Education: PhD in Economics, Columbia University (2019-2025), thesis: 'Essays on Econometric Inference with High-dimensional Factor Models' (Defense Committee: Jushan Bai, Simon Lee, Zhiliang Ying, Bernard Salanié, Haoge Chang) Master of Science in Statistics, Stanford University (2015-2018) Bachelor of Economics (International Program in Economics and Finance), Tsinghua University (2012-2015) Department of Electronic Engineering, Tsinghua University (2011) Research Focus Dr. Jiang's primary expertise lies in Econometrics and Statistical Machine Learning, developing advanced methodologies for high-dimensional data analysis. His secondary research spans Empirical Finance, Empirical Macroeconomics, Digital Economy and Finance, and Chinese Economy studies, with emphasis on financial market applications and computational economics. His work bridges theoretical statistical frameworks with practical economic modeling. Publications Insight His forthcoming 2025 Operations Research article on ORLM framework demonstrates interdisciplinary convergence between optimization theory, large-scale AI systems, and econometric modeling. The research establishes new methodologies for automated optimization in financial engineering contexts, reflecting his dual expertise in machine learning and economic applications. Academic Contributions No scientific awards or grant details were specified in the source material. His professional trajectory shows progression from MIT Financial Engineering Laboratory research (2017, supervised by Andrew W. Lo and Kathryn M. Kaminski) to his current faculty position.