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Wei Zhang is a tenured Professor at the Southern University of Science and Technology (SUSTech) , Shenzhen, China, and a Senior Member of IEEE. He serves as Associate Editor for IEEE Transactions on Control System Technology and leads the CLEAR Lab (Control & Learning for Robotics and Autonomy) within the School of Automation and Intelligent Manufacturing (AiM). His career spans institutions including the University of California, Berkeley (postdoc), and The Ohio State University (Assistant/Associate Professor). Education: PhD in Electrical Engineering from Purdue University (2009), MS in Electrical and Computer Engineering from University of Kentucky (2005), BS in Automation from University of Science and Technology of China (2003) Research Interests focus on integrating control theory, optimization, and machine learning to develop intelligent systems. Key areas include: Legged Robots: Dynamic locomotion control, bio-inspired gait design, and push recovery mechanisms Autonomous Systems: Real-time motion planning, obstacle avoidance, and safe navigation in adversarial environments Smart Grids: Distributed control for energy systems and transactive energy optimization Machine Learning: Reinforcement learning for robotics, Q-learning convergence analysis, and hybrid control-learning frameworks Publication Trends highlight interdisciplinary work at the intersection of robotics and control systems. His recent 2024 papers address: Whole-body control for wheeled-quadrupedal robots Geometric object pose refinement in computer vision Task-space Riccati feedback for underactuated systems Teacher-student reinforcement learning architectures Scientific Awards include: 2016 : NSF CAREER Award 2015 : Lumley Research Award (Ohio State University) 2013 : AFOSR Summer Faculty Fellowship 2018 : National Distinguished Expert (Young, China) 2019 : International Underwater Robot Competition 2nd Prize (team advisor) Academic Leadership involves editorial roles at IEEE Transactions on Control System Technology and IEEE Transactions on Power Systems. His lab provides state-of-the-art robotics platforms including quadruped robots, Kuka manipulators, and UAVs for algorithm validation. Research collaborators span The Ohio State University , UC Berkeley , CMU , and The University of Hong Kong .

• Control Theory
• Optimization Theory
• Machine Learning
• +5 more

Dr. Yanqing Hu is an Associate Professor at the Department of Statistics and Data Science, School of Science, Southern University of Science and Technology (SUSTech). With a Ph.D. in Systems Theory from Beijing Normal University (2011) and postdoctoral experience at the Levich Institute, City University of New York (2011-2013), his work focuses on big data analysis of complex systems, particularly in social media dynamics, network resilience, and graph neural network applications. Ph.D.: Beijing Normal University (Systems Theory, 2011) Postdoctoral: Levich Institute, CUNY (2011-2013) Research spans complex network analysis, information spreading mechanisms, and predictability of network structures. His work combines theoretical frameworks with real-world applications in social networks, infrastructure systems, and brain connectivity. Recent publications explore information percolation in social media, resilience quantification in interdependent networks, and intrinsic structure predictability. These studies appear in high-impact journals like Nature Human Behaviour (IF: 24.3), Nature Communications (IF: 17.7), and PNAS (IF: 10). World AI Conference Youth Outstanding Paper Nomination Beijing Outstanding Doctoral Dissertation Award Guangdong Special Support for Young Talents Guangdong Outstanding Youth Fund Collaborations include leading researchers from Boston University, King's College London, and Shenzhen-Hong Kong Institute of Microelectronics. His work informs network defense strategies and efficient navigation mechanisms in complex systems.

• Big Data
• Complex Networks
• Graph Neural Networks
• +1 more

Shunxiang Cao is an Assistant Professor at Tsinghua Shenzhen International Graduate School in China, where he has worked since July 2022. He earned his Ph.D. in Aerospace Engineering from Virginia Tech (United States) between August 2014 and August 2020 and completed his B.S. in Aerospace Engineering at Beihang University (China) from September 2010 to May 2014. Prior to his current role, he served as a Postdoctoral Scholar at the California Institute of Technology (United States) from November 2019 to May 2022. Teaches courses such as Advanced Numerical Analysis, Numerical Methods for PDEs, and Fluid-Structure Interaction simulations. Research Interests include fluid-structure interaction, computational fluid dynamics, shock/bubble dynamics, material failure analysis, embedded-boundary methods, resolvent-based optimization, Kalman inversion, high-intensity focused ultrasound, and digital twin technology. His work focuses on numerical modeling, optimization, and fluid-solid coupling mechanisms in diverse applications like underwater propulsion, energy storage membranes, and medical acoustics. Scientific Awards include the USNCCM16 Conference Award (2021), Graduate Fellowship at Virginia Tech (2014-2015), Merit Undergraduate Student in Beijing (2014), and Singapore Technology Engineering Fellowship (2010-2013).

• Fluid-Structure Interaction
• Computational Fluid Dynamics (CFD)
• Shock/Bubble Interaction
• +6 more

Dr. Xiong Yi is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Computational Design and Fabrication (CoDeFab) research group, focusing on the integration of computational design methods with advanced manufacturing technologies, particularly in the field of additive manufacturing. Dr. Xiong has established himself as a leading researcher in computational design for additive manufacturing, with a strong international research background spanning Europe and Asia. Dr. Xiong's educational journey includes: Doctor of Science (DSc) in Engineering Design and Production from Aalto University, Finland (2012-2016) Master of Science (MSc) in Machine Automation from Tampere University of Technology, Finland (2010-2012) Bachelor of Engineering (BEng) in Mechanical Engineering from Hubei University of Technology, China (2006-2010) Dr. Xiong's research primarily focuses on computational design and fabrication methodologies, with particular emphasis on design for additive manufacturing (DfAM), intelligent manufacturing systems, and smart materials. His work bridges the gap between theoretical design principles and practical manufacturing constraints, developing novel approaches for the production of complex engineered products. He has pioneered research in continuous fiber-reinforced composite additive manufacturing, developing innovative process planning and optimization techniques that enable the production of high-performance structural components. His research in electrothermally controlled origami and 4D printing of smart materials represents cutting-edge work at the intersection of materials science, mechanical engineering, and computational design. Dr. Xiong's recent publications reveal a strong focus on continuous fiber-reinforced composites, with significant contributions to 4D printing, metamaterials, and intelligent process planning. His work integrates computational design with manufacturing constraints, creating novel approaches for topology optimization, toolpath planning, and structural design that consider both performance requirements and manufacturability limitations. The research demonstrates increasing sophistication in materials science applications, particularly in programmable materials and multi-functional structures. Dr. Xiong has received multiple prestigious awards for his research contributions, including: Best Presentation Award at the 24th Chinese Conference on Mechanisms and Machine Science (IFToMM CCMMS2024) Best Presentation Award at the International Conference on Frontiers of Additive Manufacturing Research (RAAM 2024) Best Paper Award at the International Conference on Design for 3D Printing (ICD3DP 2023) PhD Scholarship from Aalto University (2016) Research Travel Grant from the International Association for Vehicle System Dynamics (IAVSD) (2013) National Scholarship from the Ministry of Education (2008) As a dedicated educator and mentor, Dr. Xiong serves as a PhD supervisor at SUSTech and has successfully guided students who have gone on to pursue advanced studies and careers at prestigious institutions including Hong Kong Polytechnic University, Beihang University, DJI Innovations, and Singapore's A*STAR research institute. His research is supported by multiple competitive grants, including key projects from the National Key R&D Program of China, the National Natural Science Foundation of China, and provincial and municipal funding agencies. Dr. Xiong also serves on the editorial board of the Journal of Engineering Design and as a guest editor for Composites Communications, contributing to the advancement of his field through scholarly service. Dr. Xiong leads the CoDeFab research group, which maintains a strong collaborative culture focused on 'design leading manufacturing, manufacturing driving design, and digital-intelligent integration.' The group has developed several advanced manufacturing platforms, including multi-axis continuous fiber-reinforced composite additive manufacturing systems, smart composite additive manufacturing platforms, and multifunctional soft matter open manufacturing platforms. With a focus on practical applications and innovation, the CoDeFab group actively collaborates with industry partners and has established a joint laboratory to bridge academic research with industrial implementation.

• Computational Design and Fabrication
• Design for Additive Manufacturing
• Adaptive Process Planning
• +6 more

Yiguo Xue is a Professor and PhD Tutor at the Geotechnical and Structural Engineering Center, School of Civil Engineering, Shandong University. He has contributed extensively to tunnel engineering, subsea infrastructure, and slope stability analysis through advanced prediction models and numerical simulations. Research Interests: New tunnel geological condition prediction technologies Subsea tunnel and underground energy storage systems High slope stability evaluation Engineering exploration methods His recent work focuses on tunnel safety, subsea structural mechanics, and AI-driven hazard prediction models. Publications highlight his expertise in rockburst analysis, water inrush risk, and excavation optimization using machine learning and numerical frameworks. Scientific Awards: Recipient of China's Top 100 Most Influential Domestic Academic Paper Award (2008 paper on tunnel geological hazard forecasting) He has secured multiple national invention patents for tunnel monitoring devices (e.g., vibration sensors, collapse prediction systems) and developed specialized software for displacement prediction and rock classification.

• Geotechnical Engineering
• Structural Engineering
• Tunnel Engineering
• +2 more

Wang Chun is a tenured Associate Professor in the Department of Management Science and Engineering at the School of Economics and Management, Tsinghua University. He currently serves as Deputy Director of the department and has held academic positions at Tsinghua since 2017. His work bridges stochastic control, reinforcement learning, and machine learning applications in operations management and financial technology. Education: Bachelor's Degree from Tsinghua University (2005) Master's Degree from the University of Wisconsin-Madison (2008) Doctorate in Operations Research from Columbia University (2014) Research Interests: His research focuses on stochastic control and decision-making under uncertainty, particularly in inventory management, revenue management, financial engineering, and sports analysis. He also investigates reinforcement learning algorithms for dynamic optimization and applies machine learning to business data analytics and financial technology. Published Work Trends: Wang’s publications span operations research, financial mathematics, and computational management science. Recent studies address seasonal optimization patterns, robust inventory models, and game-theoretic approaches to sports analytics. His work integrates theoretical advancements in dynamic programming with practical applications in financial engineering and market microstructure. Career History: 2023–Present: Associate Professor, Tsinghua University 2020–2023: Associate Professor, Tsinghua University 2017–2020: Assistant Professor, Tsinghua University Researcher at KCG (New York), leading studies on quantitative investment and asset optimization

• Operations Management
• Stochastic Control
• Decision-Making under Uncertainty
• +9 more

Fang Kong is an Assistant Professor in the Department of Statistics and Data Science at the Southern University of Science and Technology (SUSTech). He earned his PhD in Computer Science from Shanghai Jiao Tong University under the supervision of Prof. Shuai Li and received his Bachelor's degree in Software Engineering from Shandong University. Education: PhD in Computer Science, Shanghai Jiao Tong University (2020-2024) Bachelor's Degree in Software Engineering, Shandong University (2016-2020) Dr. Kong is broadly interested in developing theoretically guaranteed algorithms for sequential decision-making problems, with particular focus on multi-armed bandits and reinforcement learning, as well as their applications in online experimentation and recommendation systems. His research spans theoretical foundations of bandit algorithms, matching markets, influence maximization, and online learning under various feedback structures. He has made significant contributions to the understanding of best-of-both-worlds algorithms that perform well in both stochastic and adversarial environments. His publication record shows a strong trajectory of high-impact work in top-tier conferences including NeurIPS, ICML, ICLR, AAAI, WWW, and AAMAS. His research demonstrates expertise in theoretical machine learning with a focus on bandit algorithms, particularly in matching markets and sequential decision-making problems. His work often bridges theoretical guarantees with practical applications in recommendation systems and online experimentation. Scientific Awards: CCF Doctoral Dissertation Award in Agent and Multi-Agent Systems (2025) Baidu Scholarship (2024) National Scholarship for PhD students (2023, 2022) AAMAS Student Scholarship (2023) Microsoft Research Asia Excellence Award (2022) Dr. Kong actively mentors students at various levels, including PhD and Master's students at SUSTech, visiting students from other institutions, and undergraduate researchers. He serves as a reviewer for top machine learning conferences (ICLR, NeurIPS, ICML, WWW) and journals (IEEE PAMI, TMLR). His teaching includes graduate Machine Learning and undergraduate Artificial Intelligence courses at SUSTech.

• Online Learning
• Reinforcement Learning
• Machine Learning
• +4 more

Dr. Zicheng Su is a Distinguished Researcher at Tongji University's College of Transportation Engineering, specializing in intelligent transportation systems. He joined the university in July 2023 and maintains affiliations with the Department of Traffic Information and Control Engineering and the MAGIC research group. His work bridges transportation engineering and artificial intelligence to solve urban mobility challenges. Dr. Su's educational background includes: Ph.D. in Advanced Design and Systems Engineering from City University of Hong Kong (2018-2022) under Prof. Andy H.F. Chow Bachelor's degree in Transportation Engineering from Sun Yat-sen University (2014-2018) under Prof. Renxin Zhong His research centers on traffic flow modeling and adaptive control systems using reinforcement learning. Key contributions include hierarchical control frameworks for stochastic traffic networks, multi-intersection management solutions, and bus service reliability optimization through connected vehicle technology. His work integrates model-based and data-driven approaches to address real-world urban mobility problems. Analysis of Dr. Su's publications (2017-2023) reveals consistent focus on reinforcement learning applications in traffic control, with increasing emphasis on decentralized solutions and multi-agent systems. His work spans transportation journals (Transportation Research Parts B/C) and top AI conferences (AAAI), demonstrating interdisciplinary impact in both transportation engineering and machine learning domains. Dr. Su's scientific recognition includes: HKSTS Outstanding Student Paper Award (1st place, 2021) Outstanding Academic Performance Award, City University of Hong Kong (2021) Best Student Presentation Award at Zhejiang University Workshop (2020) Multiple scholarships including HKRGC Postgraduate Studentship (2018-2022) As part of the MAGIC research group at Tongji University, Dr. Su contributes to projects on traffic design, cooperative vehicle infrastructure, and smart urban mobility systems. The group has developed the MAGIC Dataset and practical demo systems for intelligent driving assistance and traffic management applications.

• Traffic flow modeling
• Network traffic signal control
• Optimal control and reinforcement learning
• +2 more

Dr. Fu Yong is an Assistant Professor and doctoral supervisor at the Department of Marine Science and Engineering , Southern University of Science and Technology (SUSTech), Shenzhen, China. He earned his PhD in Geotechnical Engineering from the National University of Singapore (NUS) in 2018, preceded by a Master's (Tongji University, 2013) and Bachelor's (Shanghai University, 2010) in Civil Engineering . Current Role: Assistant Professor, SUSTech (2020–present) Prior Roles: Research Fellow (NUS, 2018–2020), Research Engineer (NUS, 2017–2018) Dr. Fu specializes in marine geotechnical engineering , with key focuses on offshore wind power foundation design, deep-sea anchoring technologies, and underground space development. His technical expertise includes large deformation finite element modeling (CEL/RITSS methods), stochastic analysis , and machine learning (GWO-MLP optimization). His work bridges laboratory experiments (centrifuge and field tests) with computational simulations to address challenges in marine infrastructure. Marine foundation design for offshore wind turbines and marine ranches Deepwater dynamic penetration anchoring systems Horizontal deep soil mixing equipment for underground space Finite element simulation of large deformations and thermal effects His publication trends reveal a consistent focus on offshore anchoring mechanics, shale fracture modeling, and machine learning integration for geotechnical predictions. Recent works (2024–2025) emphasize torsional effects in suction anchors, sand-GFRP interface behavior, and advanced constitutive models for cemented soils. Key journals: Geotechnique , Ocean Engineering , Physics of Fluids , Computers and Geotechnics Dr. Fu has received multiple scientific awards , including the 2024 SUSTech Undergraduate Teaching Award, 2022 SUSTech Outstanding College Mentor Award, and NUS Research Scholarship (2013–2017). Selected Honors: 2024 First Prize, SUSTech Undergraduate Teaching Award 2022 SUSTech Outstanding College Mentor 2020 Shenzhen Overseas High-level C Talent He actively contributes as a reviewer for 18+ international journals and serves on editorial boards for journals like Sustainable Structures . His work on machine learning-driven anchor penetration prediction (2024) and hybrid phase-field shale fracture modeling (2024) exemplifies interdisciplinary innovation.

• Offshore Geotechnical Engineering
• Marine Foundation Engineering
• Marine Mooring and Anchoring Technologies
• +4 more

Dr. Zeying YANG is an Associate Professor and Master Tutor in the Department of Civil Engineering at Shandong University's School of Civil Engineering. His expertise lies in bridge engineering, with a focus on health monitoring, maintenance, and advanced construction technologies. Research Interests: Bridge health monitoring, damage diagnosis, and early warning systems Bridge maintenance and reinforcement technologies Steel and steel-concrete composite bridge structures Intelligent bridge construction using BIM, GIS, and GPS Long-span bridge construction monitoring His research spans theoretical developments and practical applications, including neural networks for load assessment, CFRP reinforcement techniques, and innovative construction control methods for cable-stayed bridges. Laboratory and Contact: He is based at the School of Civil Engineering, Qianfoshan Campus, Shandong University, Jinan, China. For inquiries, he can be reached at yangzy@sdu.edu.cn .

• Bridge health monitoring
• Damage diagnosis and early warning
• Bridge maintenance and reinforcement technology
• +3 more

Chen Jian is the Lenovo Chair Professor at the School of Economics and Management, Tsinghua University , and Director of the Ministry of Education Key Research Base for Humanities and Social Sciences . With a career spanning over 30 years at Tsinghua, he has held multiple leadership roles in academic societies, including Vice Presidency in four top-tier Chinese academic societies and editorial positions in over ten international journals. Research Focus: Systems engineering, supply chain optimization, and decision theory Courses Taught: Dynamic systems analysis and control, operations management His scientific contributions include over 200 publications and 50 major projects, with significant impact in operations management, supply chain coordination, and emerging business models. He has trained numerous doctoral students and received prestigious accolades like the National Science Fund for Distinguished Young Scholars and IEEE Fellow status.

• Systems engineering
• Enterprise operations management
• Supply chain coordination and optimization
• +2 more

Dr. Hou Chao is an Associate Professor and PhD Supervisor at the Department of Ocean Science and Engineering , Southern University of Science and Technology (SUSTech) since 2019. Previously, he held academic positions at the University of Sydney (2015-2019) and served as a lecturer there (2018-2019). He received his BEng (2009) and PhD (2014) in Structural Engineering from Tsinghua University , where he was recognized as an outstanding graduate and awarded the National Scholarship of China (2013). Research Interests focus on High-performance composite structures Sustainable marine engineering systems Load and marine environment coupling effects Modular construction technology Corrosion resistance in offshore structures Publication Trends reveal expertise in concrete-filled steel tubular (CFST) structures under extreme conditions. His work spans mechanical behavior analysis, fatigue assessment, and innovative connection design for modular buildings, with methodological focus on experimental testing, finite element modeling, and machine learning applications. Key sub-fields include localized corrosion effects, post-fire performance, and reliability-based design frameworks. Scientific Recognition : 2022 Ministry of Education Natural Science First Prize (3/5) China Patent Excellence Awards (2/2, 5/10) Inclusion in Stanford-Elsevier Top 2% Scientists (2023, 2024) Dean’s Outstanding Teaching Award, University of Sydney (2018) Editorial roles: Structures , Advances in Structural Engineering He has secured over 10 international research grants, including National Natural Science Foundation of China projects and Shenzhen Science & Technology Innovation Commission awards. His work is codified in 7 Chinese national standards, applied in critical infrastructure like the Wenchuan Kequ Bridge.

• High-Performance Composite Structures
• Sustainable Marine Engineering
• Structural Load and Corrosive Environment Coupling
• +3 more

Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +2 more

Xiaowang Zhang is a Full Professor at the College of Intelligence and Computing, Tianjin University, China. His research spans Knowledge Graphs, Natural Language Processing, Multi-modality, and Software Engineering, with a focus on semantic reasoning, relation extraction, and software vulnerability detection. He leads a team advising international students and contributes to open-access research in venues like ACL, WWW, and ISWC. Education : PhD from Peking University (2011), Postdoc at Hasselt University (2011-2014), Visiting Associate Professor at Griffith University (2018). His research integrates Knowledge Graphs for relation extraction and rule learning, advances LLMs in few-shot learning and cross-lingual stance detection, and addresses software engineering challenges through vulnerability description analysis and code translation error detection. Recent work emphasizes temporal reasoning and explainability in AI systems. Article trends highlight interdisciplinary applications of Knowledge Graphs in NLP tasks, leveraging graph structures for QA systems, and improving software security through feature inference and multi-interest recommendation frameworks. While no explicit awards are listed, his collaborations with institutions like Griffith University and Hasselt University demonstrate global academic engagement. He advises international students and emphasizes programming skills, communication, and teamwork in future advisees. His full_description expands on 15+ years of contributions to knowledge representation, semantic networks, and code generation systems.

• Knowledge Representation and Reasoning
• Natural Language Processing
• Machine Learning
• +3 more

Dr. Qi Hao is an Associate Professor and Deputy Director of the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He also serves as Executive Director of the SUSTech & Haylion Intelligent Transportation Center and Head of the SUSTech & FengXiangBiao ICV Education Joint Laboratory. Prior to joining SUSTech in 2014, he was an Assistant Professor at the University of Alabama (2007-2014) following postdoctoral training at the University of Kentucky. Dr. Hao received his PhD in Electrical and Computer Engineering from Duke University in 2006, with Master's and Bachelor's degrees from Shanghai Jiaotong University (1997 and 1994). His academic journey includes research positions at Singapore's Data Storage Institute (1998-2001) and engineering experience at Shanghai Electric Tool Research Institute (1997-1998). As an expert in intelligent sensing and unmanned autonomous systems, Dr. Hao's research spans bionic intelligent sensor design, machine learning applications, and autonomous vehicle technologies. His work demonstrates a strong integration of theoretical frameworks with practical implementations, particularly in pyroelectric infrared sensing, UAV systems, and distributed sensor networks. His research has evolved to address increasingly complex challenges in autonomous navigation, multi-robot coordination, and edge computing for intelligent transportation systems. Dr. Hao's publication trend reveals a strategic shift toward integrated autonomous systems, with recent work emphasizing robotics, machine learning, and wireless communication. His research bridges computer vision, sensor fusion, and deep learning to solve practical challenges in autonomous vehicle perception, wireless energy transfer, and human activity recognition. The interdisciplinary nature of his work connects electrical engineering principles with cutting-edge AI techniques. US National Science Foundation Research Awards (2009, 2011) Multiple IEEE conference best paper awards and finalists (2012-2021) Shenzhen Overseas High-level Talents Peacock Plan Category C (2017) Advanced Educators in Shenzhen (2017) Outstanding Teacher of Guangdong Province (2021) SUSTech Outstanding Research and Service Awards (2017-2021) Dr. Hao has secured significant research funding as Principal Investigator for projects from NSFC, Shenzhen Nanshan District, and industry partners including Huawei, Intel, and Haylion Technology. His current projects focus on intelligent binary sensing, next-generation camera systems, autonomous driving safety frameworks, and federated learning for vehicle perception. He has also contributed to educational initiatives through Guangdong Province and Shenzhen City education reform projects. Leading the SUSTech & Haylion Intelligent Transportation Center and the SUSTech & FengXiangBiao ICV Education Joint Laboratory, Dr. Hao oversees research teams developing solutions for intelligent transportation challenges. His labs integrate expertise across computer science, electrical engineering, and mechanical engineering to advance autonomous vehicle technologies, sensor networks, and robotics applications for real-world implementation.

• Intelligent Sensing
• Machine Learning
• Unmanned Autonomous Systems
• +3 more