جست‌وجوی استاد و پژوهشگر

• Journalism and Communication
• Intelligent Communication
• Digital Media Art
• +۶ مورد دیگر

Wang Jianmin serves as Professor and Doctoral Supervisor at Tongji University's School of Art and Media, concurrently holding the position of Vice Dean since 2014. With a computer science PhD from Sun Yat-sen University, he bridges engineering and media arts through pioneering research in intelligent communication systems and digital media interfaces. His work focuses on human-centered design for emerging technologies, particularly in automotive and virtual environments. His academic foundation includes: PhD in Engineering (Computer Software and Theory), Sun Yat-sen University (2003) Master's in Computational Mathematics, Sun Yat-sen University (1999) Bachelor's in Computational Mathematics, Nankai University (1996) Professor Wang's research centers on intelligent communication systems and digital media art, with significant contributions to automotive human-machine interfaces (HMI), virtual reality applications, and user experience methodologies. His investigations into driver-robot transparency, augmented reality navigation, and mixed-reality educational platforms demonstrate interdisciplinary innovation connecting computer science, cognitive psychology, and design theory. Current projects explore AI-driven media systems for urban environments and safety-critical interaction frameworks. Analysis of his recent publications reveals a cohesive research trajectory focusing on automotive HMI (40% of output), human-robot interaction (30%), and mixed reality applications (30%). His work consistently emphasizes experimental validation through driving simulators and user studies, yielding practical design guidelines for industry implementation. The interdisciplinary nature spans computer science, cognitive ergonomics, and media studies, with increasing emphasis on AI integration in communication systems. His scientific recognition includes national and provincial awards for innovation in human-computer interaction and educational technology: 2019 China Industry-University-Research Innovation Award for automotive HMI systems 2020 China User Experience Alliance Excellence Award 2012 Guangdong Dingying Science and Technology Award Multiple national/provincial science progress awards (2001-2009) 2020 Tongji University Teaching Achievement Award for curriculum development As an educator, Professor Wang mentors graduate students in national design competitions including the 'Core Cup' Future Automotive HMI Challenge and International User Experience Innovation Competition. His research program is supported by substantial funding from diverse sources: National Grants: National Natural Science Foundation projects on driver behavior modeling and cognitive testing Ministry of Education: 15+产学合作 projects for virtual simulation labs and curriculum development Shanghai Municipal: Publicity Department funding for smart city media research Industry Partnerships: Huawei (intelligent vehicle HMI), SAIC Motor (AR-HUD design), and automotive electronics firms He directs Tongji's Media Experiment and Practice Teaching Center and the All-Media Research Institute, leading teams developing virtual simulation platforms for emergency news reporting, intelligent vehicle interaction testing systems, and mixed reality educational tools. Current initiatives focus on AI-enhanced media art for urban applications and next-generation HMI frameworks for autonomous mobility solutions.

Yiguo Xueمشاهده پروفایل

استاد

• Geotechnical Engineering
• Structural Engineering
• Tunnel Engineering
• +۲ مورد دیگر

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.

Lei Buمشاهده پروفایل

استاد

• Model Checking
• Bounded Model Checking
• Formal Methods
• +۷ مورد دیگر

Lei Bu is a Professor and Vice Dean at the Software Institute , Nanjing University . He leads research in formal verification, cyber-physical systems, and software engineering, with a focus on bounded model checking and hybrid system analysis. B.Sc. and Ph.D. in Computer Science from Nanjing University (2004, 2010) Visiting student at Carnegie Mellon University and University of Texas at Dallas His research integrates formal methods and machine learning for verifying complex systems like IoT and software with real-time constraints. Key projects include BACH Toolset and BRICK for reachability analysis. Recent publications demonstrate expertise in hybrid system verification , cache side-channel detection , and parallel code analysis frameworks . His work bridges theoretical advancements with practical applications in safety-critical systems. Zhongchuang Software Talent Award (2023) CCF-IEEE CS Young Computer Scientist Award (2022) High-Tech Software Innovation Awards (2019-2023) As Principal Investigator, he leads major projects funded by National Science Foundation of China and Jiangsu Natural Science Foundation (2020-2027). Current tools include BACH for hybrid systems and MLB for Java symbolic execution.

Fang Kongمشاهده پروفایل

استادیار

• Online Learning
• Reinforcement Learning
• Machine Learning
• +۴ مورد دیگر

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.

Lianxiang LIمشاهده پروفایل

استاد

• Structural Engineering
• Geotechnical Engineering

Lianxiang LI is a Professor in the Department of Civil Engineering at Shandong University's School of Civil Engineering. His research focuses on structural engineering and geotechnical engineering, particularly in foundation pit support systems and composite foundation mechanics. University: Shandong University Department: Civil Engineering Email: lilianxiang@sdu.edu.cn His recent work involves centrifuge modeling for excavation mechanics, support structure optimization in composite foundations, and finite element analysis using PLAIXIS3D software. He has contributed to understanding soil pressure dynamics and innovative recyclable soil nail applications.

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +۲ مورد دیگر

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).

• Knowledge Representation and Reasoning
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

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.

Yepang Liuمشاهده پروفایل

دانشیار

• Software Testing and Analysis
• Adaptive Systems
• Mobile Computing
• +۴ مورد دیگر

Yepang Liu is a tenured Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech). He leads the Software Quality Lab and directs the Trustworthy Software Research Center under the Research Institute of Trustworthy Autonomous Systems. His academic journey includes a B.Sc. in Computer Science from Nanjing University (2010) and a Ph.D. in Computer Science and Engineering from The Hong Kong University of Science and Technology (HKUST) in 2015, followed by postdoctoral research at HKUST's CASTLE Lab and Cybersecurity Lab (2015–2018). Research interests focus on Software Testing and Analysis Empirical Software Engineering AI for Software Engineering (AI4SE) Trustworthy AI Mobile Computing Software Security Current projects explore automated testing for emerging technologies like HarmonyOS and extended reality (XR), leveraging large language models (LLMs) and reinforcement learning. Awarded thrice with ACM SIGSOFT Distinguished Paper Awards (ICSE 2021, ASE 2016, ICSE 2014) and a Platinum Level Research Artifacts Honor (FSE 2016), Liu has also received teaching accolades, including SUSTech's Junior Faculty of the Year (2021) and the Top-10 Most Popular Instructor Among 2024 Graduates. His work is funded by the National Natural Science Foundation of China, National Key Research and Development Program of China, and industry partnerships.

• Random Matrix Theory
• High dimensional Statistics
• Time Series Analysis
• +۱ مورد دیگر

Li Zeng is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech) since January 2021, following her Assistant Professor role at the same institution from August 2019 to December 2020. Previously, she was an Eberly Postdoctoral Fellow at Pennsylvania State University under Prof. Runze Li (2017-2019) and a Research Assistant at the University of Washington with Dr. Fang Han (2017). Ph.D. in Statistics and Actuarial Science from the University of Hong Kong (2017), advised by Prof. Jianfeng Yao M.Sc. in Statistics from Renmin University of China (2012) B.Sc. in Mathematical Science from Beijing Normal University (2009) Her research centers on Random Matrix Theory and High Dimensional Statistics , with significant contributions to eigenvalue/singular value distributions in large-dimensional matrices. She extends these theoretical foundations to Time Series Analysis for lagged auto-correlation structures and applies them to Machine Learning problems including neural network optimization and regularization. Her work bridges asymptotic probability theory with practical data science applications, developing robust methods for high-dimensional inference. Analysis of her 15 most recent publications (2016-2025) reveals a dominant focus on high-dimensional statistical theory with increasing machine learning integration. Key trends include asymptotic analysis of covariance/correlation matrices (60% of publications), factor modeling innovations (20%), and deep learning applications (20%). She consistently publishes in top-tier venues including Annals of Statistics (5 papers), Journal of the American Statistical Association , and machine learning conferences (ICML, ECCV), demonstrating exceptional interdisciplinary impact. Excellent Teaching Assistant Award (5 times) from HKU Department of Statistics and Actuarial Science (2012-2017) Dr. Li actively recruits postdoctoral researchers specializing in probability theory and high-dimensional statistics, emphasizing candidates with strong mathematical backgrounds. As a referee for premier journals including Annals of Statistics , JASA , and Journal of the Royal Statistical Society: Series B , she contributes significantly to scholarly review. Her research is institutionally supported through SUSTech faculty positions, with potential external funding inferred from extensive publication output and conference participation. Within SUSTech's Department of Statistics and Data Science (established 2019), Dr. Li contributes to a rapidly expanding research ecosystem focused on high-dimensional data analysis. The department supports multiple research directions including biostatistics and financial statistics, with active graduate programs (M.Phil/Ph.D.) and developing initiatives in data science education. Her teaching portfolio includes undergraduate Statistical Calculation and Software and graduate-level High Dimensional Statistics courses.

YUE MEIمشاهده پروفایل

دانشیار

• Computational Mechanics
• Biomedical Engineering
• Advanced Intelligent Materials
• +۳ مورد دیگر

YUE MEI is an Associate Professor at Dalian University of Technology's School of Mechanics and Aeronautics. He holds a doctoral degree in Mechanical Engineering from Texas A&M University and has postdoctoral experience at Swansea University (UK) and Saint-Etienne Mines (France). His research focuses on computational mechanics, biomedical applications, and CAE software development, particularly addressing national needs in industrial software autonomy. He leads major national projects including National Key R&D Programs and National Natural Science Foundation grants. Awards include the Liaoning Provincial Talent Aggregation Plan and Dalian High-level Talents designation. His work spans mechanical inversion methods, multi-physics finite element analysis, and structural nondestructive testing. Education: PhD in Mechanical Engineering, Texas A&M University (2013-2017) MSc in Solid Mechanics, South China University of Technology (2010-2013) BSc in Engineering Mechanics, China University of Petroleum (2006-2010) Research Interests: Integrating computational mechanics with biomedical and advanced materials fields. Key areas include: Mechanical inversion techniques for medical/industrial applications Multiphysics finite element methods (fluid-structure, electromagnetics) Development of autonomous CAE software tools Grants & Projects: Leads 7+ ongoing projects including 'Jointly Driven Cross-Scale Mechanical Analysis' (Liaoning Provincial) and 'General CAE Implicit Solver Engine' (National Key R&D). Recent papers focus on topology optimization in biomechanics, soft tissue characterization, and energy harvesting.

Hongwei Tangمشاهده پروفایل

استاد

• Structural Mechanics
• Materials Testing
• Fatigue and Fracture Analysis
• +۷ مورد دیگر

Hongwei Tang is a Professor at the Experimental Center of the School of Civil Engineering, Shandong University. He has been a faculty member since 2008 and is actively engaged in research and teaching in experimental mechanics and structural reliability. Education: Bachelor in Engineering Mechanics, Xi’an Jiaotong University, 1988 His research focuses on the mechanical behavior of materials and structures, particularly in strength, fatigue, fracture, vibration, and stress-strain testing. His work applies to both civil and mechanical engineering systems, with emphasis on experimental evaluation and reliability assessment. He has contributed extensively to the understanding of fatigue degradation in reinforced concrete and structural composites. Although specific article titles are not listed, his research output includes over 60 scientific papers, with 20 indexed in SCI, EI, or ISTP databases. His work spans structural health monitoring, non-destructive diagnostics using wavelet transforms, and fatigue performance of engineered materials. Scientific Awards and Honors: National Excellent Teacher in Mechanics (2006) Shandong University Laboratory Advanced Worker (2003) Outstanding Individual in Professional Ethics, School Level (2005) Professor Tang has supervised 16 master’s students and teaches graduate courses including Experimental Mechanics and Metal Fatigue and Experimental Technology . He has led multiple research projects funded by the National Natural Science Foundation of China, Ministry of Education, Shandong Provincial Natural Science Foundation, and various industrial partners such as China National Petroleum Corporation and China Aviation Industry Corporation. His recent projects include fatigue studies on corroded concrete beams, shear fatigue in composite structures, and software development for strength simulation. He holds significant academic service roles, including Secretary-General of the Shandong Mechanics Society, Chair of the Shandong Metrology and Testing Society’s Mechanics Committee, and member of national technical committees in mechanics and materials testing.

Qingrong Xiongمشاهده پروفایل

استاد

• Hydrogen Storage
• Carbon Capture and Storage
• Nuclear Waste Disposal
• +۷ مورد دیگر

Qingrong Xiong is a Professor in the Department of Hydraulic Engineering at the School of Civil Engineering, Shandong University. He holds a PhD in Civil Engineering from the University of Manchester (2012–2015) and received a Master's and Bachelor's in Hydraulic and Hydroelectric Engineering from Wuhan University (2010–2012, 2006–2010). Education: PhD in Civil Engineering, University of Manchester (2012–2015) Master of Hydraulic and Hydroelectric Engineering, Wuhan University (2010–2012) Bachelor of Hydraulic and Hydroelectric Engineering, Wuhan University (2006–2010) His research focuses on Hydrogen Storage , Carbon Capture and Storage , Nuclear Waste Disposal , and Thermal-Hydraulic-Chemical-Mechanical (THCM) Modeling of geomaterials. He specializes in pore network modeling, reactive transport in porous media, and coupled stress-seepage field analysis of underground rocks. Recent publications emphasize 3D mesostructural modeling , reactive diffusion in clays , and numerical simulation of microstructural evolution in nuclear materials. His work combines computational methods (finite element analysis, pore network models) with experimental validation. Scientific Awards President's Doctoral Scholar Award, University of Manchester

Qianqing Zhangمشاهده پروفایل

استاد

• Geotechnical Engineering
• Structural Engineering
• Pile Foundation Analysis
• +۳ مورد دیگر

Qianqing Zhang is a Professor at the School of Civil Engineering, Shandong University, affiliated with the Geotechnical and Structural Engineering Center. His work focuses on pile foundation mechanics and geotechnical engineering. Research interests include: Analysis of pile-soil interaction under tension/compression Nonlinear settlement prediction models Stability of cofferdams and underground structures Geotechnical risk assessment for karst tunnels Recent publications emphasize computational methods (e.g., Runge-Kutta, hyperbolic models) for pile response prediction, with applications in marine geotechnics and mining environments. Key trends include progressive deformation mechanisms, arching effects in soils, and numerical validation of field observations. He holds 3 software copyrights for pile foundation analysis tools and 10 first-ranked patents for geotechnical engineering devices, including: Grouting reinforcement systems Deformation monitoring equipment Sliding force measurement devices

• 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.