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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.

• Software Testing and Analysis
• Adaptive Systems
• Mobile Computing
• +4 more

Liu Wei is a Research Professor in the Department of Mechanical and Energy Engineering at Southern University of Science and Technology (SUSTech). He holds a PhD in Computer Application Technology from the University of Chinese Academy of Sciences (2013) and has extensive experience in 3D imaging perception, computer vision, and embedded systems. Education: PhD (Computer Science, Chinese Academy of Sciences), MSc (Instrument Science, USTC), BSc (Measurement & Control, USTC) Current roles: Research Professor, postdoctoral advisor, and assistant dean at SUSTech Institute of Robotics Key research areas: 3D machine vision, industrial robots, surgical robotics, and intelligent autonomous systems His work spans both academic and industrial applications, including robotics for supermarket environments and medical surveillance devices. He has led multiple significant projects including 3D visual inspection robots and UAV joint labs. Scientific achievements include the IEEE ROBIO Best Conference Paper Award and multiple invention patents. His research combines computer vision with embedded systems for both industrial and biomedical applications. Contact details: 5th Floor, North Building, College of Engineering, SUSTech | Office: 0755-88011264 | Email: liuw2@sustech.edu.cn

• Computer Vision
• Robotics
• 3D Imaging
• +4 more

Chunying Li serves as an Assistant Professor in the Department of Electronic and Electrical Engineering at the College of Engineering, Southern University of Science and Technology (SUSTech). Recognized as a 2024 Shenzhen Pengcheng Peacock Program Distinguished Talent and Ocean Power Young Scientist nominee, her research pioneers spherical underwater robotics and multi-agent systems for complex marine environments. Her educational foundation includes: Ph.D. in Intelligent Mechanical Systems Engineering from Kagawa University, Japan (2021-2023) M.S. in Control Engineering from Tianjin University of Technology, China (2017-2020) Dr. Li's research integrates biomimetic principles with advanced engineering to solve underwater navigation challenges. Her work on multi-sensor fusion enables robust environmental perception, while adaptive control algorithms allow spherical robots to operate in turbulent conditions. Key applications include ocean monitoring, resource exploration, and collaborative multi-robot missions where traditional systems fail. Recent publications (2020-2024) reveal a strong focus on performance optimization and biomimetic sensing , with 7 of 11 representative papers appearing in Q1 journals like Information Fusion (IF=17.564). A notable trend is the development of artificial lateral line systems inspired by fish, significantly improving obstacle recognition in murky waters. Her accolades include: 2024 Young Scientists Nomination for Ocean Power 2024 Shenzhen Pengcheng Peacock Program Distinguished Talent 2023 Chinese Society of Automation Natural Science Award (Second Prize) 2022 CSC National High-Level University Scholarship Dr. Li leads the Shenzhen Outstanding Science and Technology Innovation Talent Program while contributing to international projects: Shenzhen Grants: Pengcheng Peacock Program (Principal Investigator) International Collaboration: Japan SPS KAKENHI Program (Key Researcher) Regional Projects: Tianjin Science and Technology Innovation Cooperation Program Her laboratory develops spherical underwater robot platforms featuring hybrid thrusters and multi-sensor arrays, emphasizing father-son robot coordination and jellyfish-inspired swarm behaviors. Through IEEE ICMA conference leadership roles (Finance Chair 2025, Publication Co-Chair 2024), she actively shapes robotics research standards in Asia-Pacific.

• Underwater Robotics
• Biomimetic Robotics
• Multi-robot Systems
• +4 more

DING Yulong is a Research Assistant Professor at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen. He holds a Ph.D. in Chemical Engineering from the University of British Columbia (2012) and M.S./B.S. in Chemical Engineering from Tsinghua University (2008/2005). His research focuses on Internet of Things (IoT) , Industrial IoT Safety and Security , Public Safety , and Low Power Wide Area Network Communication Technology . Ph.D., Chemical Engineering, University of British Columbia (2012) M.S., Chemical Engineering, Tsinghua University (2008) B.S., Chemical Engineering, Tsinghua University (2005) His work bridges IoT applications with cyber-physical systems, emphasizing safety-security requirement conflicts , anomaly detection in industrial processes, and low-power communication protocols like LoRa. He has pioneered methods for real-time positioning using UWB and reinforcement learning for network resource allocation. His scientific awards include the 2014 Shenzhen Overseas High-level Talents (Class B). He supervises research projects in industrial IoT security and mentors students in grant applications, protocol design, and cyber-physical systems. His lab is based in Room 645A, South Tower, College of Engineering, SUSTech.

• Internet of Things
• Industrial IoT Safety and Security
• Public Safety
• +5 more

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.

• Random Matrix Theory
• High dimensional Statistics
• Time Series Analysis
• +1 more

Liangming Chen is an Associate Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), China. Previously, he held postdoctoral positions at Nanyang Technological University, Singapore. His academic roles include deputy editor-in-chief for IEEE Transactions on Robotics and IEEE Transactions on Systems, Man, and Cybernetics: Systems . As a Senior Member of the IEEE, he has been selected for the National High-Level Talent Program (Youth Project) and Shenzhen Overseas High-Level Talent Program (Peacock Plan Category C). Education : PhD in Systems and Control (University of Groningen, 2021), PhD in Control Science and Engineering (Harbin Institute of Technology, 2019), BEng in Automation (Southwest Jiaotong University, 2015). His research integrates Rigid Graph Theory , Multi-Agent Systems , and Robotics , with recent work focusing on Brain-Inspired Intelligence and Machine Learning . Key trends in his publications (2020-2025) include Formation Control , Distributed Localization , and Unmanned Systems , appearing in top journals like IEEE TAC , Automatica , and IEEE TRO . Scientific Awards include the Shenzhen Overseas High-Level Talent Program and competition prizes in Electronics Design and Mathematical Modeling . He has guided teams to national awards and mentored students to placements at top institutions. His group pursues High-Impact Research in Multi-Agent Estimation , Robot Swarms , and Autonomous Navigation , with applications in drones and embodied intelligence.

• Rigid Graph Theory
• Multi-Agent Systems
• Unmanned Systems
• +4 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

Jianwei Ma is a Professor at the School of Mathematics, Harbin Institute of Technology, leading the Center of Geophysics and the Institute of Artificial Intelligence. His expertise spans exploration geophysics, artificial intelligence, and compressed sensing. Ma holds a Ph.D. in Solid Mechanics from Tsinghua University (2002) and a BS in Engineering Mechanics from Dalian University of Technology (1998). His research focuses on improving seismic exploration through mathematical methods, including sparse transforms, compressed sensing, and deep learning applications. Notable contributions include methodologies for seismic noise reduction, data interpolation, and full waveform inversion. Ma has held visiting positions at institutions like UCLA, UC Los Angeles, and the University of Texas at Austin. He has received prestigious awards such as the NSFC Distinguished Young Scholar (2016) and World's Top 2% Scientists (2020-2024). Ma actively contributes to professional societies, including SEG and IEEE, and has organized international workshops on mathematical geophysics and AI-driven geoscience. His team includes faculty members and over 30 graduate students, reflecting his commitment to training future researchers in geophysics and AI. Collaborations with global leaders in geophysics and mathematics, such as Prof. Stanley Osher (UCLA) and Prof. Gerlind Plonka (University of Goettingen), highlight his interdisciplinary approach. Key publications include advancements in deep learning for geophysical inversion and sparse representation techniques for seismic data processing.

• Exploration Geophysics
• Artificial Intelligence
• Compressed Sensing
• +3 more

Jun Zhu is a Professor in the Computer Science Department at Tsinghua University and serves as Co-Director of the Tsinghua Artificial Intelligence Lab (TSAIL) and Director of the Tsinghua-Bosch Joint Center for Machine Learning. His research focuses on probabilistic machine learning, adversarial robustness, Bayesian methods, deep learning, and reinforcement learning. His recent work includes open-sourcing the Vidu video generator (2024), DPM-Solver for diffusion models (2024), and Tianshou deep RL library, with publications in top venues like ICLR and IEEE CoG. He has received prestigious awards including IEEE Fellow, AAAI Fellow, and MIT TR35 China recognition. Notable Awards: IEEE Fellow AAAI Fellow MIT TR35 China Pioneer (2017) NSFC Excellent Young Scholar (2013) Leadership: Co-Director, TSAIL Group Director, Tsinghua-Bosch Joint Center for ML Associate Editor-in-Chief, IEEE TPAMI

• Probabilistic Machine Learning
• Adversarial Robustness
• Bayesian Methods
• +6 more

Wei Chen is a Professor at the State Key Laboratory of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, China. His academic journey began and ended at Zhejiang University, where he earned both his Bachelor's (1996) and Ph.D. (2002) in Computer Science, followed by a visiting Ph.D. position at the Fraunhofer Institute in Germany (2000-2002) and a visiting scholar role at Purdue University (2006-2008). Research Focus: Visualization, Visual Analytics, Biomedical Image Computing, Big Data Analytics. Academic Leadership: Served on editorial boards of journals like IEEE Transactions on Visualization and Computer Graphics, Journal of Visual Informatics, and as Chair of IEEE Pacific Visualization Symposium. Impact: Over 110 IEEE/ACM Transactions and CCF-A papers, four Chinese textbooks on visualization, and two monographs on big data technology. His recent publications (2023-2024) span domains like visual analytics for health insurance fraud detection, quantum circuit visualization, federated learning visualization, urban mobility modeling, and AI-enhanced cultural heritage preservation. A thematic trend shows integration of deep learning, large language models, and human-in-the-loop systems with visual interfaces. Scientific Awards include the Zhejiang Province Natural Science First Prize (2021, 2017), CCF Technology Invention First Prize (2020), Wu Wenjun Artificial Intelligence Science and Technology Progress First Prize (2023), and multiple Best Paper/Poster recognitions at IEEE VIS and Pacific Visualization conferences. He actively mentors graduate students and leads the Visual Analytics Group , contributing to national and international standards in visualization research. His editorial and organizational roles in top conferences further solidify his influence in shaping the field.

• Data Visualization
• Visual Analytics
• Biomedical Image Computing
• +5 more

Hu Ding is a pre-tenure Professor in the School of Computer Science and Engineering at the University of Science and Technology of China (USTC), where he directs the Data Intelligence, Algorithms, and Geometry (DIAG) research group. He previously held positions as a tenure-track Assistant Professor at Michigan State University (2016-2018) and a Simons-Berkeley Research Fellow jointly at Tsinghua University and UC Berkeley (2015-2016). Education: • Ph.D. in Computer Science, State University of New York at Buffalo (2015) • B.S. in Mathematics, Sun Yat-Sen University (2009) Research Interests: Hu Ding's research focuses on developing efficient algorithms for geometric optimization problems with applications in machine learning, big data, and biomedical imaging. His work bridges theoretical computer science (especially computational geometry) with practical challenges in distributed systems, outlier detection, and high-dimensional data analysis. Key areas include constrained clustering, truth discovery in crowdsourced data, and geometric methods for biomedical image analysis. Publication Trends: His recent publications demonstrate a strong focus on scalable algorithms for high-dimensional geometric optimization, particularly in distributed environments with noisy data. A consistent theme is developing theoretically-grounded solutions with practical efficiency, evidenced by work on sublinear-time algorithms, coreset constructions, and approximation frameworks for problems like k-center clustering and SVM optimization with outliers. Awards and Honors: Young Investigator Award, Ministry of Science and Technology (2021) Simons-Berkeley Research Fellowship (2015-2016) CCF Committee Member for Theoretical CS and Big Data (2021) Grants and Projects: USTC Innovation Group Grant: 'Toward Electronic Design Automation: Theories and Algorithms from AI' (2021) MOST Young Investigator Grant: 'Optimal Transportation in Medical Imaging' (3M RMB, 2021) Research Group: Leads the DIAG group with focus on geometric algorithms for data intelligence. Current team includes 6 PhD students and 15 Master's students working on problems in clustering, distributed optimization, and biomedical applications. Former students hold positions at Alibaba, ByteDance, and academic institutions.

• Algorithms
• Computational Geometry
• Machine Learning
• +4 more

Xiao ZHANG is a Faculty Tutor at the School of Civil Engineering, Shandong University, specializing in advanced robotics, teleoperation systems, and machine learning applications in manufacturing. Their work bridges human-robot interaction, surgical robotics, and materials engineering. Research focuses on improving precision in telemanipulation, optimizing additive manufacturing processes, and developing context-aware robotic systems for healthcare and industrial automation. Key research interests include gaze-based control mechanisms, intent recognition in human-robot collaboration, and AI-driven quality assessment in manufacturing. Xiao ZHANG has extensively explored applications of neural networks and multi-agent systems to enhance robotic dexterity and safety, particularly in scenarios requiring real-time adaptation like laparoscopic surgery and autonomous vehicles. Recent Trends: Recent publications emphasize integrating machine learning with traditional manufacturing processes (e.g., laser additive manufacturing) and advancing human-centric robotics through shared control frameworks. Awards: No specific awards listed, but contributions span over two decades of peer-reviewed work. Grants/Advising: No explicit grants or student advisees mentioned in the provided text. Labs/Teams: Affiliated with robotics and manufacturing research groups at Shandong University, focusing on experimental and computational methods.

• Robotics
• Teleoperation
• Machine Learning
• +3 more

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.

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Applied Machine Learning
• +4 more

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.

• Bipedal Robotics
• Humanoid Robotics
• Powered Prosthesis
• +5 more

He Kong is an Associate Professor at Southern University of Science and Technology (SUSTech), affiliated with the School of Automation and Intelligent Manufacturing, where he also serves as Deputy Director of the SUSTech Institute of Robotics. Previously, he was an Assistant Professor in the Department of Mechanical and Energy Engineering at SUSTech from January to May 2022. Prior to joining SUSTech, he was a Research Fellow at the Australian Centre for Field Robotics, University of Sydney (2016-2021), and at Cranfield University's Advanced Vehicle Engineering Centre (2015-2016). He received his Ph.D. in Electrical Engineering from the University of Newcastle, Australia (2014), M.E. in Control Science and Engineering from Harbin Institute of Technology (2010), and B.E. in Electrical Engineering and Automation from China University of Mining and Technology (2004). His research focuses on robotic intelligent perception and decision making, robot audition, optimal filtering and estimation, and advanced control methods. Specifically, he works on active multi-mode perception, parameter calibration of robot audition systems, optimal filtering under unknown inputs, and fully actuated system approaches. His work has significant applications in precision agriculture, environmental monitoring, and robotic inspection of hazardous industries such as chemical and mining operations. His recent publications reveal a strong emphasis on multi-modal perception systems, particularly combining visual and auditory sensing for robotic applications. His research shows a progression from theoretical control methods toward practical implementations in field robotics, with increasing focus on real-world applications in agriculture and hazardous environments. Finalist for Youth Author Prize, IFAC Workshop on Robot Control (2019) Fifth China Robotics Academic Annual Conference Best Poster Award (2024) 14th International Conference on Indoor Positioning and Indoor Navigation Best Paper Award (2024) The Equity Scholarship, Council of International Students Australia (2011) Outstanding Postgraduate Students Award, Harbin Institute of Technology (2010) Professor Kong actively supervises numerous PhD and Master's students and has established a productive research group focused on active intelligent systems. His laboratory is equipped with advanced facilities including over 30 motion capture systems, Unitree humanoid robots, robot dogs, wheeled mobile robots, and custom-developed platforms like Cubli and acoustic perception systems. He serves on editorial boards for several prestigious journals including IEEE Robotics and Automation Letters and IEEE Sensors Letters, and has been an Associate Editor for major robotics conferences such as IEEE ICRA and IEEE/RSJ IROS.

• Robotics
• Robot Audition
• Optimal Filtering and Estimation
• +4 more