Find Academic Supervisors

Dr. Yi-Feng Chen is a Research Assistant Professor and Master's Supervisor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech as a postdoctoral fellow in November 2020 and was promoted to Research Assistant Professor in February 2023. His academic journey includes interdisciplinary training across engineering, neuroscience, and biomedical applications. Dr. Chen's educational background includes: Ph.D. in Engineering from Wuhan University of Technology (2014-2017), supervised by Professor Quan Liu M.Sc. from Wuhan University of Technology (2011-2014), supervised by Professor Zhou Zude B.Sc. from Wuhan University of Technology (2007-2011) He also participated in exchange programs at Yuan Ze University in Taiwan (2012) and the University of Auckland in New Zealand (2015). Dr. Chen's research spans the intersection of biomedical engineering, neuroscience, and artificial intelligence, with particular focus on brain-computer interfaces and rehabilitation technologies. His work combines advanced signal processing techniques with clinical applications, especially in decoding neural signals for movement intention and monitoring brain states during anesthesia. His research has significant implications for neurorehabilitation, assistive technologies, and intraoperative neurophysiological monitoring. His recent publications demonstrate a clear trajectory toward increasingly sophisticated neural decoding techniques, with a focus on coordinated limb movements and practical rehabilitation applications. The work shows progression from basic EEG signal processing to complex bimanual movement decoding and robot-assisted rehabilitation systems, reflecting a translational research approach from basic science to clinical applications. Dr. Chen has secured significant research funding as principal investigator and core contributor on multiple projects: National Natural Science Foundation of China Youth Science Fund Project (2024-2026) Guangdong Natural Science Foundation General Project (2024-2026) Ministry of Science and Technology National Key R&D Program Project (2023-2026) Shenzhen Science and Technology Innovation Commission Key Project (2022-2025) As a Master's Supervisor, Dr. Chen mentors graduate students in biomedical engineering with focus on neural engineering and rehabilitation robotics. His laboratory collaborates closely with clinical partners to ensure research relevance to real-world medical challenges, particularly in neurorehabilitation and intraoperative monitoring.

• Brain-computer interface
• Rehabilitation Robotics
• 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

Zhan Ma is a Professor and PhD Advisor at the School of Electronic Science and Engineering, Nanjing University. He leads research in Neural Video Communication, Smart Cameras, and Computational Vision Models. His work focuses on end-to-end learning for compression, networking, and hardware-software co-design. Dr. Ma holds a PhD from New York University's Tandon School of Engineering (2010), and prior to his current role, he served as Senior Staff Researcher at Huawei (2013-2015) and Senior Researcher at Samsung (2011-2013). Research highlights include pioneering work in point cloud compression (adopted into IEEE standards) and dual-camera systems for high-resolution video acquisition. His algorithms are deployed in WeChat/WeChat Video for rate-quality optimization and in ISO standards for video complexity indicators. Recent work emphasizes machine learning-driven approaches for image/video compression and adaptive streaming frameworks. Honors include the 2023 IEEE CAS Society Outstanding Young Author Award and multiple best paper awards at IEEE WACV, BMSB, and other venues. He leads the Vision Lab at Nanjing University and collaborates with industry partners on practical implementations of his research.

• Neural Video Communication
• Smart Cameras
• Computational Vision Models
• +3 more

Professor Liu Hongyan is a full-time Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management, where he has served since 1994, achieving the rank of Professor in 2011 after previously holding positions as Associate Professor (2003-2011) and Teacher. His research bridges theoretical data science with practical applications across e-commerce, healthcare, and social media platforms. Education: PhD in Management, School of Economics and Management, Tsinghua University (2001) His research focuses on big data management , machine learning , and business intelligence with specialized expertise in personalized recommendation systems , medical/financial data analysis , and computer vision applications . Recent work integrates large language models and causal inference to solve complex problems in short video platforms, live streaming, and healthcare analytics, emphasizing real-world impact through industry collaborations. Analysis of his 15 most recent publications (2023-2025) reveals a strong trajectory toward multimodal AI systems combining recommendation engines with computer vision, particularly in 3D animation for advertising and healthcare. Key trends include LLM-enhanced display advertising, emotion-aware facial animation, and medical image annotation using adversarial learning, while maintaining core contributions to behavioral data mining in social networks. Scientific recognition includes: National Archives Administration's Outstanding Scientific and Technological Achievement Award Multiple Best Paper Awards at international conferences Outstanding Doctoral Dissertation Supervisor designation from the Society for Management Science and Engineering Special Award for National Natural Science Foundation project on user behavior pattern discovery Professor Liu has secured leadership roles in major National Natural Science Foundation projects including Innovation Research Groups and international cooperation initiatives. His industry impact is demonstrated through patented recommendation systems adopted by multiple companies, particularly in personalized content delivery for live streaming and short video platforms. As an Outstanding Doctoral Dissertation Supervisor, he mentors the next generation of data science researchers. He serves as Deputy Director of Tsinghua University's Center for Artificial Intelligence and Management Research and holds key positions in national academic societies including the E-Commerce and Cyberspace Management Committee (China Management Modernization Research Association) and the Information Systems Engineering Committee (Chinese Society for Systems Engineering).

• Big data management and analysis
• machine learning
• data mining
• +7 more

Kun An is a Professor at the Department of Traffic Information and Control Engineering within the College of Transportation Engineering at Tongji University. She has previously held academic positions at Monash University (Senior Lecturer, 2018–2019; Lecturer, 2016–2018) and conducted postdoctoral research at the University of Illinois at Urbana-Champaign (2015–2016) and The Hong Kong University of Science and Technology (2014–2015). Her academic journey includes a PhD in Civil Engineering from HKUST (2014) and a Bachelor's degree from Tongji University (2009). PhD: Civil Engineering, The Hong Kong University of Science and Technology (2014) Bachelor's: Transportation Engineering, Tongji University (2009) Dr. An specializes in intelligent transportation systems, focusing on optimizing complex traffic networks, characterizing traveler behavior in stochastic environments, and advancing electric vehicle sharing infrastructure. Her research spans urban transit planning, carsharing logistics, battery electric bus deployment, and real-time traffic signal optimization, with applications to mitigate rail disruptions and enhance multimodal connectivity. Her publications emphasize solving transport challenges through stochastic programming, game theory, and behavioral analysis. Key themes include autonomous vehicle integration, demand uncertainty modeling, and sustainable mobility solutions. Awards include the Hong Kong PhD Fellowship and multiple best paper recognitions. Hong Kong PhD Fellowship (2010–2014) TRBADB30 Best Paper Nomination (2014) Best Paper at 18th Hong Kong Transportation Annual Meeting (Second Author, 2013)

• Intelligent Transportation Systems
• Traffic Network Optimization
• Stochastic Demand Modeling
• +3 more

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.

• Model Checking
• Bounded Model Checking
• Formal Methods
• +7 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. Lili Wang serves as an Associate Professor at Southern University of Science and Technology (SUSTech), joining in May 2024 as an independent Principal Investigator and PhD advisor. Her research focuses on theoretical and applied control systems for intelligent sensor networks through interdisciplinary integration of control theory, optimization, and machine learning. Her academic foundation includes: PhD in Engineering and Applied Science from Yale University (2014-2020) Master's in Control Theory and Control Engineering from Zhejiang University (2011-2014) Bachelor's in Automation from Zhejiang University (2007-2011) Dr. Wang's research spans social dynamics , distributed estimation , distributed control , formation control for multi-agent systems , and distributed learning applications . Her work develops novel algorithms for networked control systems with applications in intelligent sensor networks and multi-robot coordination, emphasizing theoretical rigor and practical implementation. Analysis of her publications reveals consistent advancement in distributed observer design, formation control strategies, and consensus algorithms. Key trends include innovative applications of graph theory and spectral methods to multi-agent systems, with growing integration of learning techniques for adaptive control in uncertain environments. Her distinguished recognition includes: IEEE Senior Member (2024) Lillian Gilbreth Fellowships at Purdue University (2023) CPS Rising Star Award (2023) Henry Prentiss Becton Graduate Prize (2019) Zhang Siying Outstanding Paper Award (2012) As a PhD advisor at SUSTech, Dr. Wang mentors graduate researchers in control theory and its applications. She is currently founding COL-LABORATORY, a dedicated research team advancing control systems for intelligent sensor networks through interdisciplinary collaboration, with ongoing projects in distributed learning frameworks and robust multi-agent coordination.

• Social dynamics
• distributed estimation and calculation
• distributed control
• +2 more

Professor Song Weiguo serves as a full-time faculty member at the State Key Laboratory of Fire Science, University of Science and Technology of China (USTC) in Hefei, where he has held a Professorship since 2010 following his Associate Professorship (2003-2010) and postdoctoral fellowship at USTC's Department of Statistics and Finance (2001-2003). His international research experience includes a Guest Researcher position at the U.S. National Institute of Standards and Technology (NIST) during 2006-2007. His educational foundation comprises a PhD from USTC (2001) and specialized postdoctoral training in statistical finance. Research interests focus on pedestrian evacuation dynamics, fire system criticality, performance-based building fire protection, and remote sensing fire monitoring, with particular emphasis on complex systems modeling of crowd behaviors and fire spread phenomena. Analysis of his 2007-2011 publications reveals a dominant trend in microscopic pedestrian dynamics (60% of works), especially bottleneck flow, counter-flow, and exit selection behaviors, complemented by fire monitoring via remote sensing (22%) and weather-fire interaction modeling (18%). This portfolio demonstrates rigorous integration of experimental validation with computational modeling across fire safety disciplines. His scientific recognition includes: Huaxia Construction Award for Science and Technology (First-class, 2010) Lu Jia Xi Youth Excellence Prize (Chinese Academy of Sciences, 2009) National Liangxi Forestry Science and Technology Progress Award (Second-class, 2007) National Science and Technology Progress Award (Second-class, 2006) Chinese Academy of Sciences President Award Excellence (2001) Professor Song has secured significant research funding as principal investigator for three National Natural Science Foundation projects, three National Key Technologies R&D Program grants, and additional awards from the Ministry of Science and Technology and Ministry of Education. His role as Communication Expert for China's National Natural Science Foundation and reviewer for top journals like IEEE Transactions on Intelligent Transportation Systems reflects his leadership in fire safety science. Current projects likely involve mentoring graduate students through USTC's laboratory infrastructure. He remains actively engaged at USTC's State Key Laboratory of Fire Science, which serves as China's primary hub for interdisciplinary fire research, collaborating with international bodies like the International Association of Fire Safety Science while advancing evacuation modeling and fire monitoring technologies.

• Pedestrian and evacuation dynamics
• Self-organized criticality of fire system
• Performance-based fire protection design of buildings
• +3 more

Wen-Gui Weng is a Professor at the College of Chemistry and Chemical Engineering at Xiamen University , China. He earned his Ph.D. from Case Western Reserve University in 2007, followed by postdoctoral training at the University of Delaware. His research spans mechanochemistry, supramolecular polymers, and biomedical polymers, with a strong publication record in high-impact journals. Education: Ph.D. in Chemistry, Case Western Reserve University (2007) M.S. in Chemistry, Huaqiao University (2003) B.S. in Chemistry, Huaqiao University (2000) Postdoctoral Fellow, University of Delaware Research Interests: His work focuses on reactive polymers , supramolecular polymers , topological polymers , polymer nanocomposites , and biomimetic and biomedical polymers . He is particularly known for developing mechanoresponsive materials that can sense and respond to mechanical stimuli, including self-healing polymers and mechanochromic systems. His research group explores force-induced chemical reactions at the single-molecule level and designs dynamic covalent networks with tunable mechanical and optical properties. These materials have applications in smart coatings , biomedical devices , and adaptive materials . Scientific Contributions: Prof. Weng has published over 27 peer-reviewed articles in top-tier journals such as Nature Communications , Journal of the American Chemical Society , and Angewandte Chemie . His work is highly cited and recognized for advancing the field of polymer mechanochemistry . Contact: Email: wgweng@xmu.edu.cn Phone: +86 (0592) 2182430 Address: College of Chemistry and Chemical Engineering, Xiamen University, Fujian Province, 361005, China

• Reactive polymers
• Supramolecular polymers
• Topological polymers
• +5 more

Shi-Di Huang is a Tenured Associate Professor at the Department of Mechanics and Aerospace Engineering under the College of Engineering at Southern University of Science and Technology (SUSTech) , Shenzhen, China. He joined SUSTech in 2017 as Assistant Professor and was promoted to Associate Professor in 2018, with tenure since 2023. 2005–2009 : Bachelor of Science (Physics) at Fudan University 2009–2014 : Doctorate (Physics) at The Chinese University of Hong Kong Research Directions : Turbulence : Boundary layer dynamics, turbulent coherent structures, small-scale statistical characteristics, and applications in heat transfer and pollutant dispersion. Physical Oceanography : Laboratory and numerical modeling of diapycnal mixing, river plumes, water-sediment interface interactions, and wave-vortex coupling. Active Matter : Swimming dynamics of plankton/micro-robots, collective motion, hydrodynamic interactions, and environmental responses. Nonlinear Dynamics & Complex Networks : Chaos theory, pattern formation, and network analysis in fluid systems. Scientific Trends : His recent work focuses on turbulence scaling laws, centrifugal force effects in convection, Prandtl number dependencies, and aerodynamic phenomena in urban/transport systems. Collaborative studies with Ke-Qing Xia and others explore coherent structures, transport enhancement, and geophysical/environmental applications. Scientific Awards : 2019 Guangdong Province Pearl River Talent Program 2017 Shenzhen Peacock Program 2016 Hong Kong Young Scientist Award 2014 Chinese University of Hong Kong Young Scholars Thesis Award 2009 Shanghai Outstanding Graduate Research Leadership : He has directed four National Natural Science Foundation of China (NSFC) projects and actively recruits students/postdocs for turbulence, ocean dynamics, and active matter research. He contributes to academic service as a reviewer for journals like Physical Review Letters and Journal of Fluid Mechanics , and served as guest editor and session chair.

• Turbulence
• Natural Convection
• Physical Oceanography
• +7 more

Yu He is a Researcher at the Institute of Quantum Science and Engineering, Southern University of Science and Technology (SUSTech) in Shenzhen, China, where he leads research on silicon-based quantum computing and quantum simulation. He joined SUSTech in March 2020 after international postdoctoral training. Education: Ph.D. in Physics, University of Science and Technology of China (2009-2015) B.S. in Atomic, Molecular and Optical Physics, University of Science and Technology of China (2005-2009) Research Focus: Dr. He specializes in semiconductor quantum technologies with emphasis on silicon-based atomic-scale devices, quantum simulation, and quantum networks. His work bridges experimental quantum optics, solid-state physics, and quantum information processing, particularly through quantum dot systems and dopant atoms in silicon for developing scalable quantum computing architectures. Publication Trends: His 27 publications demonstrate consistent focus on experimental quantum information science. Early work centered on high-purity single-photon sources using quantum dots. Recent breakthroughs include pioneering silicon-based quantum gates and quantum simulation platforms. Publications in Nature and Physical Review Letters frequently explore quantum measurement techniques, photonic quantum walks, and semiconductor qubit control. Awards and Honors: MIT Technology Review's 35 Under 35 Innovators (China) - 2020 Top Ten Quantum Computing Experiments in the World - 2019 Top Ten Scientific and Technological Advances in China - 2017 Laboratory Development: He directs a specialized quantum device laboratory at SUSTech developing STM-based nanofabrication and millikelvin measurement systems for silicon quantum processors. His team actively recruits postdoctoral researchers and engineers for projects in quantum dot manipulation, cryogenic electronics, and quantum chip design.

• Quantum physics
• Quantum simulation
• Quantum computation
• +4 more

Xiaotian Liu is a Research Associate Professor in the Department of Neurobiology at Southern University of Science and Technology (SUSTech) , where he joined in September 2020. He is affiliated with School of Life Sciences and works within the research group of Academician Mingjie Zhang . His academic background includes a Ph.D. in Bioengineering from HKUST (2015-2019) and a B.Sc. in Biotechnology from USTC (2003-2008). His research focuses broadly on Structural and functional study of protein machinery , Molecular mechanism of biomolecular self-assembly , and Decoding and programming biological condensation formation . His work bridges structural biology, biochemical engineering, and biomaterials science, with a particular emphasis on protein structure-function relationships and their applications in biomedical and nanotechnological contexts. Across his publications from 2010 to 2023, Liu has contributed to understanding protein condensates, developing advanced bioimaging techniques, and engineering protein hydrogels for regenerative medicine. His recent work in Nature and Nature Methods highlights his leadership in fungal enzyme structural analysis and AI-driven microscopy. Hong Kong Technology Talent Scheme Postdoctoral Hub for ITF (2020) Chan Tak Kei & Wong Kwai Ying Bioengineering Student Research Award (2019) HKJEBN Scholarship for Talents (2016-2019) Xiaotian Liu is an active researcher within the Mingjie Zhang’s group , combining structural biology with materials science to address fundamental questions in cellular organization and biomedical engineering.

• Structural and functional study of protein machinery
• Molecular mechanism of biomolecular self-assembly
• Decoding and programming biological condensation formation

Shengcai Liu is an Assistant Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). Holding a PhD in Engineering, he has established himself as a researcher with over 10 publications as first or corresponding author in top artificial intelligence conferences and journals. His academic journey includes research positions at Singapore's Agency for Science, Technology and Research (A*STAR) before joining SUSTech's faculty. Education: PhD in Computer Science and Engineering, University of Science and Technology of China (2014-2020) Bachelor's in Computer Science and Engineering, University of Science and Technology of China (2010-2014) Dr. Liu's research centers on automatic algorithm design and evolutionary approaches to machine learning. He investigates how AI systems can autonomously construct efficient algorithms for complex optimization problems while maintaining robustness and reliability. His work bridges theoretical foundations with practical applications in combinatorial optimization, adversarial machine learning, and large language model development, with particular focus on making algorithm design processes more systematic and effective. His publication record demonstrates a clear trajectory from foundational work on algorithm portfolios to sophisticated integration of evolutionary methods with large language models. Recent work shows increasing focus on textual adversarial attacks, combinatorial optimization, and leveraging large language models as optimization tools themselves. This progression reflects both theoretical depth and practical relevance to current AI challenges. Professional Activities: Project Leader for Huawei-funded research on combinatorial optimization solvers (CNY 400,000) Member of IEEE, IEEE CIS, and AAAI Reviewer for top journals including TPAMI, TEVC, TCYB Conference reviewer for NeurIPS, ICML, AAAI, IJCAI Dr. Liu maintains active research collaborations through the Artificial Intelligence RAMS Technology Innovation Laboratory at SUSTech, where his team focuses on developing next-generation automatic machine learning systems with applications in optimization and security domains.

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

Xiuxiu Li is a Research Assistant Professor at the Department of Chemistry , Southern University of Science and Technology . She earned her Ph.D. in Organic Chemistry (2018) and B.Sc. in Chemistry Base Class (2013), both from Wuhan University , under the supervision of Prof. Xumu Zhang. Her research focuses on asymmetric catalysis, particularly asymmetric hydrogenation and hydroformylation reactions. Doctoral Degree: Wuhan University, Organic Chemistry (2013-2018) Bachelor's Degree: Wuhan University, Chemistry Base Class (2009-2013) Her work spans asymmetric catalysis, reaction-diffusion networks, and selenium-based catalytic systems. Recent publications highlight advancements in regio- and enantioselective hydroformylation, asymmetric hydrogenation of complex substrates, and novel catalytic oscillator designs. She has received multiple awards including the 2021 FGS Dean Excellence Fellowship and the 2017 National Scholarship for PhD Students. 2021 Weizmann Institute FGS Dean Excellence Fellowship 2018 Wuhan University Academic Innovation Second Prize 2017 National Scholarship for PhD Students 2017 Wuhan University Bangyang Luo Jia Honorary Title Her research group at SUSTech's Grubbs Institute explores catalytic mechanisms for chiral compound synthesis, with collaborations across multiple institutions including Weizmann Institute and Wuhan University.

• Asymmetric Catalysis
• Organic Chemistry
• Reaction-Diffusion Systems
• +4 more