Dr. Li Ying is an Associate Professor and Doctoral Supervisor at the Department of Marine Science and Engineering, Southern University of Science and Technology (SUSTech), where she directs the Center for Marine Atmospheric Sciences. Her research focuses on atmospheric aerosol remote sensing , secondary pollution numerical modeling , and ocean-atmosphere interaction studies under climate change contexts. She holds a PhD in Atmospheric Environmental Science from the Hong Kong University of Science and Technology (2011), MSc in Atmospheric Science from Peking University (2006), and BSc in Physics from Bohai University (2002) with joint study at Tsinghua University. Her professional journey includes roles as Research Assistant Professor at HKUST (2015-2016) and Postdoctoral Fellow at HKUST (2011-2015), with visiting scholar experience at the University of Tennessee (2008-2009). Her research group develops satellite-based remote sensing algorithms for aerosol components (PM2.5, black carbon), studies ozone formation mechanisms in the Pearl River Delta, and creates novel emission control evaluation methods . She leads major projects including Shenzhen Smart City Atmospheric Monitoring and Guangdong-Hong Kong-Macao Air Quality Research Team . Scientific achievements include 60+ SCI publications in journals like Remote Sensing of Environment and Environmental Science & Technology Over 2,400 citations and H-index 24 Two ESI Highly Cited Papers Recipient of Shenzhen Overseas High-level C Talent (2017)
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.
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.
Siteng Fan is an Associate Professor in the Department of Earth and Space Sciences at Southern University of Science and Technology. His research focuses on planetary atmospheres, exoplanet observations, and Martian atmospheric dynamics, combining observational data from missions like TGO and EMM with climate models such as Mars PCM. Education: B.S. in Space Science and Technology from Peking University (2015), Ph.D. in Planetary Science from Caltech (2020). Research Interests: Dynamics of planetary atmospheres (Mars, Titan, Pluto), photochemistry, microphysical processes, and light curve analysis for exoplanet surface inversion. Scientific Awards: Xie Yibing Young Scientist Award (2023), Marie Skłodowska-Curie Postdoctoral Fellowship (2022), NSFC Excellent Young Scientists (Overseas) (2022). Publication Trends: Recent work spans Mars climate modeling, Pluto/Titan atmospheric haze, exoplanet proxy studies, and radiative transfer algorithms.
Maggie Wenjing Liu is a tenured Associate Professor in the Department of Marketing at Tsinghua University's School of Economics and Management (SEM). She holds leadership roles as Associate Director of the Computational and Behavioral Science Lab and China Retail Research Center, and Chair of the Marketing Department's Academic Seminar Committee. Education: Ph.D. in Marketing, Rotman School of Management, University of Toronto M.S. in Marketing, NUS Business School, National University of Singapore Bachelor in Economics, University of International Business and Economics Research Focus: Her work centers on consumer behavior , product/service experience , decision science , and pricing , examining psychological mechanisms through experimental and analytical methods. Key themes include ritualistic consumption, AI-human interaction in marketing, visual branding effects, and cross-cultural service responses. Publication Trends: Recent work (2017-2025) appears in top journals like Production and Operations Management (UTD) and Journal of Consumer Psychology (FT50), revealing strong interdisciplinary connections between behavioral science, marketing strategy, and data-driven consumer insights with increasing focus on AI applications and crisis response. Scientific Awards: National Natural Science Foundation Youth Project Outstanding Evaluation (2017) National Natural Science Foundation General Project Outstanding Evaluation (2021) Tsinghua University teaching reform project Grade A (2020) Beijing Higher Education Young Elite Scholar Award Two-time Outstanding Expert Reviewer (Journal of Marketing Science) Society of Asian Retailing and Distribution Best Paper Award Advising & Grants: As a doctoral advisor, she mentors PhD candidates who frequently co-author publications. She secured three National Natural Science Foundation General Programs, one Youth Project, three provincial/ministerial grants, and multiple Tsinghua projects, with two NSFC projects rated Outstanding upon completion. Labs & Leadership: She co-directs Tsinghua SEM's Computational and Behavioral Science Lab and China Retail Research Center, driving research at the intersection of marketing analytics, consumer neuroscience, and retail innovation while serving on editorial boards for Marketing Letters and Journal of Business Research .
Professor Jinyan Fan is a faculty member in the Department of Psychology at Auburn University, USA, holding the academic rank of Professor. She maintains an active research profile with international collaborations, recently presenting a workshop on AI applications in talent assessment at Tsinghua University's School of Economics and Management in Beijing on December 7, 2023. Her research integrates psychological principles with computational methodologies, focusing on Artificial Intelligence and Machine Learning applications within Talent Assessment frameworks. This work bridges Organizational Behavior and Personnel Psychology with cutting-edge technology to innovate human resource evaluation systems, reflecting her expertise in both psychological theory and data-driven analytical approaches.
Wei Hu is a full Professor and Ph.D. supervisor in the Department of Computer Science and Technology at Nanjing University, China. He earned his Ph.D. and B.S. from Southeast University in 2009 and 2005, respectively, and joined Nanjing University faculty in 2009. He has held visiting positions at Stanford University (2014-2015), University of Texas at Arlington (2016-2017), and University of Toronto (2017) as a visiting scholar/professor. Research Interests focus on Knowledge Graphs : Representation learning, foundation models, and error detection Databases : Entity alignment, crowdsourcing, and blockchain integration Digital Medicine : Collaboration with Nanjing University's National Institute of Health Data Science Publication Trends show expertise in knowledge graph reasoning, federated learning, and biomedical applications. Recent works include in-context learning for graph reasoning and blockchain-based data fusion systems. Scientific Awards include Huawei 2025 Challenges Spark Award ASE 2024 Distinguished Paper Award CHIP 2021 Best Paper Award CCKS 2018 Best English Paper Award Nanjing University Study Abroad Program Awardee (2013) IBM China Excellent Student (2008) Advising involves leading the Knowledge Fusion Group at Nanjing University, mentoring 18 current and former Ph.D. and Master's students. Professional services include editorial roles at Transactions on Graph Data and Knowledge and Big Data Research , plus committee memberships in CCF, CIPSC, and JSCS.
Zhuoren Jiang is a tenure-track Assistant Professor and Doctoral Supervisor at the Department of Information Resources Management, School of Public Affairs, Zhejiang University. He holds a Ph.D. and serves as a key faculty member in the department, with his office located in Room 612-2 of the School of Public Affairs building at Zhejiang University's main campus in Hangzhou. Dr. Jiang's research spans multiple interdisciplinary domains, with primary focus on Information Science & Library Science, Computational Social Science, Policy Simulation, and Artificial Intelligence Algorithms and Applications. His work bridges social science, data science, and computer science, creating innovative approaches to understanding complex social phenomena through computational methods. He has developed expertise in graph representation learning, large language models, and their applications in scientific impact prediction, legal reasoning, and policy analysis. His publication record reveals a strong trajectory in high-impact venues, with recent work focusing on multimodal business analysis, legal judgment prediction, policy style measurement, and scientific peer review systems. His research demonstrates an increasing emphasis on integrating large language models with graph-based approaches to solve complex interdisciplinary problems, particularly in the domains of legal informatics and scientific communication. Zhejiang University Teaching Excellence Award, 2024 Best Short Paper Awards Nominee, SIGIR Conference, 2024 Alibaba Research Fellowship, 2019 & 2020 Boya Postdoctoral Fellowship, Peking University, 2016 Best Poster Award, JCDL, 2013 Dr. Jiang actively mentors students and seeks highly self-motivated PhD and master's candidates interested in interdisciplinary research. He has served as principal investigator or sub-project lead for multiple research projects including National Key R&D Program, Major Projects of the National Social Science Foundation of China, and Projects of the National Natural Science Foundation of China. His academic service includes editorial board membership for Frontiers in Big Data and peer reviewing for numerous top journals including Information Processing & Management and Journal of the Association for Information Science and Technology. He maintains active collaborations with industry, having previously served as a consultant (visiting professor) at Alibaba DAMO Academy and currently as a collaborating expert at Tongyi Lab. His professional memberships include ACM (Association for Computing Machinery), ACM SIGIR, ACL (Association for Computational Linguistics), and ASIS&T (Association for Information Science and Technology).
ZHOU Xianghong is a Professor and PhD Advisor at the School of Economics and Management, Tongji University. Currently serving as Assistant Dean, she specializes in urban public service management, medical care, and health policy. Her academic and professional work bridges public administration, poverty reduction, and sustainable development. Doctor of Laws (Sociology), Nanjing University (1999–2002) Post-doctorate in Management Science and Engineering, Tongji University (2002–2005) Her research focuses on macro-level policy analysis, particularly in urban public service management , medical care and health policy , and poverty reduction mechanisms . She has led studies on smart city development, agricultural carbon emission efficiency, and health resource allocation. The 15 most recent publications span interdisciplinary topics, including environmental economics, machine learning for recommendations, and public health governance. Her work emphasizes quasi-experimental methods, regional disparities, and policy evaluation in urban and rural contexts. ZHOU has secured grants from the National Natural Science Foundation of China (NSFC) and Shanghai government agencies for projects on smart city governance, poverty reduction, and environmental policy. She has taught courses across MPA, MBA, and executive programs, focusing on public affairs, policy analysis, and urban innovation.
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.
Guo Xunhua is a Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management. He serves as Party Branch Secretary and holds significant leadership roles including Executive Director and Secretary-General of the China National Association for Information Technology (CNAIS), Vice President of the China Society of Information Economics, and Secretary-General of the Information Management Committee of the China Management Modernization Research Association. His educational background includes dual bachelor's degrees in Management Information Systems and Computer Science and Technology from Tsinghua University (2000), followed by master's and doctoral degrees in Management Science and Engineering from the same institution (2005). He has been teaching at Tsinghua since 2005 and maintains international academic connections through visiting positions at RWTH Aachen University and MIT Sloan School of Management. Guo's research spans Management Information Systems, E-commerce, Social Networks, and Business Intelligence, with a focus on business intelligence methods in internet environments and behavioral patterns under emerging information technologies. His work integrates both 'technology' and 'behavior' perspectives within information systems research. His recent publications reveal a strong trend toward AI applications in e-commerce, personalized recommendation systems, social commerce dynamics, and the psychological aspects of digital interactions. Ministry of Education Changjiang Scholar Young Scholar (2019) Ministry of Education Outstanding Achievement Award for Scientific Research in Humanities and Social Sciences Beijing Philosophical and Social Sciences Outstanding Achievement Award Beijing Higher Education Teaching Achievement Award Multiple Tsinghua University Teaching Achievement Awards Professor Guo serves as Assistant Editor-in-Chief of the Journal of Information Systems and sits on editorial boards for international journals including Information Technology & People, Electronic Commerce Research, and Journal of Global Information Management. His teaching portfolio includes graduate courses in IT and Organization, and undergraduate courses in Principles of Computer Systems, Management Information Systems, and Introduction to Management Science and Engineering Research.
Dr. Ke Gao is an Associate Professor in the Department of Earth and Space Sciences at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in 2019 after completing postdoctoral research at Los Alamos National Laboratory in the United States. Dr. Gao holds a Ph.D. in Rock Mechanics from the University of Toronto, which he obtained in 2017. His educational background includes: 2021–present: Associate Professor, Department of Earth and Space Sciences, Southern University of Science and Technology 2019–2020: Assistant Professor, Department of Earth and Space Sciences, Southern University of Science and Technology 2017–2019: Post Doc, Solid Earth Geophysics, Los Alamos National Laboratory, USA 2012–2017: Ph.D., Rock Mechanics and Rock Engineering, University of Toronto, Canada Dr. Gao's research primarily focuses on rock mechanics and fault mechanics, with particular emphasis on the development of multiphysics coupling models based on the combined finite-discrete element method (FDEM). His work investigates rock fracturing mechanisms, hydraulic fracturing, and the stick-slip characteristics in sheared granular faults. He has made significant contributions to tensor-based statistical methods for characterizing stress variability and heterogeneity in fractured rock masses. His research bridges computational mechanics with earthquake physics, creating innovative approaches to understanding fundamental geological processes. Analysis of Dr. Gao's recent publications reveals a strong focus on computational geomechanics and earthquake physics. His work consistently applies and advances the combined finite-discrete element method (FDEM) to solve complex rock mechanics problems. There's a clear progression from fundamental method development to applications in earthquake source mechanics and hydraulic fracturing. The integration of machine learning techniques with traditional computational methods represents an emerging trend in his recent work, particularly for predicting slip behavior in granular fault systems. Dr. Gao has received several notable recognitions: Best Paper Award at the 7th International Symposium on In Situ Rock Stress (2016) National Overseas High-level Talent Program (Youth) (2020) Shenzhen 'Peacock Plan' B Talents (2021) Dr. Gao serves as principal investigator for multiple research projects funded by prestigious organizations including the National Natural Science Foundation of China, Ministry of Science and Technology key research and development projects, Guangdong Province general projects, and Shenzhen City general projects. He actively mentors graduate students and postdoctoral researchers, recruiting candidates with backgrounds in solid geophysics, rock mechanics, geological engineering, computational mechanics, and related disciplines. His research group provides comprehensive training in both theoretical and experimental aspects of rock mechanics and earthquake physics. Dr. Gao is affiliated with several professional organizations including the American Rock Mechanics Association, American Geophysical Union, International Society for Rock Mechanics, Canadian Geotechnical Society, Society of American Seismology, and ASCE Engineering Mechanics Institute, reflecting the interdisciplinary nature of his work spanning rock mechanics, geophysics, and computational engineering.
Christina (Yang) Gao is an Assistant Professor at the Southern University of Science and Technology (SUSTech) in the Department of Physics under the Faculty of Science. Her research focuses on theoretical investigations beyond the Standard Model, particularly in dark matter phenomenology and quantum systems for dark matter detection. PhD in Physics from University of California, Davis (2012-2018) Bachelor and Master of Physics from University of Oxford (2008-2012) Dr. Gao's work spans multiple high-energy physics domains: Dark Matter Direct Detection Particle Cosmology Quantum Systems in Physics Machine Learning Applications Her publications highlight collaborative efforts in axion detection methods using superfluid helium-3 and superconducting cavities. 2023 Simons Foundation Travel Grant 2009 New College Scholarship Contact: gaoy3@sustech.edu.cn
Dr. Hua Mengyuan is an Associate Professor in the Department of Electronic and Electrical Engineering at Southern University of Science and Technology (SUSTech), where she has been conducting research since September 2018. She was promoted to Associate Professor in May 2024 after serving as an Assistant Professor from 2018 to 2024. Prior to joining SUSTech, she completed a postdoctoral fellowship at The Hong Kong University of Science and Technology (HKUST) from 2017 to 2018. Dr. Hua received her PhD in Electronic and Computer Engineering from HKUST (2013-2017) and earned her B.Sc. in Physics from Tsinghua University (2009-2013). Her academic journey reflects a strong foundation in both physics and electronic engineering, which informs her interdisciplinary research approach. Dr. Hua's research focuses on wide-bandgap semiconductor devices, particularly GaN-based power devices. She investigates device fabrication, material characterization, device physics, and reliability/stability issues. Her work spans fundamental semiconductor physics to practical applications in power electronics. The research encompasses both experimental and computational approaches, including 2D device simulation and modeling. Her group explores novel materials like Ga 2 O 3 monolayers and heterostructures for next-generation electronic applications. Analysis of Dr. Hua's recent publications reveals a strong focus on reliability mechanisms in GaN power devices, particularly threshold voltage instability and dynamic on-resistance issues. Her work increasingly incorporates computational methods, including machine learning potentials for material simulation. There's a clear progression toward exploring novel 2D materials and heterostructures, particularly involving gallium oxide and related compounds, for both electronic and optoelectronic applications. 2017 IEEE ISPSD Charitat Award (Best Young Scholar Award) 2018 Shenzhen Overseas High-Caliber Personnel 2017-2023 IEEE Trans. Electron Devices Golden Reviewer 2018-2023 IEEE Electron Device Lett. Golden Reviewer 2020 IEEE ICSICT Outstanding Youth Scientific and Technological Paper Award National Special Support Program for High-level Talents, 2023 Dr. Hua actively mentors students and researchers, recruiting PhD students, master's students, research assistants, and postdoctoral fellows to her group. Her research has been supported by provincial and ministerial projects, including two projects she has presided over. She serves as an editor for IEEE Trans. Electron Devices and has applied for six invention patents. Her group is currently working on next-generation wide-bandgap semiconductor device research with applications in power electronics and miniaturized power supplies. Dr. Hua's research group operates within the College of Engineering at SUSTech, utilizing advanced semiconductor fabrication and characterization facilities. Her team collaborates with international researchers, as evidenced by publications with colleagues from institutions like HKUST. The group maintains strong connections with the global semiconductor research community, regularly presenting at leading conferences including IEDM and ISPSD.
Yuhui Hu is an Associate Professor and Doctoral Supervisor in the Department of Pharmacology, School of Medicine at Southern University of Science and Technology (SUSTech) in Shenzhen, China. She joined SUSTech in August 2016 as an independent PI and was promoted to Associate Professor in the Department of Pharmacology in March 2022, having previously served as a Research Associate Professor in the Department of Biology, School of Life Sciences at SUSTech from 2016-2022. Dr. Hu earned her Ph.D. in Functional Genomics from the Max Planck Institute for Molecular Genetics in Berlin, Germany in 2006. Prior to joining SUSTech, she led her own research group on pharmacogenomics and chemical genetics at the same institute from 2007-2009, then moved to the Berlin Institute for Medical Systems Biology (BIMSB) at the Max Delbrück Center for Molecular Medicine where she served as a Senior Research Scientist until 2016. Her research focuses on integrating diverse systems biology approaches in transcriptomics, genomics/epigenomics, proteomics, CRISPR gene editing, single-cell and spatiotemporal omics to decipher gene regulations in cancer precision medicine and developmental biology. She has pioneered high-throughput platforms for protein subcellular localization, apoptosis detection, and protein-protein interaction analysis. Her work bridges traditional Chinese medicine compounds with modern systems biology approaches to develop new drugs, particularly through the investigation of natural compounds for anticancer and anti-COVID applications. Her recent publications show a strong focus on cutting-edge single-cell and spatial transcriptomics technologies, with multiple high-impact papers in journals like Cell, Nature Communications, and Developmental Cell. Her research group has developed several innovative protocols for sample multiplexing and cell death quantification compatible with single-cell sequencing. Excellent Patent Award, Southern University of Science and Technology (2020) Industrial Development and Innovative Talent Award, Shenzhen (2021) Shenzhen Peacock Talent Award As a Doctoral Supervisor, Dr. Hu leads a research group that has secured significant funding, including six national/provincial projects totaling over ¥7.14 million. She serves as a core PI in the Shenzhen Key Laboratory of Gene Regulation and Systems Biology and holds several academic society positions, including committee member of the division of anti-inflammatory and immunological pharmacology of the Chinese Pharmacological Society, council member of Guangdong Pharmacological Society, and executive director of Guangdong Overseas Returnees Association. Her lab continues to develop novel systems biology approaches for gene regulation and pharmacology research, with a particular focus on cancer genomics and chemical genomics for early diagnosis, therapy, and prognosis.
