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Jianfeng ShiView profile

Professor

• Non-metallic and composite pressure equipment
• Advanced testing and sensing techniques
• Nondestructive testing
• +3 more

Jianfeng Shi is a Professor at the College of Energy Engineering, Zhejiang University. He serves as a Doctoral Supervisor and Deputy Director of the Institute of Process Equipment. His research focuses on non-metallic and composite pressure equipment , advanced testing techniques , and nondestructive safety assessment . Research Interests: Design of extreme-service pressure equipment, structural sensing technologies, and safety evaluation of polymeric pipelines in nuclear and hydrogen energy systems. Article Trends: Recent publications emphasize advanced composite materials non-destructive testing with AI hydrogen transportation safety finite element modeling embedded sensor systems Awards: Zhejiang Province Science and Technology Progress Award (1st prize) China Petroleum and Chemical Industry Federation Technology Invention Award (2× 1st prize) ASME Outstanding Technical Paper Awards

Wang ZiganView profile

Associate Professor

• Finance
• Economics
• Applied Microeconomics
• +13 more

Wang Zigan is a Tenured Associate Professor in the Department of Finance at Tsinghua University's School of Economics and Management, with additional appointments at the Shenzhen Institute of Economics and Management and the Institute of Innovation Management at Tsinghua University Shenzhen International Graduate School. His research spans international finance, corporate finance, and environmental economics, with a focus on empirical analysis of financial markets and corporate behavior. Education PhD in Economics, Columbia University (2009-2015) Master of Philosophy in Economics, Columbia University (2009-2012) Master of Arts in Economics, Columbia University (2009-2011) Bachelor of Economics, Tsinghua University (2005-2009) Research Focus Professor Wang's research examines critical intersections between financial markets and real economic activity. His primary expertise lies in foreign exchange dynamics, corporate investment behavior under regulatory constraints, and banking network structures. He employs advanced computational methodologies including machine learning and network analysis to address complex questions in international finance and corporate governance, with particular attention to how institutional contexts shape market outcomes. Publication Trends His recent scholarly output demonstrates a sophisticated integration of traditional finance theory with cutting-edge data science techniques. Wang's work increasingly examines how digital transformation impacts financial markets, with growing emphasis on financial technology security and high-dimensional econometric methods. His publications in premier journals including Journal of International Economics and Journal of Financial Economics reveal consistent methodological innovation while addressing fundamental questions about market efficiency and corporate decision-making. Professional Experience Assistant Professor, School of Business, The University of Hong Kong (2015-2023) Visiting Professor, State University of New York (2014) Teaching Responsibilities At Tsinghua University, Professor Wang teaches graduate-level courses including Seminars on Advanced Topics in Finance, Research on Frontier Issues in Finance, and Cryptography and Network Security. His curriculum emphasizes both theoretical foundations and practical applications of computational methods in modern finance, preparing students for careers at the intersection of finance and technology.

JI JieView profile

Professor

• Fire Plume Behavior
• Combustion of Liquids and Solids
• Fire Spread
• +3 more

Professor JI Jie serves as Professor and Vice Director of the State Key Laboratory of Fire Science at the University of Science and Technology of China (USTC), with additional leadership roles in the Engineering Research Center of Thermal Safety and National & Local Joint Engineering Research Center of Thermal Safety Technology. Education: Bachelor of Science, University of Science and Technology of China, 2002 Ph.D. in Engineering, University of Science and Technology of China, 2008 Postdoctoral Research, University of Science and Technology of China, 2008-2010 His research pioneers fire plume dynamics, combustion of complex materials, fire spread mechanisms in ancient/high-rise structures, and real-time fire forecasting using data assimilation techniques. Current work focuses on integrating experimental and numerical methods for fire risk assessment in tunnels, subways, and cultural heritage sites. Recent publications (2021-2023) demonstrate expertise across fire dynamics, smoke management, and safety engineering, with significant contributions to understanding wind-driven fires, multi-fire interactions, and real-time smoke prediction systems using Ensemble Kalman Filter approaches. Honors and Awards: Elected fellow of the Combustion Institute (2022) Highly Cited Chinese Researcher (Elsevier 2020, 2021) National Science and Technology Innovation Leadership Talent (2018) Excellent Young Scientists Fund recipient (2017) Multiple national teaching and research awards including China Fire Protection Association First Prize (2019) Professor Ji has secured major funding including the National Science Fund for Excellent Young Scholars and multiple National Natural Science Foundation projects, while supervising award-winning research teams. His lab implements fire safety standards in national construction projects and international protocols. He leads USTC's State Key Laboratory of Fire Science research team, focusing on experimental combustion studies, computational fire dynamics, and practical fire safety engineering applications for complex infrastructure.

• Data-driven decision optimization
• Supply chain management
• Emergency management
• +5 more

Professor Yu Guodong is a faculty member in the Department of Project Management and Industrial Engineering at Shandong University's School of Management. He holds the title of Qilu Young Scholar and leads an excellent young innovation team. His research focuses on data-driven decision optimization , particularly in small data environments, with applications in supply chain management, emergency response, and industrial systems. He has pioneered methods like Wasserstein distributionally robust optimization and fairness-aware resource allocation frameworks. His work addresses challenges in high-end manufacturing, emergency logistics, and low-probability event scenarios. Yu has published extensively in top-tier journals including Manufacturing & Service Operations Management , Production and Operations Management , and INFORMS Journal on Computing . He has secured 12 national and provincial grants, including key projects from the National Natural Science Foundation of China. Collaborations with industry leaders like Weichai Power and Jereh Petroleum Equipment demonstrate his applied research impact. His team develops computationally efficient algorithms (e.g., Benders decomposition, Branch-and-Benders-cut) to solve complex optimization problems under uncertainty. Notable contributions include integrating inverse optimization with historical data for contract pricing, quantifying fairness-efficiency trade-offs via robust optimization, and designing resilient service networks for emergencies. His research bridges theoretical advancements and practical implementation, enhancing decision-making resilience in data-scarce scenarios.

Rentai LIUView profile

Professor

• Geotechnical Engineering
• Grouting Technology
• Underground Engineering
• +2 more

Rentai LIU is a Professor and doctoral supervisor at Shandong University's School of Civil Engineering, affiliated with the Geotechnical and Structural Engineering Center. He earned his Ph.D. in Geotechnical Engineering from Shandong University in 2012 and subsequently joined the faculty, with a postdoctoral stint at Shandong Energy Group (2012-2015). His research focuses on controlling geological hazards in underground engineering and developing innovative grouting technologies. Research Interests: Dr. LIU specializes in dynamic water grouting theory, quantitative grouting design for water-rich strata, and novel material development. His work addresses practical challenges like water inrush disasters, sand layer stabilization, and rock mass reinforcement. Key innovations include cement-based anti-dispersion grouts and expansion materials for high-pressure water surges, applied in major infrastructure projects across China. Research Trends: His publications (2011-2019) predominantly explore grouting material properties, underground water control, and geomechanical modeling. Recent work emphasizes computational simulations, subsea tunnel risks, and sustainable materials like pervious concrete. Awards & Honors: Second Prize, Shandong Provincial Science and Technology Progress Award (2019) Second Prize, China Coal Association Science and Technology Award (2018) Second Prize, Ministry of Education Technology Invention Award (2017) Second Class, China Highway and Transportation Society Award (2016) First Prize, Shandong Provincial Science and Technology Progress Award (2015) First Prize, Ministry of Education Science and Technology Progress Award (2014) Grants & Projects: Dr. LIU leads multidisciplinary projects including: National Natural Science Foundation grants on grouting reinforcement mechanisms (¥610K active) National Key R&D Program sub-projects on infrastructure resilience (¥300K) 14 industry collaborations with Qingdao Metro, China Railway Bureaus, and Shandong Energy Group (total value ~¥4.5M), addressing tunnel safety and material R&D. Laboratory & Advising: He directs research at the Geotechnical Engineering Center and mentors doctoral candidates in geotechnical engineering, emphasizing practical solutions for underground construction hazards.

刘潇View profile

Professor

• Experimental Economics
• Behavioral Economics
• Computational Economics

刘潇 is a Professor in the Department of Economics at Tsinghua University's School of Economics and Management. She joined Tsinghua in 2012 as an Assistant Professor and advanced through roles including Associate Professor (2016-2021), Tenured Associate Professor (2021-2025), and Professor (2025-). She holds a Bachelor's degree from Renmin University of China (2006) and a PhD from the University of Michigan (2012). Her research focuses on Experimental Economics , Behavioral Economics , and Computational Economics , utilizing field experiments and game theory to analyze human decision-making. Recent work explores AI-driven economic rationality, incentive structures in digital platforms, and behavioral interventions in social policies. Her publications emphasize experimental methodologies applied to diverse contexts like online education, labor incentives, and environmental markets. Trends include increasing integration of computational tools and cross-disciplinary collaboration with computer science. Awards & Honors: Advanced Worker, Tsinghua University (2021) Advanced Worker, Tsinghua SEM (2014, 2020) Outstanding Student Work Award (2020) Outstanding Class Advisor (2017) Advanced Research Work Award (2014) She serves as Associate Editor for Management Science , Journal of Economic Behavior & Organization , and Journal of Behavioral and Experimental Economics , and holds leadership roles in academic committees including the Economic Science Association.

卫强View profile

Professor

• Big data analysis
• Information systems
• E-commerce
• +4 more

Professor Qiang Wei is a faculty member at the Department of Management Science and Engineering, Tsinghua University School of Economics and Management. With a PhD in Management from Tsinghua (1999-2003), he has held academic positions including Department Head and Deputy Secretary of the Party Branch. His research focuses on big data analysis, information systems, e-commerce, business intelligence, data mining, and management simulation. Doctor of Management, Tsinghua School of Economics and Management (1999-2003) Master of Tsinghua School of Economics and Management (1997-1999) Bachelor of Science, Tsinghua School of Economics and Management (1992-1997) His recent research explores big data analysis , online review systems , and personalized recommendation through machine learning and neural network frameworks. Articles span topics including monetary incentives in crowdsourcing , temporal consistency in review ranking , and product relationship discovery via embeddings . He has published in top journals like Management World, INFORMS Journal on Computing, and Information Systems Research. Key awards include: 2021 Ten Best Papers Award, Management World 2020 Ministry of Education Second Prize in Philosophy and Social Sciences 2018 Theory Contribution Award, China Society for Information Economics He teaches courses such as Information Technology and Management , Big Data and Business Analysis , and Data Structure and Algorithm . His work bridges computational methods with business applications, including cybersecurity analysis, mobile technology impact studies, and competitive intelligence systems.

Abolfazl BayatView profile

Professor

• Quantum Information Technology
• Many-Body Systems
• Quantum Sensing
• +3 more

Professor Abolfazl Bayat is a full-time faculty member at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China (UESTC). He holds the position of Professor and serves as a Ph.D. supervisor for doctoral candidates. His academic journey includes a PhD in Physics from Sharif University of Technology (2008) followed by postdoctoral research at University College London and University of Ulm. He joined UESTC in 2017 under the prestigious 1000-Talent Youth Professorship and also secured the Sichuan Talent Fellowship in 2018. Education Bachelor of Engineering in Electrical Engineering, Sharif University of Technology Master of Science in Physics, Sharif University of Technology Doctor of Science in Physics, Sharif University of Technology Research Interests Bayat's research focuses on theoretical aspects of strongly correlated many-body systems and their applications in quantum technologies. His work leverages numerical simulations (e.g., Density Matrix Renormalization Group) and machine learning techniques (neural networks, Bayesian estimators). Key areas include: Many-Body Localization Non-Equilibrium Dynamics of Many-Body Systems Quantum Sensing Quantum Simulation Recent Article Trends His publications (2015-2018) emphasize quantum refrigeration, entanglement dynamics, and quantum phase transitions. Notable contributions involve machine-learning-assisted entanglement measurement and experimental protocols for spin arrays. Topics span quantum dots, impurity systems, and coherence in magnetic devices. Awards & Recognition China's 1000-Talent Youth Professorship (2017) Sichuan Talent Fellowship (2018) Advising & Labs Bayat supervises a research group called Physics of Quantum Information Technology , including students Rozha, Utkarsh Mishra, and Victor Montenegro. His team explores quantum technologies through theoretical and computational frameworks. No grant data is explicitly listed, but his work aligns with EPSRC-funded quantum technology initiatives. Labs & Teams His group operates within the Institute of Fundamental and Frontier Sciences, focusing on quantum information, many-body systems, and nanoelectronics applications.

• Inverse problems
• Uncertainty quantification
• Numerical Analysis
• +6 more

Zhang Wenlong is an Assistant Professor in the Department of Mathematics at the Southern University of Science and Technology (SUSTech). His research focuses on inverse problems, uncertainty quantification, numerical analysis, and their applications in fields such as electrical impedance tomography and partial differential equations. He holds a B.Sc. in Information and Computational Science from Nanjing University (2007-2011), a Ph.D. in Computational Mathematics from the Chinese Academy of Sciences (2011-2014), and a dual Ph.D. from the DMA at École Normale Supérieure and ETH Zürich under Professors Zhiming Chen and Habib Ammari. Prior to his current role, he was a postdoc at SUSTech (2017–2019). His research areas include inverse problems, hybrid imaging methods, homogenization theory, and numerical methods for PDEs. He has contributed to advancements in convexification techniques for inverse problems, stochastic convergence analysis, and data-driven model reduction methods. His work often intersects applied mathematics, computational physics, and biomedical imaging. Zhang has taught courses in linear algebra, ordinary differential equations, and finite element methods at SUSTech since 2017. His publications span top journals such as SIAM Journal on Imaging Sciences, Inverse Problems, and Journal of Computational Physics, reflecting his expertise in interdisciplinary computational and applied mathematics.

Hao WangView profile

Researcher

• Data Management
• Machine Learning
• Recommender Systems
• +7 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.

• Scientific computing
• Numerical Methods for Time-Dependent PDEs
• Finite-Volume Methods
• +2 more

Dr. Alexander Kurganov is a Chair Professor in the Department of Mathematics at Southern University of Science and Technology (SUSTech), China, since 2019. Previously, he served as Professor at SUSTech (2016–2019) and Tulane University (2010–2015, 2004–2010, 2001–2004). He has held visiting positions at Shanghai Jiao Tong University, University of Bordeaux I, Johannes Gutenberg University, Paul Sabatier University, and University of Michigan. PhD in Applied Mathematics, Tel Aviv University, 1998 MS in Mathematics, Moscow State University, 1989 Research Interests: Dr. Kurganov specializes in scientific computing, numerical methods for time-dependent PDEs, finite-volume methods, geophysical fluid dynamics, and nonlinear PDEs. His work focuses on developing robust numerical schemes for complex fluid dynamics problems, including shallow water systems, chemotaxis models, and compressible flows. Publication Trends: His recent publications emphasize high-resolution numerical schemes for hyperbolic conservation laws, shallow water equations, and interdisciplinary applications in environmental modeling, fluid dynamics, and financial mathematics. 2015–2018 NSF Research Grant (PI) 2012–2015 ONR Research Grant (PI) 2011 German Research Foundation (DFG) Grant 1997 The Rosset Prize (Tel Aviv University) Grants & Collaborations: Dr. Kurganov has secured multiple NSF and ONR grants as principal investigator. His collaborations span institutions in the USA, China, France, Germany, and Sweden, with applications in geophysics, biology, and finance.

• Stochastic Analysis
• Stochastic Computation
• Numerical Analysis of Stochastic Partial Differential Equations
• +2 more

LIU Zhihui is an Associate Professor in the Department of Mathematics at Southern University of Science and Technology (SUSTech), holding a concurrent Researcher position. Appointed as Associate Professor since June 2022 after serving as Assistant Professor from 2020-2022, his academic trajectory includes prior roles as Research Assistant Professor at Hong Kong University of Science and Technology (2018-2020) and Postdoctoral Fellow at Hong Kong Polytechnic University (2017-2018). His research focuses on stochastic analysis and computation, specializing in numerical methods for stochastic partial differential equations (SPDEs). Key areas include stochastic phase field models, ergodicity of stochastic systems, and numerical approximation of equations driven by fractional/white noise. His work demonstrates strong theoretical foundations combined with computational implementation. Analysis of his 15 most recent publications (2016-2022) reveals consistent focus on convergence rates, regularity theory, and structure-preserving numerical schemes for SPDEs. The research spans monotone systems, Allen-Cahn equations, Schrödinger equations with stochastic dispersion, and fractional noise models, with predominant application in mathematical physics and computational mathematics. He currently leads two active National Natural Science Foundation of China projects: the Excellent Young Scientists Fund Program (Overseas) on numerical analysis of SPDEs and a Young Program grant on numerical ergodicity of stochastic Allen-Cahn equations. Previously, he managed multiple Hong Kong RGC grants on stochastic phase field models before transferring them upon departure. His academic training includes a PhD from the Chinese Academy of Sciences' Academy of Mathematics and Systems Sciences (2012-2017) and a BSc in Mathematics and Statistics from Hubei University (2008-2012). No student advisement information is publicly available.

• Complex intermittent process modeling
• Advanced process control
• Iterative learning control
• +4 more

Jia SHI is an Associate Professor in the Department of Chemical Engineering and Biological Engineering at Xiamen University's School of Chemistry and Chemical Engineering. With a background in Control Science and Engineering, they have contributed to advanced process control techniques for batch processes over their academic career. Education: Bachelor's (Control Science, Xiamen University, 1994), Master's (Operations Research and Cybernetics, Xiamen University, 1997), Doctorate (Control Science and Engineering, Zhejiang University, 2006) Academic Career: Lecturer at Xiamen University's School of Information Science and Technology (1997-2007), Associate Professor since 2008 Their research focuses on complex intermittent process modeling and advanced process control techniques , particularly iterative learning control (ILC) and model predictive control (MPC). These methods address challenges in batch process systems through robust, adaptive, and intelligent control strategies. Selected recent publications demonstrate expertise in 2D modeling approaches for control systems, fault-tolerant control, and integrated feedback mechanisms. Key trends include application of mathematical optimization techniques to industrial chemical processes and development of robust algorithms for process reliability. As an active researcher, they have collaborated with institutions like Hong Kong University of Science and Technology (Research Assistant, 2003-2006) and maintain a strong publication record in top-tier journals such as AIChE Journal and Industrial & Engineering Chemistry Research.

• Quantum Chemistry
• Nanomaterials
• Molecular Modeling
• +3 more

Kai Tan serves as an Associate Professor in the Department of Chemistry at Xiamen University's College of Chemistry and Chemical Engineering, a position he has held continuously since December 2002. His academic journey is deeply rooted at Xiamen University, where he completed all his formal education. His educational background includes: Bachelor of Science (1993) Master of Physical Chemistry (1999) Doctorate in Physical Chemistry specializing in quantum chemistry (2003) Dr. Tan's research centers on quantum chemistry in nanometer-scale systems , with significant contributions to graph cluster applications and molecular modeling . His work bridges theoretical computation and experimental nanomaterials science, focusing on the electronic properties and chemical behavior of carbon-based nanostructures. This specialized expertise positions him at the intersection of computational chemistry and advanced materials development. Analysis of his 2005-2008 publications reveals a concentrated research trajectory in endohedral metallofullerenes, where he pioneered computational investigations into metal-carbide and metal-nitride cluster fullerenes. His work consistently employed density functional theory to unravel electronic structures, redox properties, and size-dependent reactivity patterns, establishing fundamental insights into how encapsulated clusters influence fullerene chemistry. No scientific awards were documented in the source material. While the text confirms his faculty position and research focus, it contains no information about student mentoring, research grants, laboratory facilities, or collaborative teams. His professional activities appear centered within the Department of Chemistry at Xiamen University, with no external affiliations indicated.

• Computational Enzymology
• Biological Electron Transfer
• Protein Design
• +1 more

Binju Wang is a Professor in the Department of Chemistry at Xiamen University's College of Chemistry and Chemical Engineering, promoted to this rank in 2018 after completing his PhD at the same institution. His academic foundation includes a BS from Harbin Institute of Technology (2007) and PhD from Xiamen University (2012). His educational background: BS in Chemistry, Harbin Institute of Technology, 2007 PhD in Chemistry, Xiamen University, 2012 Professor Wang's research centers on computational enzymology, specializing in multiscale simulations of metalloenzyme catalysis mechanisms, biological electron transfer processes, and protein design/biosynthesis. He employs quantum mechanical/molecular mechanical (QM/MM) methods and molecular dynamics to investigate enzyme reaction pathways, with significant contributions to understanding oxygen activation in cytochrome P450s, lytic polysaccharide monooxygenases, and nonheme iron enzymes. His work bridges computational chemistry and biochemistry to elucidate fundamental mechanisms with implications for enzyme engineering. Analysis of his 15 most recent publications (2019-2021) reveals a dominant focus on oxygen activation mechanisms across diverse metalloenzyme systems, electron transfer dynamics, and reactive oxygen species utilization. His computational approaches consistently uncover novel reaction pathways in enzymes critical for biocatalysis and metabolic engineering, with publications spanning top journals including JACS, Nature Catalysis, and ACS Catalysis. Scientific awards: No awards are documented in the provided information. Advising and grants: The text contains no details regarding student mentorship, grant funding, or research collaborations. Labs and teams: No information is provided about laboratory facilities, research groups, or team structures.