Tao Lu serves as an Assistant Professor and Doctoral Supervisor at the Business School of Southern University of Science and Technology (SUSTech) since September 2020. His academic appointments focus on bridging information systems, finance, and artificial intelligence research within a business context. Education: Ph.D. in Information Systems, The Chinese University of Hong Kong (2020) M.Phil. in Information Systems, Hong Kong University of Science and Technology (2017) B.S. in Physics/Economics, Tsinghua University (2015) Dr. Lu's research spans three interconnected domains: (1) Social network dynamics and online word-of-mouth marketing using analytic modeling and experimental design; (2) Quantitative finance focusing on distributional uncertainty in asset pricing and neural network applications; (3) Algorithmic bias mitigation in machine learning systems. His work combines rigorous theoretical modeling with empirical validation, often translating to practical applications in quantitative finance. Scientific Recognition: Innovation Research Award of China Information Economics Society (2022) Dr. Lu actively supervises research through his team's recruitment of research assistants and postdoctoral fellows, with projects applying to real-world trading systems in quantitative hedge funds. His research group focuses on developing computational models for financial markets and social platforms, emphasizing both theoretical contributions and practical implementation.
Florian Bossmann is an Associate Professor at the College of Mathematics, Harbin Institute of Technology (HIT), specializing in Applied Signal Processing with a focus on algorithms and applications. His research encompasses inverse problems, greedy algorithms, sparsity, and compressed sensing, with applications in seismic exploration, ptychography, and video processing. He holds a PhD in Mathematics from the University of Göttingen (2013) and a Diplom in Mathematics from the University of Duisburg-Essen (2009). Current projects include NSFC-funded seismic data interpolation and a start-up grant for applied signal processing research. He teaches courses like 'Signal and Image Processing' and 'Calculus for Civil Engineering'. Supervising Master/PhD students in signal processing, he emphasizes collaboration and innovation. His lab, the ASP Group, explores cutting-edge methods in multidimensional data reconstruction and algorithm design. Prominent publications focus on impedance inversion, neural network applications in geoscience, and object reconstruction techniques. He has led collaborative research initiatives with institutions like the Fraunhofer Institute and the Helmholtz Center for Environmental Health.
Professor Meng Qingchun is a second-level professor and doctoral supervisor at the School of Management, Shandong University. He leads the Digital Intelligence Management and Decision Simulation Laboratory (Class A) and focuses on emergency resource coordination, supply chain resilience, and disaster response strategies. His research has been supported by multiple National Natural Science Foundation projects and resulted in over 80 publications, including works in Transportation Research Part E , Production and Operations Management , and Risk Analysis . Key achievements include optimizing emergency material reserves, volunteer task allocation algorithms, and fairness-aware decision models. Education details are not explicitly mentioned in the provided texts, but his academic roles and publications indicate advanced training in operations research and management science. He serves as deputy secretary-general of the China Association for Optimization and chairs its Network Science Branch. His work bridges theoretical advancements with practical applications in disaster management and public policy. Research interests emphasize emergency governance, collaborative decision-making mechanisms, and government-enterprise partnerships. Recent projects address volunteer scheduling efficiency, risk integration under uncertainty, and loan subsidy policies post-disasters. His team’s work has been published in top-tier journals like Production and Operations Management (UTD 24) and Risk Analysis (ABS 4). Scientific awards include the National Teaching Achievement Award (2nd Prize) and Shandong Provincial Social Science Award (1st Prize). He has secured grants totaling four National Natural Science Foundation key/general projects and a National Social Science Foundation key project. His lab collaborates with industry leaders in manufacturing and public health, applying optimization theories to real-world challenges.
Dr. Yi Ava Wu is a ZJU100 Young Professor at the School of Management, Zhejiang University, where she serves as a doctoral supervisor. Her research focuses on Financial Accounting, Financial Analysis, Integrated Reporting, and Audit Quality. She is based in Hangzhou, China, and can be contacted at avayi_wu@zju.edu.cn. Her work bridges accounting practices, regulatory frameworks, and organizational behavior, with recent studies examining auditor litigation risk, analyst behavior, and the impact of integrated reporting on corporate strategy. Education details are not explicitly provided, but her role as a doctoral supervisor suggests advanced academic credentials in accounting or finance. Her research emphasizes empirical studies on market efficiency, regulatory effectiveness, and the application of technology in education and healthcare, as seen in her work on automated feedback systems and mobile healthcare surveillance. Publications highlight trends in audit quality evaluation, analyst decision-making under regulatory changes, and cross-disciplinary innovations in information systems. While no awards are noted, her prolific output across accounting, finance, and technology underscores her contributions to multiple academic domains. She advises doctoral students and has engaged in diverse research collaborations spanning both theoretical and applied fields.
Xu Guo is a Professor at the Geotechnical and Structural Engineering Center of the School of Civil Engineering , Shandong University. His research spans computational mathematics, geotechnical engineering, and financial modeling. Education : PhD in Applied Mathematics (Hong Kong Baptist University, 2015), MSc in Finance and Actuarial Mathematics (City University of Hong Kong, 2012), BSc in Financial Mathematics and Financial Engineering (Shandong University, 2011). His research focuses on Numerical methods for fractional diffusion equations Machine learning applications in rock mass parameter prediction Computational finance and CGMY model-based option valuation High-order finite difference schemes for tempered fractional equations Integration of tunnel boring machine data with predictive modeling Xu’s publications highlight interdisciplinary trends merging geotechnical engineering with machine learning , and financial mathematics with advanced numerical analysis . Key themes include predictive modeling, fractional calculus, and computational finance applications.
Zhuozhao Li is an Assistant Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in August 2021 after serving as a Postdoctoral Scholar at the University of Chicago from July 2018 to July 2021. His research focuses on high-performance computing, distributed systems, and cloud/edge computing, with applications across scientific domains. Ph.D. in Computer Science, University of Virginia, May 2018 M.S. in Computer Science, University of Southern California, May 2012 B.E. in Computer Science, Zhejiang University, July 2010 Dr. Li's research spans several critical areas in modern computing infrastructure. His primary focus is on High Performance Computing , where he develops novel approaches to optimize computational efficiency in scientific applications. In Distributed Systems , his work addresses challenges in large-scale system coordination and resource management. His contributions to Cloud/Edge Computing focus on improving virtualization techniques and resource allocation strategies. Additionally, he explores applications in the Internet of Things , examining how distributed computing paradigms can enhance IoT infrastructure. Dr. Li's publication record demonstrates a consistent focus on advancing distributed computing systems and their scientific applications. His recent work shows a clear progression from foundational research on data-parallel frameworks and job scheduling toward more applied systems for scientific computing. His contributions to projects like funcX and DLHub represent significant advancements in making distributed computing more accessible for scientific workflows. A notable trend is the increasing interdisciplinary nature of his work, with applications spanning from computational biology (SARS-CoV-2 research) to privacy-preserving spatial crowdsourcing. ACM HPDC Best Paper Nominees, 2019 IEEEMASS Service Award, 2019 Outstanding Graduate Research Assistant, University of Virginia, 2018 Dr. Li actively mentors students and researchers, currently recruiting PhD students, Master's students, postdocs, and research assistants for his research group at SUSTech. His research has been supported through various collaborative projects, including significant contributions to the U.S. Department of Energy National Virtual Biotechnology Laboratory Project about COVID-19, which was awarded the Secretary of Energy Achievement Award. His work on funcX, DLHub, and Parsl represents major software infrastructure development efforts with broad scientific impact. Dr. Li is affiliated with multiple research initiatives, including the Globus Lab (during his postdoc at University of Chicago) and various collaborative projects involving high-performance computing resources. At SUSTech, he leads a research group focused on distributed systems and high-performance computing. His work often involves collaboration with researchers across institutions, particularly in projects related to scientific computing infrastructure like funcX and DLHub.
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.
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.
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.
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.
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.
Liu Zhanli is a Full Professor at the Department of Engineering Mechanics, School of Aerospace, Tsinghua University. He holds dual B.S. and Ph.D. degrees in Engineering Mechanics from Tsinghua University (2004 and 2009 respectively), followed by postdoctoral research at Northwestern University, USA (2009-2012). His research focuses on computational mechanics methods for understanding material and structural deformation/failure, particularly in aerospace and biomedical engineering contexts. Key areas include impact loading dynamics, machine learning-driven reverse engineering, lightweight protective structures, and biological tissue mechanics. Professional roles include Regional Editor for the International Journal of Fracture , Editorial Board member of Applied Mathematics and Mechanics , and leadership positions in multiple Chinese mechanical engineering societies. Notable awards include the 2020 Aeronautical Society Science Prize and the National Science Fund for Excellent Young Scholars (2017). Major research initiatives span: Dynamic response modeling under impact loads Machine learning-based inverse design methodologies Biomedical tissue damage and repair Ballistic-resistant composite material design His work bridges computational theory with practical applications in defense, aerospace, and medical engineering domains.
Chen Jia is a Lecturer at Tongji University's School of Art and Media. He holds a Master's degree in French horn from Mainz Conservatory of Music and Mannheim State Conservatory of Music, studying under renowned professors such as Xiaoming Han and Hermann Baumann. He has served as principal horn in multiple orchestras and co-founded the Tongji University Music Boat Woodwind Quintet. Education: Master's in French Horn (Mainz Conservatory, Mannheim State Conservatory) Professional Affiliations: Member of Mannheim Blech ensemble, Principal Horn for multiple orchestras His research interests include music education and orchestral performance, though his recent publications focus on computational fluid dynamics, electrohydrodynamics, and microfluidics. Key topics include lattice Boltzmann modeling, viscoelastic fluid dynamics, and electrohydrodynamic flow control. Scientific contributions span interdisciplinary domains, with work on physics-informed neural networks for microfluidic systems and polymer 3D printing challenges. Collaborative projects involve international ensembles and computational simulations.
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.
Mao Xiaojie is an Associate Professor at the Department of Management Science and Engineering, Tsinghua University's School of Economics and Management . Holding a PhD in Statistics and Data Science from Cornell University (2021) and a bachelor's in Mathematical Economics and Finance from Wuhan University (2016), Mao specializes in causal inference and data-driven optimization decision-making . PhD: Cornell University (2016-2021) Bachelor: Wuhan University (2012-2016) Mao's research bridges machine learning , statistics , and operations research to address challenges in contextual optimization, algorithmic fairness, and robust causal inference. Recent work focuses on data combination , surrogate variables , and minimax methods for handling unobserved confounding and limited outcome data. Key trends in Mao's publications include: Advancing bandit algorithms for efficient contextual decision-making Developing debiased machine learning frameworks for quantile treatment effects Designing robust optimization models under noisy and incomplete covariates Scientific recognition includes: Applied Probability Society Best Student Paper Competition Finalist (2020) Multiple teaching excellence awards at Tsinghua University (2022-2024) Research grants from the National Natural Science Foundation of China Mao currently teaches Empirical Methods in Management Science (PhD), Data Analysis: Inference and Decision Making (Master), and Probability Theory and Mathematical Statistics (Undergraduate). Research collaborations span institutions like Cornell and MIT, with publications in top venues including NeurIPS , ICML , and Operations Research .
