Shunxiang Cao is an Assistant Professor at Tsinghua Shenzhen International Graduate School in China, where he has worked since July 2022. He earned his Ph.D. in Aerospace Engineering from Virginia Tech (United States) between August 2014 and August 2020 and completed his B.S. in Aerospace Engineering at Beihang University (China) from September 2010 to May 2014. Prior to his current role, he served as a Postdoctoral Scholar at the California Institute of Technology (United States) from November 2019 to May 2022. Teaches courses such as Advanced Numerical Analysis, Numerical Methods for PDEs, and Fluid-Structure Interaction simulations. Research Interests include fluid-structure interaction, computational fluid dynamics, shock/bubble dynamics, material failure analysis, embedded-boundary methods, resolvent-based optimization, Kalman inversion, high-intensity focused ultrasound, and digital twin technology. His work focuses on numerical modeling, optimization, and fluid-solid coupling mechanisms in diverse applications like underwater propulsion, energy storage membranes, and medical acoustics. Scientific Awards include the USNCCM16 Conference Award (2021), Graduate Fellowship at Virginia Tech (2014-2015), Merit Undergraduate Student in Beijing (2014), and Singapore Technology Engineering Fellowship (2010-2013).
Wang Chun is a tenured Associate Professor in the Department of Management Science and Engineering at the School of Economics and Management, Tsinghua University. He currently serves as Deputy Director of the department and has held academic positions at Tsinghua since 2017. His work bridges stochastic control, reinforcement learning, and machine learning applications in operations management and financial technology. Education: Bachelor's Degree from Tsinghua University (2005) Master's Degree from the University of Wisconsin-Madison (2008) Doctorate in Operations Research from Columbia University (2014) Research Interests: His research focuses on stochastic control and decision-making under uncertainty, particularly in inventory management, revenue management, financial engineering, and sports analysis. He also investigates reinforcement learning algorithms for dynamic optimization and applies machine learning to business data analytics and financial technology. Published Work Trends: Wang’s publications span operations research, financial mathematics, and computational management science. Recent studies address seasonal optimization patterns, robust inventory models, and game-theoretic approaches to sports analytics. His work integrates theoretical advancements in dynamic programming with practical applications in financial engineering and market microstructure. Career History: 2023–Present: Associate Professor, Tsinghua University 2020–2023: Associate Professor, Tsinghua University 2017–2020: Assistant Professor, Tsinghua University Researcher at KCG (New York), leading studies on quantitative investment and asset optimization
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.
Liu Qing is a Tenured Associate Professor in the Department of Economics at Tsinghua University's School of Economics and Management. He has been affiliated with Tsinghua University since 2008 and holds a Ph.D. in Economics from the University of Toronto. Education: Ph.D. in Economics, University of Toronto (2008) M.A. in Economics, University of British Columbia (2002) M.A. in Economics, Zhejiang University (1999) His research focuses on international macroeconomics, monetary economics, and China's economy, particularly examining real exchange rates, current account imbalances, monetary policy, industrial policy, and structural transformation. Recent studies analyze labor market flexibility, CO2 emissions decomposition, and the impact of preferential lending policies on earnings inequality. His publications span journals like International Economic Review , Journal of Development Economics , and Journal of International Money and Finance . Research methods emphasize Bayesian DSGE modeling and empirical analysis of macroeconomic structures. Scientific Awards: Tsinghua SEM Teaching Excellence Award (First Prize) Tsinghua University Outstanding Class Teacher (Second Prize) Tsinghua SEM Outstanding Class Teacher Award Liu Qing teaches undergraduate courses in Money and Banking, master's courses in International Finance, and doctoral courses in Advanced Macroeconomics and Open Macroeconomics. His academic contributions include analyzing China's monetary policy frameworks and global welfare implications of oil price stabilization.
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.
Xiyun Jiao is an Assistant Professor at the Department of Statistics and Data Science, Southern University of Science and Technology (SUSTech). She earned her Ph.D. in Statistics from Imperial College London (2016) and held a postdoctoral research fellowship at University College London (2017–2020). Education : Ph.D. (Imperial College London), Master’s (KU Leuven), Bachelor’s (Tsinghua University) Research Interests : Computational statistics, Bayesian statistics, Markov chain Monte Carlo algorithms, and statistical methods in population genetics Her publications focus on integrating Bayesian frameworks with computational techniques for phylogenetics, cosmology, and statistical modeling. No scientific awards or student advisees are listed in the provided text.
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.
Zhang Yiying is an Assistant Professor and Doctoral Supervisor in the Department of Mathematics at the Southern University of Science and Technology (SUSTech). Previously, they served as an Assistant Professor at Nankai University from January 2019 to August 2021. Their academic journey includes a PhD in Actuarial Science from the University of Hong Kong (2018), with academic visits to the University of Leuven (Belgium) and University of Amsterdam (Netherlands) during 2018-2019. Dr. Zhang's educational background includes: Bachelor of Science in Mathematics and Statistics from Lanzhou University (2012) Master of Science in Mathematics and Statistics from Lanzhou University (2015) PhD in Actuarial Science from the University of Hong Kong (2018) Dr. Zhang's research spans three main areas with significant contributions to theoretical frameworks and practical applications: Risk Management and Actuarial Science: Focusing on optimal reinsurance design, credibility theory for heterogeneous risks, systemic risk assessment using conditional distortion measures, and risk measurement under asymmetric information. Applied Probability: Investigating ordered variable models, random order theory, stochastic comparisons, and statistical dependence structures in risk modeling. Reliability Theory and Statistics: Developing methodologies for reliability analysis, system reliability design, and optimization of k-out-of-n systems with heterogeneous components and random weights. Dr. Zhang's publication record demonstrates a consistent research trajectory across actuarial science and reliability engineering domains. Their work frequently appears in top-tier journals including Insurance: Mathematics and Economics, Scandinavian Actuarial Journal, ASTIN Bulletin, and Naval Research Logistics. Recent publications show strong interdisciplinary connections between actuarial science, probability theory, and reliability engineering, with particular emphasis on risk modeling under asymmetric information and optimal system design. Dr. Zhang has received recognition for their scholarly contributions through publications in leading actuarial journals such as IME, SAJ, ASTIN Bulletin, and NAAJ, as well as operations research journals including EJOR, RESS, and NRL. As a Doctoral Supervisor, Dr. Zhang mentors PhD students in advanced research topics at the intersection of actuarial science, probability theory, and reliability engineering. Their research program addresses fundamental questions in risk modeling and system reliability, with potential applications in insurance, finance, and engineering systems.
Fuchun SUN is a Full-time Professor at Tsinghua University's Department of Computer Science and Technology, where he has been affiliated since 1998. He holds a Bachelor's (1986) and Master's (1989) in Automation from the Institute of Naval Aeronautical Engineering, and a Ph.D. in Computer Science & Technology from Tsinghua University (1997). Prof. SUN serves as Deputy Director of the State Key Laboratory of Intelligent Technology and Systems and holds editorial positions in IEEE Transactions on Neural Networks and Soft Computing. Research Focus: His work spans Intelligent Control, Robotics, Networked Control Systems, and Artificial Cognitive Systems. He pioneers neuro-fuzzy modeling, adaptive control of nonlinear systems, and Markov jump system filtering, with applications in spacecraft, mobile robots, and flexible manipulators. Research projects include National 863 High-Tech Programs and National Basic Research (973) Programs on hyperspace vehicle control and multi-satellite networking. Publications Trend: His 15 most recent articles (2002–2010) demonstrate consistent focus on neuro-fuzzy adaptive control, robotics, Markov jump systems, and H-infinity filtering. Over 80% involve experimental validation of theoretical frameworks for robotic manipulators and nonlinear systems. Awards: National Natural Science Funds for Distinguished Young Scholars (2006) New Century Talents Award, Ministry of Education (2004) National Science and Technology Progress Award, Second Class (2002) 18th Choon-Gang Academic Award, Korea (2003) Science and Technology Progress Award, Beijing (Second Class, 2004) National Distinguished Doctoral Dissertation (2000) Academic Leadership: Supervised 15 Ph.D. and 20 Master's students. Secured 10+ national grants including NSFC Distinguished Young Scholars funding. Developed experimental platforms: two flexible-link robot test-beds and a space teleoperation system for on-orbit servicing.
Chen Guoqing is a Professor and Senior Professor of Humanities at Tsinghua University's School of Economics and Management, where he serves as Deputy Director of the Academic Committee. He chairs the National Natural Science Foundation of China's Big Data Major Research Program Guidance Expert Group and directs the Ministry of Education's Higher Education Management Science and Engineering Professional Teaching Guidance Committee, with additional roles on national informatization and New Liberal Arts committees. Education: Bachelor's Degree, Renmin University of China (1982) Master's Degree, University of Leuven, Belgium (1988) Doctorate, University of Leuven, Belgium (1992) Research Focus: Professor Chen pioneers research in Business Intelligence and Big Data Analysis, developing frameworks like PAGE for data-driven decision-making. His work spans E-commerce, IT Strategy, and Fuzzy Logic, with recent emphasis on personalized recommendation systems, online consumer behavior, and AI-human collaboration in business contexts. His publications reveal a trajectory from foundational fuzzy logic research to cutting-edge big data applications, consistently bridging technical innovation with managerial relevance. Awards: AIS Fellow (first from mainland China) IFSA Fellow Fudan Management Outstanding Contribution Award Changjiang Scholar Distinguished Professor National Science Fund for Distinguished Young Scholars National Outstanding Doctoral Dissertation Advisor Leadership: As former Executive Vice Dean of Tsinghua SEM and Vice President of IFSA, he has shaped academic policy globally. He leads major NSFC projects and international collaborations while advising government bodies on informatization strategy. His teaching includes national-level courses like "Management Information Systems" and "Management in the Big Data Era."
Liu Chun is an Associate Professor in the Department of Finance at Tsinghua University's School of Economics and Management. He holds a PhD from the University of Toronto (2002-2007), a Master's (1999-2001) and Bachelor's (1995-1999) in Economics and Management from Tsinghua University. His research focuses on capital markets, financial measurement, and risk management. Professor Liu teaches courses including Intermediate Financial Theory, Financial Data Analysis, and Financial Practice Classes. His research explores diverse aspects of China's financial system including local government financing, SME credit access, investor behavior, and market microstructure. Recent work analyzes policy impacts using advanced econometric methods like Bayesian latent variable models and survival analysis. His publications predominantly appear in finance and economics journals, with thematic concentrations in: Chinese financial market dynamics Behavioral finance and investor decision-making Policy evaluation and regional development Financial econometrics and statistical modeling Market microstructure and volatility analysis Fintech innovations and digital finance Professor Liu maintains research collaborations across departments and supervises projects on financial infrastructure development. His office is located in Room B310 of Tsinghua University's Lihua Building in Beijing.
Dr. Han Feng is a Research Associate Professor at the School of Environmental Science and Engineering, Southern University of Science and Technology, collaborating with Professor Yi Zheng. He has been with the institution since 2015, progressing from Post-doctoral Research Fellow (2015-2018) to Research Assistant Professor (2018-2021), and currently holds his present position since 2021. His educational background includes a Ph.D. in Mechanics (Energy and Resources Engineering) from Peking University (2008-2015) and a Bachelor's degree in Theoretical and Applied Mechanics from the same institution (2004-2008). Dr. Han specializes in eco-hydrological and water environmental simulation, with research focusing on numerical simulation of hydrological processes, ecological processes, and water quality processes at watershed scale . His work encompasses development of new computational models, exploration of efficient model-data fusion methods, and investigation into coupling paradigms of deep learning and process-based models. His expertise spans eco-hydrological modeling, non-point source pollution simulation, data assimilation, uncertainty analysis, and machine learning applications in environmental systems. His publication record demonstrates significant contributions to the field, with over 40 papers including 38 SCI-indexed articles in top journals such as Water Research, Water Resources Research, and Journal of Hydrology. His recent work shows a clear trend toward integrating artificial intelligence with traditional hydrological modeling, particularly in applying physics-encoded deep learning to distributed hydrological modeling and using Bayesian approaches for assimilating sensor data in water quality modeling. National Natural Science Foundation of China青年科学基金 project (41807164) as PI (2019-2021) NSFC-CONICYT International Cooperation project (5191101522) as main participant (2020-2024) Chinese Academy of Sciences Strategic Priority Research Program (XDA20100104) as main participant (2018-2023) Shenzhen Municipal Engineering Lab of Environmental IoT Technologies as key member (2018-2020) China Postdoctoral Science Foundation project (2017M612505) as PI (2017-2018) As one of the main developers of the three-dimensional distributed eco-hydrological model HEIFLOW (Hydrological-Ecological Integrated watershed-scale FLOW), Dr. Han's research has been applied across diverse geographical contexts including the Heihe River Basin, Pearl River Basin, Luanhe River Basin, Miho Basin (South Korea), Skjern Basin (Denmark), and San Joaquin Basin (United States).
Jiang Xuejun is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). He has been with SUSTech since 2013, initially as a Tenure-Track Assistant Professor and promoted to Associate Professor in 2019. Prior to joining SUSTech, he served at Zhongnan University of Economics and Law. His educational background includes: Ph.D. in Statistics from The Chinese University of Hong Kong (2009) M.Sc. from Yunnan University B.Sc. from National University of Defense Technology Jiang Xuejun's research focuses on advanced statistical methodologies with applications in various domains. His work spans statistical theory development and practical applications in finance, economics, and risk assessment. He has made significant contributions to quantile regression, variable selection, survival analysis, and nonparametric regression methods. His research publications demonstrate a strong trend toward developing innovative statistical methods for high-dimensional data analysis, Bayesian modeling approaches, and applications in financial econometrics and disaster risk assessment. Many of his recent papers focus on quantile regression techniques, dimension reduction methods, and robust statistical testing procedures. Jiang Xuejun has received several prestigious awards: Shenzhen Outstanding Teacher (2018) Southern University of Science and Technology "Outstanding Teaching Award" (2018) "Excellent Mentor Award" from Southern University of Science and Technology (2016) Selected for Shenzhen's "Peacock Plan" for overseas high-level talents He has successfully secured multiple research grants as Principal Investigator, including projects funded by the National Natural Science Foundation of China (both General and Youth programs), Guangdong Provincial Natural Science Foundation, and Shenzhen Science and Technology Innovation Commission. His research portfolio includes work on likelihood inference for high-dimensional models, statistical methods for epidemic disease control, and quantitative trading systems using machine learning. Jiang maintains an active research group focusing on statistical methodology development and applications, with particular emphasis on financial statistics and econometrics. His team collaborates with researchers across multiple disciplines to address complex data analysis challenges in economics, finance, and public health.
Dong Xiaojing is a Tenured Associate Professor at Santa Clara University, USA, specializing in Bayesian statistical methodologies and their practical implementations. She was invited to deliver a lecture series at Tsinghua University's School of Economics and Management, Department of Marketing, in June 2025. Her research centers on advanced statistical modeling with emphasis on Bayesian inference systems, probabilistic programming, and cross-disciplinary applications. She explores methodological innovations in uncertainty quantification and decision-theoretic frameworks, contributing to both theoretical statistics and real-world problem solving across scientific domains. As an active academic presenter, she engages with international research communities through specialized workshops and university collaborations, demonstrating commitment to advancing statistical education and methodology dissemination.
