جستجوی استاد راهنمای دانشگاهی

• Sufficient Dimension Reduction
• Variable Selection
• High Dimensional Analysis
• +۱ مورد دیگر

CHEN Xin is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech), where he has been employed since January 2019. He initially joined the Department of Mathematics before transitioning to his current department in August 2019. Prior to SUSTech, he held academic positions as Assistant Professor at the National University of Singapore (2011-2019) and Syracuse University (2010-2011). Education History: PhD in Statistics from University of Minnesota, USA (2005-2010) MS in Statistics and Applied Probability from National University of Singapore (2001-2003) BS in Mathematics from Nankai University, China (1994-1999) Research focuses on developing statistical methodologies for high-dimensional and complex data analysis. Primary research domains include: sufficient dimension reduction techniques for efficient data representation; variable selection methods for high-dimensional models; robust statistical approaches for complex datasets; and optimization of computational efficiency in statistical estimation. His work consistently addresses theoretical foundations while developing practical implementations for modern data challenges. Publications demonstrate sustained focus on statistical methodology development, particularly in dimension reduction and high-dimensional analysis. Recent works emphasize robust methods for ultrahigh-dimensional data, efficient computation algorithms, and model diagnostics. Research consistently appears in top-tier statistics journals including Biometrika, Journal of the American Statistical Association, and Annals of Statistics. No scientific awards, student advising relationships, or research grants are mentioned in the source materials. Similarly, there is no information available regarding laboratory affiliations or research team composition.

• Offshore Geotechnical Engineering
• Marine Foundation Engineering
• Marine Mooring and Anchoring Technologies
• +۴ مورد دیگر

Dr. Fu Yong is an Assistant Professor and doctoral supervisor at the Department of Marine Science and Engineering , Southern University of Science and Technology (SUSTech), Shenzhen, China. He earned his PhD in Geotechnical Engineering from the National University of Singapore (NUS) in 2018, preceded by a Master's (Tongji University, 2013) and Bachelor's (Shanghai University, 2010) in Civil Engineering . Current Role: Assistant Professor, SUSTech (2020–present) Prior Roles: Research Fellow (NUS, 2018–2020), Research Engineer (NUS, 2017–2018) Dr. Fu specializes in marine geotechnical engineering , with key focuses on offshore wind power foundation design, deep-sea anchoring technologies, and underground space development. His technical expertise includes large deformation finite element modeling (CEL/RITSS methods), stochastic analysis , and machine learning (GWO-MLP optimization). His work bridges laboratory experiments (centrifuge and field tests) with computational simulations to address challenges in marine infrastructure. Marine foundation design for offshore wind turbines and marine ranches Deepwater dynamic penetration anchoring systems Horizontal deep soil mixing equipment for underground space Finite element simulation of large deformations and thermal effects His publication trends reveal a consistent focus on offshore anchoring mechanics, shale fracture modeling, and machine learning integration for geotechnical predictions. Recent works (2024–2025) emphasize torsional effects in suction anchors, sand-GFRP interface behavior, and advanced constitutive models for cemented soils. Key journals: Geotechnique , Ocean Engineering , Physics of Fluids , Computers and Geotechnics Dr. Fu has received multiple scientific awards , including the 2024 SUSTech Undergraduate Teaching Award, 2022 SUSTech Outstanding College Mentor Award, and NUS Research Scholarship (2013–2017). Selected Honors: 2024 First Prize, SUSTech Undergraduate Teaching Award 2022 SUSTech Outstanding College Mentor 2020 Shenzhen Overseas High-level C Talent He actively contributes as a reviewer for 18+ international journals and serves on editorial boards for journals like Sustainable Structures . His work on machine learning-driven anchor penetration prediction (2024) and hybrid phase-field shale fracture modeling (2024) exemplifies interdisciplinary innovation.

• Risk Management
• Actuarial Science
• Optimal Reinsurance
• +۹ مورد دیگر

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.

Liu Chunمشاهده پروفایل

دانشیار

• Capital Markets
• Financial Measurement
• Risk Management

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.

• Statistics in Financial Econometrics
• Quantile Regression
• Variable Selection
• +۴ مورد دیگر

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.