Dr. Yanqing Hu is an Associate Professor at the Department of Statistics and Data Science, School of Science, Southern University of Science and Technology (SUSTech). With a Ph.D. in Systems Theory from Beijing Normal University (2011) and postdoctoral experience at the Levich Institute, City University of New York (2011-2013), his work focuses on big data analysis of complex systems, particularly in social media dynamics, network resilience, and graph neural network applications. Ph.D.: Beijing Normal University (Systems Theory, 2011) Postdoctoral: Levich Institute, CUNY (2011-2013) Research spans complex network analysis, information spreading mechanisms, and predictability of network structures. His work combines theoretical frameworks with real-world applications in social networks, infrastructure systems, and brain connectivity. Recent publications explore information percolation in social media, resilience quantification in interdependent networks, and intrinsic structure predictability. These studies appear in high-impact journals like Nature Human Behaviour (IF: 24.3), Nature Communications (IF: 17.7), and PNAS (IF: 10). World AI Conference Youth Outstanding Paper Nomination Beijing Outstanding Doctoral Dissertation Award Guangdong Special Support for Young Talents Guangdong Outstanding Youth Fund Collaborations include leading researchers from Boston University, King's College London, and Shenzhen-Hong Kong Institute of Microelectronics. His work informs network defense strategies and efficient navigation mechanisms in complex systems.
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
Daohong Qiu is an Associate Professor and Master Tutor at the Geotechnical and Structural Engineering Center, School of Civil Engineering, Shandong University. His research focuses on advanced geological prediction in tunnels, surrounding rock stability, structural health monitoring for urban rail transit, and TBM tunneling performance optimization. Position: Associate Professor Affiliation: School of Civil Engineering, Shandong University Email: qiudh@sdu.edu.cn Research Interests: He specializes in geotechnical engineering challenges related to underground construction, including rock burst prediction , disaster control , and machine learning applications in geological modeling. His work emphasizes integrating advanced computational methods like quantum genetic algorithms and RBF neural networks with field data to improve tunnel safety and efficiency. Publication Trends: His recent 2019 studies address subsea tunnel risk assessment , rock burst prediction in underground caverns, and machine learning-driven surrounding rock classification . Earlier works (2014-2015) explore SVM/GA-SVM for geological disaster forecasting, while pre-2010 papers focus on optimization theory and stress field analysis. Patent Contributions: He holds multiple invention patents for geological prediction devices, including three-dimensional geological network modeling , seismic signal detection , and concrete elevation control systems .
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.
Fu Zongmei is a Professor at the School of Environmental Science and Engineering of Southern University of Science and Technology (SUSTech) since 2019. She previously held academic positions at Peking University (2010-2019) and Hong Kong Polytechnic University (2008). Her research focuses on atmospheric chemistry , air pollution , and chemistry-climate interactions , with expertise in inverse modeling of emissions, secondary organic aerosol formation, and air-sea exchange processes. Education: Ph.D. in Earth & Planetary Sciences (Harvard University, 2007), S.M. in Engineering & Applied Sciences (Harvard University, 2005), M.S. and B.S. in Atmospheric Sciences (National Taiwan University) Her research combines ground-based , aircraft , and satellite observations to study pollutant transport, cloud-aerosol interactions, and climate impacts on air quality. Recent work includes assessing haze mitigation strategies in Northern China and analyzing PM2.5 health risks through spatiotemporal modeling. Key honors include the National Natural Science Foundation of China Outstanding Young Scientist Fellowship (2012), the Tu Chang Wang Meteorological Science and Technology Award (2013), and the Ministry of Education Natural Science Award (Second Prize) (2019). She serves as an associate editor for Atmospheric Environment and contributes to major international conferences as session co-chair and organizer. Notable grants: NSFC General Project on Organic Nitrogen Aerosols (2020-2023, PI), National Key R&D Program on Air Quality Modeling (Co-I), and 973 Program on Chemistry-Climate Interactions (Co-PI)
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.
Xu Mingtao is an Associate Professor at the Department of Innovation, Entrepreneurship and Strategy within Tsinghua University's School of Economics and Management. With academic appointments since 2024 and prior experience at Louisiana State University (2020-2021) and Purdue University (2016-2019), his research bridges strategic management, technological innovation, and entrepreneurship. Education: PhD in Strategic Management (Purdue 2020), PhD in Organizational Management & MBA (Emory 2015), Masters in Statistics & Economics (Georgia Tech 2013), BEcon (UIBE 2011) Research Focus: Strategy implications of digital platforms, AI, patent litigation, and property rights systems Teaching: Strategic Management (BSc/MSc), Global Management (MSc), Advanced Research Methods (PhD) His work examines how property rights frameworks affect organizational learning and resource allocation, while exploring emerging trends in GPT-enabled startups and AI healthcare applications. Current research projects include NSFC-funded studies on generative AI's impact on human capital and geopolitical risks in venture capital alignment. Recent publications span Journal of Management (2025) and Academy of Management Review (2022), with working papers addressing GPT startups, patent monetization, and platform ecosystems. Awards include Alibaba Living Water Plan (2022) and multiple honors from Purdue University and academic conferences. 2022- Krannert School of Management Outstanding Research Award 2019- Krannert Ph.D. Service Award 2016- AOM BPS Division Outstanding Reviewer Actively involved in editorial boards ( Management and Organization Review ), professional associations (AOM, SMS, INFORMS), and industry advisory roles in AI, healthcare, and automotive sectors.
Professor Liangjun Su is a distinguished academic in econometrics, currently serving as the C.V. Starr Chair Professor at Tsinghua University's School of Economics and Management. He has held positions at Peking University and Singapore Management University, contributing extensively to nonparametric econometrics, panel data analysis, and machine learning applications. PhD in Economics, University of California, San Diego (2004) Master of Economics, University of California, Riverside (1999) Bachelor of Engineering Economics, Xi'an Jiaotong University (1994) His research focuses on advanced econometric methodologies, including: Nonparametric and semiparametric techniques High-dimensional panel data models Machine learning integration in econometric analysis Interactive fixed effects and latent group structures Recent publications highlight his work on: Dynamic panel models with interactive fixed effects Specification testing and structural changes High-dimensional factor models and FAVAR estimation Classifier-Lasso applications for hidden heterogeneity Scientific recognition includes: Fellow of the Journal of Econometrics (2014) Multa Scripsit Award, Econometric Theory (2014) Senior Fellow, Rimini Centre for Economic Analysis (2020-2026) Lee Kuan Yew Research Award (2011) Actively involved in academia, he serves as Co-Editor of Econometric Theory and on editorial boards of multiple journals. His team is currently recruiting postdoctoral fellows for research on high-dimensional metrology and machine learning applications in economics.
Dr. Ke Gao is an Associate Professor in the Department of Earth and Space Sciences at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in 2019 after completing postdoctoral research at Los Alamos National Laboratory in the United States. Dr. Gao holds a Ph.D. in Rock Mechanics from the University of Toronto, which he obtained in 2017. His educational background includes: 2021–present: Associate Professor, Department of Earth and Space Sciences, Southern University of Science and Technology 2019–2020: Assistant Professor, Department of Earth and Space Sciences, Southern University of Science and Technology 2017–2019: Post Doc, Solid Earth Geophysics, Los Alamos National Laboratory, USA 2012–2017: Ph.D., Rock Mechanics and Rock Engineering, University of Toronto, Canada Dr. Gao's research primarily focuses on rock mechanics and fault mechanics, with particular emphasis on the development of multiphysics coupling models based on the combined finite-discrete element method (FDEM). His work investigates rock fracturing mechanisms, hydraulic fracturing, and the stick-slip characteristics in sheared granular faults. He has made significant contributions to tensor-based statistical methods for characterizing stress variability and heterogeneity in fractured rock masses. His research bridges computational mechanics with earthquake physics, creating innovative approaches to understanding fundamental geological processes. Analysis of Dr. Gao's recent publications reveals a strong focus on computational geomechanics and earthquake physics. His work consistently applies and advances the combined finite-discrete element method (FDEM) to solve complex rock mechanics problems. There's a clear progression from fundamental method development to applications in earthquake source mechanics and hydraulic fracturing. The integration of machine learning techniques with traditional computational methods represents an emerging trend in his recent work, particularly for predicting slip behavior in granular fault systems. Dr. Gao has received several notable recognitions: Best Paper Award at the 7th International Symposium on In Situ Rock Stress (2016) National Overseas High-level Talent Program (Youth) (2020) Shenzhen 'Peacock Plan' B Talents (2021) Dr. Gao serves as principal investigator for multiple research projects funded by prestigious organizations including the National Natural Science Foundation of China, Ministry of Science and Technology key research and development projects, Guangdong Province general projects, and Shenzhen City general projects. He actively mentors graduate students and postdoctoral researchers, recruiting candidates with backgrounds in solid geophysics, rock mechanics, geological engineering, computational mechanics, and related disciplines. His research group provides comprehensive training in both theoretical and experimental aspects of rock mechanics and earthquake physics. Dr. Gao is affiliated with several professional organizations including the American Rock Mechanics Association, American Geophysical Union, International Society for Rock Mechanics, Canadian Geotechnical Society, Society of American Seismology, and ASCE Engineering Mechanics Institute, reflecting the interdisciplinary nature of his work spanning rock mechanics, geophysics, and computational engineering.
Christina (Yang) Gao is an Assistant Professor at the Southern University of Science and Technology (SUSTech) in the Department of Physics under the Faculty of Science. Her research focuses on theoretical investigations beyond the Standard Model, particularly in dark matter phenomenology and quantum systems for dark matter detection. PhD in Physics from University of California, Davis (2012-2018) Bachelor and Master of Physics from University of Oxford (2008-2012) Dr. Gao's work spans multiple high-energy physics domains: Dark Matter Direct Detection Particle Cosmology Quantum Systems in Physics Machine Learning Applications Her publications highlight collaborative efforts in axion detection methods using superfluid helium-3 and superconducting cavities. 2023 Simons Foundation Travel Grant 2009 New College Scholarship Contact: gaoy3@sustech.edu.cn
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.
Dawei Lu is an Associate Professor in the Department of Physics at Southern University of Science and Technology (SUSTech), where he leads the Spin Quantum Computing Lab. He has been with SUSTech since August 2017, initially as an Assistant Professor before being promoted to Associate Professor in May 2019. His educational background includes: PhD in Quantum Information Physics (2012) from University of Science and Technology of China (Hefei National Laboratory for Physical Sciences at Microscale) BSc in Optical Information Science and Technology (2007) from University of Science and Technology of China (Special Class for Gifted Young) Professor Lu's research focuses on experimental quantum computing using spin systems, particularly nuclear magnetic resonance (NMR) and nitrogen-vacancy (NV) centers in diamond. His work spans quantum control, quantum simulation, quantum metrology, and quantum thermodynamics. He holds the world record for controlling the largest number of qubits (12) using NMR technology. His research group has made significant contributions to quantum algorithms, quantum simulation of complex systems, and quantum thermodynamics, with numerous publications in top journals including multiple papers in Physical Review Letters. Analysis of Professor Lu's recent publications reveals a strong focus on quantum thermodynamics, quantum simulation of complex systems, and quantum metrology. His work often combines theoretical advances with experimental implementations on NMR and NV center platforms. There's a clear trend toward exploring quantum advantage in thermodynamic processes, quantum machine learning applications, and topological quantum computing. Professor Lu has received several prestigious awards: 2020 Pearl River Recruitment Program of Talents (Youth) 2020 Peng Cheng Professor 2018 National 1000-Talent Youth Plan 2017 Overseas High-Caliber Personnel in Shenzhen (Peacock Plan) 2012 CAS Presidential Scholarship Professor Lu actively mentors students and has advised numerous PhD and Master's students who have gone on to successful careers in academia and industry. His research is supported by multiple grants from the National Natural Science Foundation of China and provincial/municipal funding agencies. He has published over 50 papers as first or corresponding author, including 15 in Physical Review Letters. At SUSTech, Professor Lu established the Spin Quantum Computing Lab in August 2017, which focuses on realizing physical quantum computers using spins. The lab has two main experimental platforms: NMR quantum computing and NV center quantum computing. The lab's research encompasses a diverse array of quantum technologies, including quantum control, quantum simulation, quantum machine learning, quantum metrology, and quantum thermodynamics.
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.
