جستجوی استاد راهنمای دانشگاهی

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.

• Big Data
• Complex Networks
• Graph Neural Networks
• +۱ مورد دیگر

Wenzhong Li is a Professor at the School of Computer Science, Nanjing University, where he leads research at the State Key Laboratory for Novel Software and Technology. His academic career spans over 15 years with significant contributions to AI-empowered distributed systems, big data mining, and networking applications. He teaches Computer Networks and guides graduate students in Distributed Computing Research. Professor Li's research focuses on cutting-edge areas including AI-Empowered Distributed Systems and Applications (MultiModal Large Models, Embodied Intelligence, Edge Computing), Big Data Mining (Time Series Analysis, Graph Computing, Social Networks Analysis), and AI-Based Distributed Resource Scheduling. His work bridges theoretical foundations with practical implementations in real-world systems. His recent publications demonstrate a strong trend toward integrating deep learning with graph theory and time series analysis, with applications in human activity recognition, network optimization, and multimodal systems. The research spans multiple disciplines including artificial intelligence, computer vision, networking, and data mining, with a particular emphasis on practical implementations for real-world problems. Best Paper Runner Up at KSEM 2023 for 'Learning-based Dichotomy Graph Sketch for Summarizing Graph Streams with High Accuracy' Best Paper Award at APNet 2018 for 'Toward Effective and Fair RDMA Resource Sharing' Professor Li has advised numerous PhD and Master's students who have gone on to prominent positions at institutions like Nanjing University, Huawei, Alibaba, Microsoft, and various international universities. His research is supported by substantial grants from the National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, National Power Grid, and other major funding bodies, totaling multiple multi-year projects with significant budgets. He leads the AINet Group and is affiliated with the Sino-German Institute of Social Computing and MobileCloud research initiatives. His DISLAB provides the organizational framework for his research team, which includes dozens of graduate students and collaborators working on cutting-edge problems in AI, networking, and distributed systems.

• AI-Empowered Distributed Systems
• MultiModal Large Models
• Embodied Intelligence
• +۹ مورد دیگر

Yiming Li is an Associate Professor in the Department of Biomedical Engineering at Southern University of Science and Technology's School of of Engineering. His research focuses on cutting-edge 3D super-resolution imaging techniques and their biological applications, with expertise spanning optical instrumentation, theoretical optics, and advanced imaging algorithms developed during his postdoctoral work at EMBL and Yale University. Education: Ph.D. in Biophysics, Karlsruhe Institute of Technology (2010-2015) M.Sc. in Medical Physics, Heidelberg University (2009-2010) B.Eng. in Biomedical Engineering, Shanghai Jiao Tong University (2005-2009) Research Interests: Dr. Li specializes in developing advanced 3D super-resolution microscopy techniques with particular expertise in single-molecule localization microscopy , point spread function engineering , and real-time 3D imaging systems. His work bridges optical physics and biological applications, enabling nanoscale observation of cellular processes. His software earned first place in the SMLM Challenge 2016, the field's most prestigious software contest. Publication Trends: Dr. Li's research shows a clear progression from fundamental algorithm development to comprehensive imaging system design, with increasing emphasis on real-time 3D applications. His publications in Nature Methods, Nature Communications, and other high-impact journals demonstrate consistent innovation in super-resolution microscopy, particularly in point spread function calibration, aberration correction, and biological applications at the nanoscale level. Scientific Awards: National Overseas High-level Talents (Youth Program) (2020) Shenzhen Overseas High-level Talents Category B EMBL-EIPOD Marie Curie Postdoc Fellowship (2016-2019) Karlsruhe School of Optics and Photonics Fellowship (2010-2013) International Symposium on Biomedical Imaging Travel Grant (2013) Professional Activities: As a PhD supervisor at SUSTech, Dr. Li mentors graduate students in biomedical engineering. He serves as a reviewer for top journals including Nature Methods, Light: Science & Applications, and Optics Letters. His laboratory maintains active international collaborations with EMBL, Yale University, Oxford University, and Cambridge University, facilitating cross-institutional research in advanced imaging techniques. Research Laboratory: Dr. Li leads an active research group focused on next-generation imaging technologies, operating under the website https://li-lab-sustech.github.io/ . His laboratory combines theoretical optics, software development, and biological applications to push the boundaries of what's possible in optical microscopy for cellular and subcellular observation.

• Super-resolution microscopy
• 3D imaging techniques
• Biomedical Engineering
• +۳ مورد دیگر

Bian Qing serves as an Associate Professor and Master's Supervisor in the Department of Communication at Tongji University's School of Art and Media. His research centers on media sociology, media convergence, and news production dynamics, with affiliations including the Health Communication Professional Group of the Chinese Preventive Medicine Association. He has held visiting positions at City University of Hong Kong (2017) and the University of Cambridge (2019-2020). Education: PhD in Radio and Television, Fudan University School of Journalism (2009-2012) MA in Radio and Television Journalism, Communication University of China (2004-2007) BE in Recording Engineering, Beijing Broadcasting Institute (2000-2004) Research Focus: Professor Bian investigates algorithmic governance in social media , hate speech regulation , and newsroom transformations in digital ecosystems. His work critically examines emotional drivers of online collective action and the sociopolitical dimensions of Chinese media landscapes, bridging theoretical frameworks with empirical analysis of platform dynamics. Publication Trends: Recent works (2021-2022) analyze algorithmic bias and cyber hate in global social platforms, while earlier research (2012-2016) explores Chinese social media's role in public diplomacy and journalism evolution. His scholarship consistently addresses media's societal impacts through convergent journalism frameworks and digital discourse analysis. Awards: National and university research grants including National Social Science Fund Youth Project (150,000 yuan) Multiple Outstanding Advisor Awards for China Data Journalism Competition (2019, 2021, 2022) Tongji University Young Talent Program recognition (2011, 2013) Shanghai European Society Youth Paper Award (2014) Mentorship & Grants: He has secured significant funding from national and provincial sources for projects on online public discourse and media convergence. His student guidance has yielded award-winning data journalism projects, and he serves as an SSCI journal reviewer while teaching courses including 'Converged Media Reporting' and 'Frontiers of Global Journalism Practice'. Research Infrastructure: Affiliated with Tongji University's Media Experiment and Practice Teaching Center and Omnimedia Research Institute, his work integrates experimental media production with sociological analysis of digital communication systems.

• Media Sociology
• Media Convergence
• News Production
• +۲ مورد دیگر

Professor Liu Hongyan is a full-time Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management, where he has served since 1994, achieving the rank of Professor in 2011 after previously holding positions as Associate Professor (2003-2011) and Teacher. His research bridges theoretical data science with practical applications across e-commerce, healthcare, and social media platforms. Education: PhD in Management, School of Economics and Management, Tsinghua University (2001) His research focuses on big data management , machine learning , and business intelligence with specialized expertise in personalized recommendation systems , medical/financial data analysis , and computer vision applications . Recent work integrates large language models and causal inference to solve complex problems in short video platforms, live streaming, and healthcare analytics, emphasizing real-world impact through industry collaborations. Analysis of his 15 most recent publications (2023-2025) reveals a strong trajectory toward multimodal AI systems combining recommendation engines with computer vision, particularly in 3D animation for advertising and healthcare. Key trends include LLM-enhanced display advertising, emotion-aware facial animation, and medical image annotation using adversarial learning, while maintaining core contributions to behavioral data mining in social networks. Scientific recognition includes: National Archives Administration's Outstanding Scientific and Technological Achievement Award Multiple Best Paper Awards at international conferences Outstanding Doctoral Dissertation Supervisor designation from the Society for Management Science and Engineering Special Award for National Natural Science Foundation project on user behavior pattern discovery Professor Liu has secured leadership roles in major National Natural Science Foundation projects including Innovation Research Groups and international cooperation initiatives. His industry impact is demonstrated through patented recommendation systems adopted by multiple companies, particularly in personalized content delivery for live streaming and short video platforms. As an Outstanding Doctoral Dissertation Supervisor, he mentors the next generation of data science researchers. He serves as Deputy Director of Tsinghua University's Center for Artificial Intelligence and Management Research and holds key positions in national academic societies including the E-Commerce and Cyberspace Management Committee (China Management Modernization Research Association) and the Information Systems Engineering Committee (Chinese Society for Systems Engineering).

• Big data management and analysis
• machine learning
• data mining
• +۷ مورد دیگر

Professor LIU Kaijun is a distinguished space plasma physicist currently serving as Professor and Deputy Head of Department at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He previously held academic positions at Auburn University in Alabama, USA, where he was promoted from Assistant Professor (2012) to tenured Associate Professor (2017). His career includes significant research experience at Los Alamos National Laboratory and the Finnish Meteorological Institute following completion of his doctoral studies. 2007: Ph.D. in Space Plasma Physics, Cornell University 2002: M.S. in Space Physics, Peking University 1999: B.S. in Space Physics, Peking University Professor Liu specializes in theoretical and computational space plasma physics, with particular expertise in plasma instabilities and wave-particle interactions in Earth's radiation belts. His work also encompasses pickup ion dynamics in the heliosphere and solar wind-planet interactions. His research employs advanced computational techniques including particle-in-cell simulations and linear stability analysis to understand fundamental plasma processes. Analysis of Professor Liu's publication record from 2015-2018 reveals a consistent focus on electromagnetic wave phenomena in space plasmas, particularly examining Bernstein instabilities, Alfvén-cyclotron waves, and magnetosonic waves. His work demonstrates strong interdisciplinary connections between theoretical plasma physics, computational modeling, and spacecraft observations, with significant contributions to understanding radiation belt dynamics and heliospheric plasma processes. National-level Leading Talent (2019) Professor Liu has established a robust research program with extensive collaborations across international institutions including Los Alamos National Laboratory, NASA missions, and various universities. His work has been supported by research grants enabling advanced computational studies of space plasma phenomena. He advises graduate students in space physics and leads research projects investigating fundamental plasma processes relevant to space weather prediction and heliospheric physics. Professor Liu directs a computational plasma physics research group at SUSTech's Department of Earth and Space Sciences, utilizing high-performance computing resources to simulate complex plasma phenomena in Earth's magnetosphere and the heliosphere. His team collaborates with observational space physics groups to validate theoretical models against spacecraft measurements from missions including Van Allen Probes and THEMIS.

• Space Plasma Physics
• Theoretical Plasma Physics
• Computational Plasma Physics
• +۵ مورد دیگر

Zhe Dong is an Associate Professor (Researcher) and doctoral supervisor in the Department of Chemistry at the Southern University of Science and Technology . He obtained his B.S. from Peking University (2012), Ph.D. from the University of Chicago (2018), and conducted postdoctoral research under 2021 Nobel laureate David MacMillan at Princeton University (2018–2021). Current focus on photoredox-enabled organoboron chemistry and heterocycle functionalization Developed stereospecific coupling reactions for chiral quaternary carbon synthesis Innovated carbocation-carbanion substitution reactions and bioimaging probes His 15+ publications include Nature , JACS , and Nature Chemistry papers spanning: Photoredox-mediated deoxygenation Pd/NBE cooperative catalysis Protein hydrophobic pocket mapping Natural product total synthesis C-H bond functionalization strategies Scientific Recognition: National Scholarship for Outstanding Self-funded Students (2015) Reaxys PhD Prize Finalist (2017) University of Chicago Best Thesis Award (2018) Overseas High-level Talent Program (2020) Current funding includes Ministry of Science and Technology Key R&D Program (2021–2024) and participation in Peacock Team and Guangchuang Team research consortia.

• Organic Synthesis
• Photoredox Catalysis
• Organoboron Chemistry
• +۴ مورد دیگر

Dr. Xiong Yi is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Computational Design and Fabrication (CoDeFab) research group, focusing on the integration of computational design methods with advanced manufacturing technologies, particularly in the field of additive manufacturing. Dr. Xiong has established himself as a leading researcher in computational design for additive manufacturing, with a strong international research background spanning Europe and Asia. Dr. Xiong's educational journey includes: Doctor of Science (DSc) in Engineering Design and Production from Aalto University, Finland (2012-2016) Master of Science (MSc) in Machine Automation from Tampere University of Technology, Finland (2010-2012) Bachelor of Engineering (BEng) in Mechanical Engineering from Hubei University of Technology, China (2006-2010) Dr. Xiong's research primarily focuses on computational design and fabrication methodologies, with particular emphasis on design for additive manufacturing (DfAM), intelligent manufacturing systems, and smart materials. His work bridges the gap between theoretical design principles and practical manufacturing constraints, developing novel approaches for the production of complex engineered products. He has pioneered research in continuous fiber-reinforced composite additive manufacturing, developing innovative process planning and optimization techniques that enable the production of high-performance structural components. His research in electrothermally controlled origami and 4D printing of smart materials represents cutting-edge work at the intersection of materials science, mechanical engineering, and computational design. Dr. Xiong's recent publications reveal a strong focus on continuous fiber-reinforced composites, with significant contributions to 4D printing, metamaterials, and intelligent process planning. His work integrates computational design with manufacturing constraints, creating novel approaches for topology optimization, toolpath planning, and structural design that consider both performance requirements and manufacturability limitations. The research demonstrates increasing sophistication in materials science applications, particularly in programmable materials and multi-functional structures. Dr. Xiong has received multiple prestigious awards for his research contributions, including: Best Presentation Award at the 24th Chinese Conference on Mechanisms and Machine Science (IFToMM CCMMS2024) Best Presentation Award at the International Conference on Frontiers of Additive Manufacturing Research (RAAM 2024) Best Paper Award at the International Conference on Design for 3D Printing (ICD3DP 2023) PhD Scholarship from Aalto University (2016) Research Travel Grant from the International Association for Vehicle System Dynamics (IAVSD) (2013) National Scholarship from the Ministry of Education (2008) As a dedicated educator and mentor, Dr. Xiong serves as a PhD supervisor at SUSTech and has successfully guided students who have gone on to pursue advanced studies and careers at prestigious institutions including Hong Kong Polytechnic University, Beihang University, DJI Innovations, and Singapore's A*STAR research institute. His research is supported by multiple competitive grants, including key projects from the National Key R&D Program of China, the National Natural Science Foundation of China, and provincial and municipal funding agencies. Dr. Xiong also serves on the editorial board of the Journal of Engineering Design and as a guest editor for Composites Communications, contributing to the advancement of his field through scholarly service. Dr. Xiong leads the CoDeFab research group, which maintains a strong collaborative culture focused on 'design leading manufacturing, manufacturing driving design, and digital-intelligent integration.' The group has developed several advanced manufacturing platforms, including multi-axis continuous fiber-reinforced composite additive manufacturing systems, smart composite additive manufacturing platforms, and multifunctional soft matter open manufacturing platforms. With a focus on practical applications and innovation, the CoDeFab group actively collaborates with industry partners and has established a joint laboratory to bridge academic research with industrial implementation.

• Computational Design and Fabrication
• Design for Additive Manufacturing
• Adaptive Process Planning
• +۶ مورد دیگر

Liu Leo Liu is an Associate Professor and tenure-track faculty member in the Department of Chemistry at Southern University of Science and Technology (SUSTech), Shenzhen, China, leading research on multi-active ambiphilic main group centers. He serves on editorial boards for Chem (2020-2023), European Journal of Inorganic Chemistry (since 2022), Chinese Chemical Letters (since 2022), Inorganic Chemistry Frontiers (since 2024), and Acta Chimica Sinica (since 2024). His educational background includes: B.Sc. in Chemistry, Xiamen University (2007-2011) Ph.D. in Organic Chemistry, Xiamen University (2011-2016) with joint study at UC San Diego (2013-2015) Post-doctoral Fellow, University of Toronto (2016-2019) under Prof. Douglas W. Stephan Post-doctoral Scholar, Lawrence Berkeley National Laboratory & UC Berkeley (2019-2020) under Profs. F. Dean Toste, Kenneth N. Raymond, and Robert G. Bergman Dr. Liu's research pioneers the synthesis of novel ambiphilic compounds across d- and p-block elements, integrating synthetic chemistry, computational modeling, and spectroscopy. His group develops multi-active centers that challenge textbook knowledge while enabling practical applications in catalysis and materials science. Key focus areas include low-valent carbon species, isolable carbenes, and main group element chemistry with inverted electronic configurations. His publication record demonstrates consistent innovation in ambiphilic main group chemistry, with recent breakthroughs in crystalline stannyne, σ0π2 carbenes, and carbyne anions featured in Science, Nature Chemistry, and JACS. These works reveal fundamental principles of bonding and reactivity while expanding synthetic possibilities for unconventional molecular architectures. Significant scientific recognition includes: Chinese Chemical Society Young Chemist Award (2022) Huang Yao-Zeng Metal Organic Chemistry Young Award (2023-2024) Distinguished Lectureship Award from Chemical Society of Japan (2024) Chinese Chemical Society-RSC Young Chemist Award (2024) World's Top 2% Scientists (2024) As a doctoral supervisor, Dr. Liu mentors graduate researchers and received SUSTech's Outstanding Graduate Supervisor Award (2025). His research is supported by National Natural Science Foundation grants including Original Exploration Program, Excellent Youth Fund (Overseas), General Program, and Youth Fund C Project. The LLL group maintains active collaborations with international institutions and contributes to major chemistry conferences through invited lectures on ambiphilic main group chemistry. The LLL group operates as a dynamic interdisciplinary team within SUSTech's chemistry department, combining experimental synthesis with computational analysis to explore frontier areas of main group element chemistry. Their work bridges fundamental discovery and potential applications in catalysis, materials science, and chemical synthesis.

• Main Group Chemistry
• Organic Chemistry
• Inorganic Chemistry
• +۴ مورد دیگر

Cao Haishan is an Associate Professor at Tsinghua University, affiliated with the Department of Energy and Power Engineering in the School of Mechanical Engineering. His research focuses on cryogenic cooling systems, high heat flux thermal management, and the physics of amorphous ice formation and phase transitions. He leads a research group supported by the National Natural Science Foundation of China and industry partners including Huawei, Midea, and Lenovo. Ph.D., Mechanical Engineering, University of Twente, 2013 M.Sc., Chemical Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 2009 B.Sc., Chemical Engineering, Zhejiang University, 2006 Dr. Cao's research spans three major areas: cryogenic cooling (including micro cryocoolers and sorption systems), high heat flux electronic cooling (especially with non-condensable gases), and the formation and transformation of amorphous water ice. His work combines theoretical modeling, computational simulation, and experimental validation, often at micro and nano scales. He applies principles from thermodynamics, fluid dynamics, and materials science to solve engineering challenges in refrigeration and thermal control. The recent publications reflect a strong trend toward interdisciplinary research, integrating machine learning for heat transfer prediction, computational screening of MOFs for cryogenic switches, and fundamental studies of ice nucleation on various substrates. The articles span journals in physics, engineering, materials, and applied thermal sciences, indicating broad impact across multiple domains. Notable scientific awards include: Gustav and Ingrid Klipping Award (2016) Cryogenics Best Paper Award (2017) Annual Teaching Excellence Award, Tsinghua University (2023) Excellent Supervisor Award, Tsinghua University (2024) Multiple First Prize Advisor awards in national student contests on energy saving Dr. Cao has been principal investigator on several grants, including projects funded by the National Natural Science Foundation of China on amorphous ice lifetime and micro-cryocooling for semiconductor chips. He has also led industry-university collaborations with Huawei, Midea, and Lenovo. He advises graduate students and leads a research team focused on next-generation cooling technologies. He serves on editorial boards for Journal of Refrigeration , Vacuum and Cryogenics , and Energies , and has chaired sessions at major international conferences such as ICEC-ICMC and ACTS. His research group operates within the Institute of Thermophysics at Tsinghua University, leveraging facilities in the Lee Shau Kee Science and Technology Building. The team collaborates with national laboratories and international institutions, particularly maintaining ties with the University of Twente. Current efforts are directed toward ultra-low vibration cooling, efficient separation of non-condensable gases, and extending the stability of amorphous ice for cryobiological applications.

• Cryogenic Cooling
• Micro Cryocoolers
• Joule-Thomson Cooling
• +۱۲ مورد دیگر

Shi Li is a Professor in the Theory Group at the Department of Computer Science and Technology, School of Computer Science, Nanjing University. He previously held faculty positions at the University at Buffalo (2015–2023) as Assistant and Associate Professor, and was a Research Assistant Professor at Toyota Technological Institute at Chicago (2013–2015). He earned his Ph.D. from Princeton University in 2014 under Moses Charikar and completed his B.S. in Computer Science and Technology at Tsinghua University, where he was part of Andrew Chi-Chih Yao’s Special Pilot Class. His research lies at the intersection of theoretical computer science and combinatorial optimization, with a focus on the design and analysis of algorithms for problems in clustering, scheduling, network design, facility location, and online algorithms. He also explores learning-augmented algorithms and differential privacy in algorithmic contexts. His work combines deep theoretical insights with practical algorithmic frameworks, often leveraging linear programming relaxations, iterative rounding, and randomized techniques. The recent publications highlight a consistent trend in approximation algorithms, particularly in clustering (e.g., correlation clustering, fair k-set selection), scheduling (e.g., unrelated machine scheduling, load balancing), and robust optimization. His work frequently appears in top-tier theoretical venues such as STOC, FOCS, SODA, and ICALP, with increasing emphasis on fairness, privacy, and efficiency in algorithm design. Best Paper Award of Track A, ICALP 2011 Co-winner of Best Paper Award, FOCS 2012 Invited to Special Issue of SICOMP (FOCS 2017 paper) Best Paper Award, COCOON 2018 Invited to Special Issue of SICOMP (STOC 2019 paper) Best Paper Award of Track A, ICALP 2024 Outstanding Paper Award, SPAA 2024 Shi Li has advised several PhD and master’s students, including Yuda Feng, Han Dai, Zihao Liang, and Jia Ye, and has mentored postdoctoral researcher Ruilong Zhang. He has served on numerous program committees (e.g., STOC, SODA, ICALP) and is an Editorial Board Member of ACM Transactions on Algorithms . He teaches core algorithm courses such as Design and Analysis of Algorithms and Advanced Algorithms , and actively collaborates with researchers worldwide. His lab focuses on theoretical foundations of efficient and fair algorithm design, with applications in large-scale data analysis and distributed systems.

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Combinatorial Optimization
• +۱۱ مورد دیگر

Xu Xiang is a Tenured Professor and Vice Dean at Tongji University School of Art and Media , where he serves as Director of the Big Data and Computational Communication Research Center. He is also Editor-in-Chief of Computing, Intelligence, and Communication , reviewer for CSSCI journals, and former Deputy Secretary-General of Beijing Society of Literature and Art. PhD in Literary Theory (Beijing Normal University, 2009) M.A. in Chinese Language and Literature (Tsinghua University, 2006) B.A. in Sociology (Nanjing University, 2004) His research focuses on computational communication, particularly analyzing social media networks, user behavior, and information dissemination. He established key theoretical frameworks including 'Opinion Paradigm', 'Social Echo Chamber', 'Influence Circle', 'Idol Conformity', 'Emotional Setting', and 'Silent Public Opinion', with extensive publications on social media emotional dynamics, information narrowing, and cultural communication mechanisms. His 15 most recent publications (2017-2022) demonstrate consistent exploration across computational communication theory, algorithmic platform analysis, and social media's role in cultural dissemination. Key research trends include information narrowing mechanisms, emotional preference patterns, and structural transformations in social media ecosystems. Fourth China 'News Communication Academic Award' (2018) Youth New Media Academic Research 'Qi Hao Award' (2018) National News Academic Youth Scholar Award (2012) Beijing-Taiwan Research Award (2013) Beijing Outstanding Talent Funding (2011) Tongji University '13th Five-Year Plan' Scientific Research Advanced Individual (2020) As an award-winning mentor, he guided student teams to first, second, and third prizes in multiple national competitions including China Data Journalism Competition and National Undergraduate New Media Creative Competition. His research bridges academic theory with policy practice, with findings receiving Central Politburo member instructions and extensive national media coverage.

• Journalism
• Communication
• Computational Communication
• +۵ مورد دیگر

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

• Operations Management
• Stochastic Control
• Decision-Making under Uncertainty
• +۹ مورد دیگر

Lei Bu is a Professor and Vice Dean at the Software Institute , Nanjing University . He leads research in formal verification, cyber-physical systems, and software engineering, with a focus on bounded model checking and hybrid system analysis. B.Sc. and Ph.D. in Computer Science from Nanjing University (2004, 2010) Visiting student at Carnegie Mellon University and University of Texas at Dallas His research integrates formal methods and machine learning for verifying complex systems like IoT and software with real-time constraints. Key projects include BACH Toolset and BRICK for reachability analysis. Recent publications demonstrate expertise in hybrid system verification , cache side-channel detection , and parallel code analysis frameworks . His work bridges theoretical advancements with practical applications in safety-critical systems. Zhongchuang Software Talent Award (2023) CCF-IEEE CS Young Computer Scientist Award (2022) High-Tech Software Innovation Awards (2019-2023) As Principal Investigator, he leads major projects funded by National Science Foundation of China and Jiangsu Natural Science Foundation (2020-2027). Current tools include BACH for hybrid systems and MLB for Java symbolic execution.

• Model Checking
• Bounded Model Checking
• Formal Methods
• +۷ مورد دیگر

Shaowei Wang is a Professor in the Department of Engineering Mechanics at the School of Civil Engineering, Shandong University, China. He holds a Ph.D. in Mathematics (2007) from Shandong University and completed post-doctoral research in fluid mechanics (2009) at Peking University. His research focuses on heat and mass transfer in porous media , fluid mechanics , and mathematical methods in mechanics . His recent publications highlight advancements in electro-osmotic flows, oscillatory non-Newtonian fluid dynamics, and stability analysis of bioconvection in porous media. Key themes include modeling viscoelastic fluids (Oldroyd-B, Maxwell), fractional calculus applications, and microchannel transport phenomena. First Prize in Natural Science of the Ministry of Education (2015) Second Prize of Natural Science Award of Shandong Province (2015) Du Qinghua Young Scholar Award (2016) Wang serves as Director of Shandong Society of Mechanics and editorial board member of multiple journals. His work bridges theoretical fluid dynamics with practical applications in microfluidics and geophysical systems.

• Heat and mass transfer in porous media
• Fluid mechanics
• Mathematical methods in mechanics