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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
• +1 more

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
• +9 more

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
• +7 more

Xuehai Qian is a Tenured Full Professor in the Department of Computer Science at Tsinghua University since July 2024. Prior to this, he served as an Associate Professor at Purdue University (2022-2024), Assistant Professor at the University of Southern California (2015-2022), and Postdoctoral Researcher at the University of California Berkeley (2013-2015). Ph.D. in Computer Science, University of Illinois at Urbana-Champaign, USA (2013) ME in Computer Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, China (2007) BE in Computer Science and Technology, Beihang University, China (2004) His research focuses on parallel computer architecture , hardware/software co-designed domain-specific architectures for graph analytics and machine learning , hardware security , and quantum computer architecture . He has pioneered scalable cache coherence protocols for atomic block execution, hardware sequential consistency violation detection, and distributed frameworks for graph processing leveraging emerging memory technologies. Xuehai Qian’s publications span computer architecture , graph analytics , machine learning systems , and quantum computing , with a strong emphasis on distributed systems , accelerators , and memory optimization . His work includes novel architectures for graph processing, decentralized training protocols, and ReRAM-based accelerators for deep learning. NSF CAREER Award (2018) ACSIC (American Chinese Scholar In Computing) Rising Star Award (2019) IEEE Senior Member (2019) Hall of Fame inductions: ASPLOS (2018), HPCA (2019), ISCA (2021), MICRO (2021) W.J. Poppelbaum Memorial Award (2013) He has advised students who have received Microsoft Research Lovelace Fellowships , Ph.D. Fellowships , and Facebook Fellowships . His research has been supported by grants such as the NSF SPX project on FPGA-based machine learning platforms and smaller NSF grants. Notably, he has served as an Associate Editor for Science China (Information Sciences) since 2023 and as a Guest Editor for IEEE Transactions on Parallel and Distributed Systems (2019).

• Computer Architecture
• Computer Systems
• Hardware Security
• +3 more

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
• +7 more

Maggie Wenjing Liu is a tenured Associate Professor in the Department of Marketing at Tsinghua University's School of Economics and Management (SEM). She holds leadership roles as Associate Director of the Computational and Behavioral Science Lab and China Retail Research Center, and Chair of the Marketing Department's Academic Seminar Committee. Education: Ph.D. in Marketing, Rotman School of Management, University of Toronto M.S. in Marketing, NUS Business School, National University of Singapore Bachelor in Economics, University of International Business and Economics Research Focus: Her work centers on consumer behavior , product/service experience , decision science , and pricing , examining psychological mechanisms through experimental and analytical methods. Key themes include ritualistic consumption, AI-human interaction in marketing, visual branding effects, and cross-cultural service responses. Publication Trends: Recent work (2017-2025) appears in top journals like Production and Operations Management (UTD) and Journal of Consumer Psychology (FT50), revealing strong interdisciplinary connections between behavioral science, marketing strategy, and data-driven consumer insights with increasing focus on AI applications and crisis response. Scientific Awards: National Natural Science Foundation Youth Project Outstanding Evaluation (2017) National Natural Science Foundation General Project Outstanding Evaluation (2021) Tsinghua University teaching reform project Grade A (2020) Beijing Higher Education Young Elite Scholar Award Two-time Outstanding Expert Reviewer (Journal of Marketing Science) Society of Asian Retailing and Distribution Best Paper Award Advising & Grants: As a doctoral advisor, she mentors PhD candidates who frequently co-author publications. She secured three National Natural Science Foundation General Programs, one Youth Project, three provincial/ministerial grants, and multiple Tsinghua projects, with two NSFC projects rated Outstanding upon completion. Labs & Leadership: She co-directs Tsinghua SEM's Computational and Behavioral Science Lab and China Retail Research Center, driving research at the intersection of marketing analytics, consumer neuroscience, and retail innovation while serving on editorial boards for Marketing Letters and Journal of Business Research .

• Consumer Behavior
• Product and Service Experience
• Decision Science
• +3 more

Wei Hu is a full Professor and Ph.D. supervisor in the Department of Computer Science and Technology at Nanjing University, China. He earned his Ph.D. and B.S. from Southeast University in 2009 and 2005, respectively, and joined Nanjing University faculty in 2009. He has held visiting positions at Stanford University (2014-2015), University of Texas at Arlington (2016-2017), and University of Toronto (2017) as a visiting scholar/professor. Research Interests focus on Knowledge Graphs : Representation learning, foundation models, and error detection Databases : Entity alignment, crowdsourcing, and blockchain integration Digital Medicine : Collaboration with Nanjing University's National Institute of Health Data Science Publication Trends show expertise in knowledge graph reasoning, federated learning, and biomedical applications. Recent works include in-context learning for graph reasoning and blockchain-based data fusion systems. Scientific Awards include Huawei 2025 Challenges Spark Award ASE 2024 Distinguished Paper Award CHIP 2021 Best Paper Award CCKS 2018 Best English Paper Award Nanjing University Study Abroad Program Awardee (2013) IBM China Excellent Student (2008) Advising involves leading the Knowledge Fusion Group at Nanjing University, mentoring 18 current and former Ph.D. and Master's students. Professional services include editorial roles at Transactions on Graph Data and Knowledge and Big Data Research , plus committee memberships in CCF, CIPSC, and JSCS.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +5 more

Zhuoren Jiang is a tenure-track Assistant Professor and Doctoral Supervisor at the Department of Information Resources Management, School of Public Affairs, Zhejiang University. He holds a Ph.D. and serves as a key faculty member in the department, with his office located in Room 612-2 of the School of Public Affairs building at Zhejiang University's main campus in Hangzhou. Dr. Jiang's research spans multiple interdisciplinary domains, with primary focus on Information Science & Library Science, Computational Social Science, Policy Simulation, and Artificial Intelligence Algorithms and Applications. His work bridges social science, data science, and computer science, creating innovative approaches to understanding complex social phenomena through computational methods. He has developed expertise in graph representation learning, large language models, and their applications in scientific impact prediction, legal reasoning, and policy analysis. His publication record reveals a strong trajectory in high-impact venues, with recent work focusing on multimodal business analysis, legal judgment prediction, policy style measurement, and scientific peer review systems. His research demonstrates an increasing emphasis on integrating large language models with graph-based approaches to solve complex interdisciplinary problems, particularly in the domains of legal informatics and scientific communication. Zhejiang University Teaching Excellence Award, 2024 Best Short Paper Awards Nominee, SIGIR Conference, 2024 Alibaba Research Fellowship, 2019 & 2020 Boya Postdoctoral Fellowship, Peking University, 2016 Best Poster Award, JCDL, 2013 Dr. Jiang actively mentors students and seeks highly self-motivated PhD and master's candidates interested in interdisciplinary research. He has served as principal investigator or sub-project lead for multiple research projects including National Key R&D Program, Major Projects of the National Social Science Foundation of China, and Projects of the National Natural Science Foundation of China. His academic service includes editorial board membership for Frontiers in Big Data and peer reviewing for numerous top journals including Information Processing & Management and Journal of the Association for Information Science and Technology. He maintains active collaborations with industry, having previously served as a consultant (visiting professor) at Alibaba DAMO Academy and currently as a collaborating expert at Tongyi Lab. His professional memberships include ACM (Association for Computing Machinery), ACM SIGIR, ACL (Association for Computational Linguistics), and ASIS&T (Association for Information Science and Technology).

• Information Science & Library Science
• Computational Social Science
• Policy Simulation
• +3 more

Guo Xunhua is a Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management. He serves as Party Branch Secretary and holds significant leadership roles including Executive Director and Secretary-General of the China National Association for Information Technology (CNAIS), Vice President of the China Society of Information Economics, and Secretary-General of the Information Management Committee of the China Management Modernization Research Association. His educational background includes dual bachelor's degrees in Management Information Systems and Computer Science and Technology from Tsinghua University (2000), followed by master's and doctoral degrees in Management Science and Engineering from the same institution (2005). He has been teaching at Tsinghua since 2005 and maintains international academic connections through visiting positions at RWTH Aachen University and MIT Sloan School of Management. Guo's research spans Management Information Systems, E-commerce, Social Networks, and Business Intelligence, with a focus on business intelligence methods in internet environments and behavioral patterns under emerging information technologies. His work integrates both 'technology' and 'behavior' perspectives within information systems research. His recent publications reveal a strong trend toward AI applications in e-commerce, personalized recommendation systems, social commerce dynamics, and the psychological aspects of digital interactions. Ministry of Education Changjiang Scholar Young Scholar (2019) Ministry of Education Outstanding Achievement Award for Scientific Research in Humanities and Social Sciences Beijing Philosophical and Social Sciences Outstanding Achievement Award Beijing Higher Education Teaching Achievement Award Multiple Tsinghua University Teaching Achievement Awards Professor Guo serves as Assistant Editor-in-Chief of the Journal of Information Systems and sits on editorial boards for international journals including Information Technology & People, Electronic Commerce Research, and Journal of Global Information Management. His teaching portfolio includes graduate courses in IT and Organization, and undergraduate courses in Principles of Computer Systems, Management Information Systems, and Introduction to Management Science and Engineering Research.

• Management Information Systems
• E-commerce
• Social Networks
• +4 more

Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +2 more

Florian Bossmann is an Associate Professor at the College of Mathematics, Harbin Institute of Technology (HIT), specializing in Applied Signal Processing with a focus on algorithms and applications. His research encompasses inverse problems, greedy algorithms, sparsity, and compressed sensing, with applications in seismic exploration, ptychography, and video processing. He holds a PhD in Mathematics from the University of Göttingen (2013) and a Diplom in Mathematics from the University of Duisburg-Essen (2009). Current projects include NSFC-funded seismic data interpolation and a start-up grant for applied signal processing research. He teaches courses like 'Signal and Image Processing' and 'Calculus for Civil Engineering'. Supervising Master/PhD students in signal processing, he emphasizes collaboration and innovation. His lab, the ASP Group, explores cutting-edge methods in multidimensional data reconstruction and algorithm design. Prominent publications focus on impedance inversion, neural network applications in geoscience, and object reconstruction techniques. He has led collaborative research initiatives with institutions like the Fraunhofer Institute and the Helmholtz Center for Environmental Health.

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +5 more

Xiaowang Zhang is a Full Professor at the College of Intelligence and Computing, Tianjin University, China. His research spans Knowledge Graphs, Natural Language Processing, Multi-modality, and Software Engineering, with a focus on semantic reasoning, relation extraction, and software vulnerability detection. He leads a team advising international students and contributes to open-access research in venues like ACL, WWW, and ISWC. Education : PhD from Peking University (2011), Postdoc at Hasselt University (2011-2014), Visiting Associate Professor at Griffith University (2018). His research integrates Knowledge Graphs for relation extraction and rule learning, advances LLMs in few-shot learning and cross-lingual stance detection, and addresses software engineering challenges through vulnerability description analysis and code translation error detection. Recent work emphasizes temporal reasoning and explainability in AI systems. Article trends highlight interdisciplinary applications of Knowledge Graphs in NLP tasks, leveraging graph structures for QA systems, and improving software security through feature inference and multi-interest recommendation frameworks. While no explicit awards are listed, his collaborations with institutions like Griffith University and Hasselt University demonstrate global academic engagement. He advises international students and emphasizes programming skills, communication, and teamwork in future advisees. His full_description expands on 15+ years of contributions to knowledge representation, semantic networks, and code generation systems.

• Knowledge Representation and Reasoning
• Natural Language Processing
• Machine Learning
• +3 more

Zhiyuan Liu is a Tenured Associate Professor in the Department of Computer Science and Technology at Tsinghua University, Beijing, China. He has held this position since December 2021, having progressed through the ranks from Assistant Professor (2016-2017), Tenure-track Associate Professor (2017-2021), and Assistant Researcher (2013-2016). His academic journey at Tsinghua began with his undergraduate studies (2002-2006), continued through his PhD (2006-2011), and included a postdoctoral fellowship (2011-2013). Dr. Liu's research centers on Large Language Models , Natural Language Processing , Knowledge Graphs , and Social Computing . His work spans knowledge-guided NLP, representation learning, and practical applications in domains including legal intelligence and multimodal systems. He has made significant contributions to knowledge representation learning, network embedding techniques, and few-shot natural language processing methodologies. His publication record demonstrates a clear evolution toward large-scale language models and knowledge-integrated NLP systems. Recent work focuses on efficient pretraining and fine-tuning methods for LLMs, multimodal LLMs, tool learning with LLMs, and domain-specific large models for scientific intelligence. His research shows strong interdisciplinary applications, particularly in legal intelligence and social computing, with several papers receiving prestigious recognition including F5000 awards and conference best paper honors. First Prize of Qian Weichang Chinese Information Processing Science and Technology Award (2024) World Internet Conference Awards for Pioneering Science and Technology (2023, 2022) Wu Wenjun Artificial Intelligence Science and Technology Award (2023) First Prize of the State Natural Science Award (2022, 2020) MIT Technology Review 35 Innovators Under 35, China (2018) National Young Talents Program (2020) Dr. Liu has mentored over 20 PhD students and numerous Master's students, many of whom have received top honors including Tsinghua Outstanding PhD Theses, Beijing Outstanding Graduate awards, and Siebel Scholarships. His research is supported by major grants including the Key Program of the National Natural Science Foundation of China for 'Theories and Methods of Few-shot Natural Language Processing' (2023-2027), and the Beijing Municipal Science & Technology Commission project on 'Sparse Modular Large Model Technology' (2023-2025). He leads research in the Natural Language Processing Laboratory at Tsinghua University, where his team develops cutting-edge LLM infrastructure, knowledge computing systems, and applications in legal intelligence. The lab maintains strong industry collaborations with Tencent, Baidu, and AMD, and has contributed to several successful startups emerging from his research.

• Large Language Models
• Natural Language Processing
• Knowledge Graphs
• +4 more

Kemi Ding is an Associate Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), leading the LEMONS lab (Learning and Mathematical Optimization in Networked Systems). Her career spans postdoctoral research at Arizona State University and Nanyang Technological University, followed by roles at SUSTech since 2021. Education Ph.D., Electronic and Computer Engineering, HKUST (2018) B.E., Electronic and Information Engineering, HUST (2014) Visiting Student, Harvard SEAS (2016-2017) Her research bridges control theory, game theory, signal processing, and machine learning , focusing on cyber-physical system security/privacy , networked estimation , and graph-based learning methods . She has published in top journals like Automatica and IEEE Transactions series, emphasizing secure and robust networked systems. Key publication trends include: Game-theoretic approaches to sensor security and deception strategies Optimization of estimation under bandwidth and attack constraints Graph signal processing for networked system modeling Privacy conflict resolution in social networks Resilient control theory under adversarial conditions

• Control Theory
• Game Theory
• Signal Processing
• +4 more

Wei Chen is a Professor at the State Key Laboratory of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, China. His academic journey began and ended at Zhejiang University, where he earned both his Bachelor's (1996) and Ph.D. (2002) in Computer Science, followed by a visiting Ph.D. position at the Fraunhofer Institute in Germany (2000-2002) and a visiting scholar role at Purdue University (2006-2008). Research Focus: Visualization, Visual Analytics, Biomedical Image Computing, Big Data Analytics. Academic Leadership: Served on editorial boards of journals like IEEE Transactions on Visualization and Computer Graphics, Journal of Visual Informatics, and as Chair of IEEE Pacific Visualization Symposium. Impact: Over 110 IEEE/ACM Transactions and CCF-A papers, four Chinese textbooks on visualization, and two monographs on big data technology. His recent publications (2023-2024) span domains like visual analytics for health insurance fraud detection, quantum circuit visualization, federated learning visualization, urban mobility modeling, and AI-enhanced cultural heritage preservation. A thematic trend shows integration of deep learning, large language models, and human-in-the-loop systems with visual interfaces. Scientific Awards include the Zhejiang Province Natural Science First Prize (2021, 2017), CCF Technology Invention First Prize (2020), Wu Wenjun Artificial Intelligence Science and Technology Progress First Prize (2023), and multiple Best Paper/Poster recognitions at IEEE VIS and Pacific Visualization conferences. He actively mentors graduate students and leads the Visual Analytics Group , contributing to national and international standards in visualization research. His editorial and organizational roles in top conferences further solidify his influence in shaping the field.

• Data Visualization
• Visual Analytics
• Biomedical Image Computing
• +5 more