جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence and Accounting
• Artificial Intelligence and Auditing
• Intelligent Finance
• +۵ مورد دیگر

Cheng Yuhan is an Assistant Professor at Shandong University's School of Management, serving as director of the Center for Artificial Intelligence and Digital Finance and recognized as a Taishan Scholar Young Expert in Shandong Province. His interdisciplinary research bridges artificial intelligence with finance, accounting, and economics through collaborations with MIT and Tsinghua University. His educational background includes: Bachelor of Science in Mathematical Sciences from Beijing Normal University Double Degree in Economics from Peking University National School of Development PhD in Finance from Tsinghua University PBC School of Finance Cheng's research focuses on AI applications in accounting, auditing, and finance, particularly large language models for financial regulation, asset pricing, and macro-finance. He integrates computational methods to solve complex financial problems using industrial-grade computing infrastructure, enabling novel approaches to traditional economic analysis. His recent publications demonstrate a clear trend toward generative AI in financial modeling, with emphasis on stock factor generation, predictive analytics, and economic forecasting. These works showcase how language models transform financial analysis through automated insight extraction and data-driven decision frameworks. Key honors include: Taishan Scholar Young Expert award Best Paper Award at 2024 China International Risk Forum Outstanding Paper Awards at 13th International Conference on Futures and Derivatives Membership in Shandong Provincial Philosophy and Social Sciences Young Talent Team Cheng mentors students across Master of Accounting, Master of Auditing, and MBA programs while securing competitive grants including National Natural Science Foundation of China Youth Fund. His lab supports students in academic exchanges, with former research assistant Dou Yun admitted to University of Chicago Economics Master's with scholarship. His research lab features industrial-grade hardware including NVIDIA H100, Huawei Ascend 910b, A100, and A800 processors, providing near-tech-company computing power for large-scale AI/finance research and industrial application development.

Zhumin Chenمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Big Data Processing
• +۷ مورد دیگر

Zhumin Chen is a Professor and Associate Dean at the School of Artificial Intelligence, Shandong University. He serves as a member of multiple committees including the Natural Language Processing Committee of China Computer Federation (CCF) and holds leadership roles in research institutions like the Qingdao Key Laboratory of Big Data Technology Innovation. His research focuses on natural language processing, artificial intelligence, and big data, with a strong emphasis on large language models, healthcare applications, and recommendation systems. He has secured over 30 major grants including National Key Research and Development Programs and has published extensively in top venues like ACL, SIGIR, and NeurIPS. His work has been recognized with awards such as the SIGIR 2024 Best Paper Honorable Mention and the Shandong Science and Technology Progress First Prize. He leads the development of large-scale AI models and platforms, including the Shanda Intelligent Assistant, which supports teaching and research across the university. Dr. Chen collaborates with institutions like Microsoft Research Asia and Purdue University, and has co-founded multiple industry partnerships with companies such as Inspur and Hisense. His academic services include reviewing for top conferences (SIGIR, WWW, ACL) and journals, and he actively mentors students through doctoral and master’s programs.

Liu Hongyanمشاهده پروفایل

استاد

• Big data management and analysis
• machine learning
• data mining
• +۷ 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).

Andrew I. Cooperمشاهده پروفایل

استاد

• Advanced organic functional materials
• Conjugated microporous polymers
• Porous organic cages
• +۵ مورد دیگر

Andrew I. Cooper is a Professor at the University of Liverpool, Director of the Materials Innovation Factory, and Honorary Professor at East China University of Science and Technology (ECUST). He pioneers research in advanced organic functional materials, including conjugated microporous polymers, porous organic cages, and porous liquids, and developed the first autonomous mobile robotic chemist. Fields of Interest: Materials chemistry, robotics, AI-driven synthesis, and computational design of functional materials. Scientific Impact: Over 200 SCI-indexed papers, 52,000+ citations, H-index 117. Key journals: Nature , Science , JACS , Angewandte Chemie . Awards: Royal Society of Chemistry Professorship, Outstanding AI Leader award, Fellow of the Royal Society and European Academy of Sciences. Leadership: Editor-in-Chief of Chemical Science , trained 47 Chinese postdoctoral fellows and PhD students, and leads international collaborations with Chinese universities. Research Trends: Articles focus on robotics, AI, computational materials design, photocatalysis, and dynamic porosity in molecular systems.

• Higher education policy and governance
• Talent development and evaluation
• Private higher education
• +۱ مورد دیگر

Liu Xu is an Assistant Professor at Southern University of Science and Technology (SUSTech), affiliated with the Center for Higher Education Research. She serves as the Secretary-General of both the Higher Education Committee of Shenzhen Education Society and the Shenzhen Women Scientists Association. As a doctoral supervisor and Shenzhen high-level introduced talent, she contributes significantly to the academic community. Her research interests focus on higher education policy and governance, talent development and evaluation, private higher education, and regional higher education development. Her work examines critical issues in Chinese higher education, including governance structures of public and private universities, cross-border educational collaborations in the Guangdong-Hong Kong-Macau Greater Bay Area, and the impact of emerging technologies like AI on talent cultivation. Dr. Liu has published over 40 papers in Chinese and English journals, including more than 20 SSCI Q1-Q2 papers and 10+ Chinese CSSCI papers. Her publications appear in prestigious journals such as Higher Education, International Journal of Qualitative Studies, and Nature portfolio journals. She has also authored 4 books on higher education topics. 2022 SUSTech College Excellent Tutor 2024 SUSTech Excellent Teaching Award 2023 Shenzhen Philosophy and Social Sciences Achievement Excellent Award Candidate for the 12th batch of Sichuan Provincial Academic and Technical Leaders Dr. Liu has presided over more than 10 research projects at various levels, including National Social Science Foundation funding and National Education Science Planning projects. She serves on the editorial boards of Higher Education Quarterly (JCI Q2) and Humanities and Social Sciences Communications (JCI Q1), and regularly reviews for multiple academic journals. Her research team actively investigates higher education policy implementation, university-city synergistic development, and interdisciplinary talent cultivation for STEM undergraduates.

Jianke Zhuمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۳ مورد دیگر

Jianke Zhu is a Professor at the College of Computer Science and Technology of Zhejiang University . He obtained his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and conducted postdoctoral research at the BIWI Computer Vision Lab, ETH Zurich . His research focuses on Computer Vision and Machine Learning , with a particular emphasis on 3D scene understanding, LiDAR-based mapping, and neural rendering. Dr. Zhu’s research spans several subfields, including 3D Reconstruction , Semantic Segmentation , Multimodal Learning , and Autonomous Driving . His work integrates Neural Networks , LiDAR Processing , and Uncertainty Quantification to address challenges in real-time and adverse conditions. Selected Recent Trends: 2025 publications highlight his work in Hexagonal Mesh-based Neural Rendering , Instance-aware 3D Scene Understanding , and Efficient Visual Projectors for Multimodal LLMs . Earlier works include Box2Mask for Instance Segmentation (2024) and Token Selection for Point Cloud Learning (2025). Scientific Awards : Senior member of the IEEE Advising and Grants : As a Doctoral Supervisor , he mentors students in advanced topics like LiDAR Odometry and Multi-view Stereo Recovery . His projects have attracted funding for autonomous driving , 3D scene modeling , and neural rendering .

Fang Kongمشاهده پروفایل

استادیار

• Online Learning
• Reinforcement Learning
• Machine Learning
• +۴ مورد دیگر

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.

• Artificial Intelligence
• Evolutionary Algorithms
• Automatic Machine Learning
• +۴ مورد دیگر

Shengcai Liu is an Assistant Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). Holding a PhD in Engineering, he has established himself as a researcher with over 10 publications as first or corresponding author in top artificial intelligence conferences and journals. His academic journey includes research positions at Singapore's Agency for Science, Technology and Research (A*STAR) before joining SUSTech's faculty. Education: PhD in Computer Science and Engineering, University of Science and Technology of China (2014-2020) Bachelor's in Computer Science and Engineering, University of Science and Technology of China (2010-2014) Dr. Liu's research centers on automatic algorithm design and evolutionary approaches to machine learning. He investigates how AI systems can autonomously construct efficient algorithms for complex optimization problems while maintaining robustness and reliability. His work bridges theoretical foundations with practical applications in combinatorial optimization, adversarial machine learning, and large language model development, with particular focus on making algorithm design processes more systematic and effective. His publication record demonstrates a clear trajectory from foundational work on algorithm portfolios to sophisticated integration of evolutionary methods with large language models. Recent work shows increasing focus on textual adversarial attacks, combinatorial optimization, and leveraging large language models as optimization tools themselves. This progression reflects both theoretical depth and practical relevance to current AI challenges. Professional Activities: Project Leader for Huawei-funded research on combinatorial optimization solvers (CNY 400,000) Member of IEEE, IEEE CIS, and AAAI Reviewer for top journals including TPAMI, TEVC, TCYB Conference reviewer for NeurIPS, ICML, AAAI, IJCAI Dr. Liu maintains active research collaborations through the Artificial Intelligence RAMS Technology Innovation Laboratory at SUSTech, where his team focuses on developing next-generation automatic machine learning systems with applications in optimization and security domains.

Wei Huمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۵ مورد دیگر

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.

• Information Science & Library Science
• Computational Social Science
• Policy Simulation
• +۳ مورد دیگر

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).

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +۲ مورد دیگر

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).

• Knowledge Representation and Reasoning
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

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.

Zhiyuan Liuمشاهده پروفایل

دانشیار

• Large Language Models
• Natural Language Processing
• Knowledge Graphs
• +۴ مورد دیگر

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.

Yepang Liuمشاهده پروفایل

دانشیار

• Software Testing and Analysis
• Adaptive Systems
• Mobile Computing
• +۴ مورد دیگر

Yepang Liu is a tenured Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech). He leads the Software Quality Lab and directs the Trustworthy Software Research Center under the Research Institute of Trustworthy Autonomous Systems. His academic journey includes a B.Sc. in Computer Science from Nanjing University (2010) and a Ph.D. in Computer Science and Engineering from The Hong Kong University of Science and Technology (HKUST) in 2015, followed by postdoctoral research at HKUST's CASTLE Lab and Cybersecurity Lab (2015–2018). Research interests focus on Software Testing and Analysis Empirical Software Engineering AI for Software Engineering (AI4SE) Trustworthy AI Mobile Computing Software Security Current projects explore automated testing for emerging technologies like HarmonyOS and extended reality (XR), leveraging large language models (LLMs) and reinforcement learning. Awarded thrice with ACM SIGSOFT Distinguished Paper Awards (ICSE 2021, ASE 2016, ICSE 2014) and a Platinum Level Research Artifacts Honor (FSE 2016), Liu has also received teaching accolades, including SUSTech's Junior Faculty of the Year (2021) and the Top-10 Most Popular Instructor Among 2024 Graduates. His work is funded by the National Natural Science Foundation of China, National Key Research and Development Program of China, and industry partnerships.

Wei Chenمشاهده پروفایل

استاد

• Data Visualization
• Visual Analytics
• Biomedical Image Computing
• +۵ مورد دیگر

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.