جست‌وجوی استاد و پژوهشگر

Chao Huangمشاهده پروفایل

استادیار

• Large Language Models
• Graph Learning
• Recommender Systems
• +۳ مورد دیگر

Chao Huang is an Assistant Professor at the Department of Computer Science and Institute of Data Science at the University of Hong Kong (HKU). As the director of the Data Intelligence Lab@HKU, his research focuses on Large Language Models (LLMs), LLM Agents, Graph Learning, Recommender Systems, and AI for Smart Cities. He holds a PhD from the University of Notre Dame. Education: PhD in Computer Science from University of Notre Dame (USA). Research Interests: His work bridges machine learning with practical applications, including: Developing advanced LLM frameworks like GraphGPT and UrbanGPT Creating automated research tools (AutoAgent, AI-Researcher) Designing recommendation systems with LLM integration (RLMRec, LLMRec) Exploring spatio-temporal AI for urban challenges Notable Achievements: Over 11,000 Google Scholar citations (h-index 55), multiple top conference awards (WWW/SIGIR/KDD), and open-source projects with thousands of GitHub stars. His work has been recognized as most influential/pioneering in major AI conferences. Labs/Teams: Leads the Data Intelligence Lab, collaborating on projects like LightRAG, MiniRAG, and VideoRAG. The lab emphasizes open-source contributions with repositories on GitHub. Grants/Advising: Supervises PhD/MPhil students and offers research internships. Active in recruiting motivated researchers and students through HKU's programs.

Eirini Ntoutsiمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Responsible AI
• +۷ مورد دیگر

Prof. Eirini Ntoutsi is a Professor of Open Source Intelligence at the CODE Research Institute for Cybersecurity and Smart Data , Bundeswehr University Munich . She leads the Artificial Intelligence & Machine Learning (AIML) research group , focusing on adaptive learning, responsible AI, and generative AI. Research Interests: Developing intelligent algorithms for real-world data challenges, addressing fairness-aware machine learning, explainable AI, and generative models. Projects: Co-leads the EU-funded MAMMOth (Multimodal AI for Trustworthy Human-Centric Applications) and STELAR (Spatio-Temporal Linked Data for Agri-food) initiatives. Applications: Deploying AI solutions in education, social networks, banking, agriculture, manufacturing, and engineering. Key Contributions: Developed the MMM-Fair open-source toolkit for fairness analysis with no-code interface. Actively contributes to conferences like ECML PKDD , FAccT , IJCNN , and WWW .

Brendan Jubaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Theoretical Computer Science
• Machine Learning
• +۲ مورد دیگر

Brendan Juba is an Associate Professor at Washington University in St. Louis , affiliated with the School of Engineering and the Department of Computer Science and Engineering . He is also associated with the Division of Computational & Data Sciences and the Machine Learning & Artificial Intelligence group. Brendan earned his PhD from MIT in 2010 under Madhu Sudan , followed by a postdoctoral fellowship at Harvard University with Leslie Valiant until 2014. He was also jointly affiliated with CSAIL and the Center for Science of Information through 2012. His PhD thesis, Universal Semantic Communication , was published by Springer. Research Interests His work focuses on theoretical approaches to artificial intelligence , particularly algorithms for integrated learning and reasoning (e.g., common sense reasoning ), and computational complexity . He has also explored communication protocols without standards and the science of information in contexts like data mining and logical reasoning. His research spans the intersection of machine learning, formal logic, and algorithmic efficiency. Scientific Awards & Collaborations While specific awards are not listed, his work has been supported by the NSF Big Data Analytics REU site grant (IIS-1560191) and collaborations with researchers like Loizos Michael and Manuel Blum . He has presented tutorials at AAAI-18 and seminars at institutions like the Center for Computational Intractability and the IAS CSDM seminar . Teaching & Supervision He has taught courses such as CSE 347 (Analysis of Algorithms) , CSE 513T (Theory of AI & Machine Learning) , and CSE 519T (Advanced Machine Learning) . His supervision includes PhD students like Golnoosh Dehghanpoor and Zihao Deng , as well as Masters students (e.g., Roger Iyengar , Phoebe Chua ).

Sue Bok Moonمشاهده پروفایل

استاد

• Networking Systems
• Online Social Networks
• Computer Science
• +۳ مورد دیگر

Sue Bok Moon is a Professor at the School of Computing, Korea Advanced Institute of Science and Technology (KAIST) in Daejeon, South Korea. She received her B.S. and M.S. from Seoul National University in Computer Engineering in 1988 and 1990, respectively, and her Ph.D. in Computer Science from the University of Massachusetts at Amherst in 2000. After working at Sprint ATL from 1999 to 2003, she joined KAIST as an Assistant Professor in August 2003, was promoted to Associate Professor in 2006, and became a Full Professor in 2012. Her educational background includes: Ph.D. in Computer Science, University of Massachusetts Amherst (2000) M.S. in Computer Engineering, Seoul National University (1990) B.S. in Computer Engineering, Seoul National University (1988) Professor Moon's research focuses on networking systems and online social networks. Her work spans internet measurement, network architecture design, social network analysis, and cloud computing infrastructure. She has made significant contributions to understanding information propagation through social networks and optimizing network infrastructure for performance and efficiency. Her research has practical applications in improving internet services, social media platforms, and data center operations, with recent work emphasizing memory disaggregation techniques, network performance optimization, and analysis of manipulation patterns in online comment systems. Her recent publications demonstrate a strong focus on memory disaggregation systems, high-performance networking, and social network analysis. She has published extensively in top-tier conferences including ACM SIGCOMM, Internet Measurement Conference (IMC), and WWW, with her 2022 paper receiving the ACM SIGCOMM Internet Measurement Conference Test of Time Award. Professor Moon has received numerous prestigious awards including the 2023 Best Paper Award at ACM SIGCOMM APNet, election to the National Academy of Engineering in Korea in 2022, and the Presidential Commendation by the Ministry of Science and ICT in 2019. She has also been recognized for promoting women in STEM with awards such as the Amore Pacific Award for Outstanding Women in the Sciences in 2009. She has held significant leadership positions including serving on the Board of Directors for KB Kookmin Bank and KISTEP, Co-Chair of Leaders' Forum at KOFWST, and Vice President of WiTECK (Women in Science, Engineering and Technology in Korea). Her academic service includes General Co-Chair roles for COMSNETS 2019 and ACM SIGCOMM CoNEXT 2017, and membership on the ACM Publications Board. Professor Moon leads the Advanced Networking Laboratory at KAIST, located in the IT Convergence Building (N1), which focuses on cutting-edge research in networking systems and social network analysis. She has also been actively involved in promoting women in STEM through talks including 'Why Korea Needs More Women in STEM' for International Women's Day 2023 by UN Women Centre of Excellence for Gender Equality.

• Data Science
• Network Science
• Computational Social Science
• +۵ مورد دیگر

Yu-Ru Lin is an Associate Professor at the School of Computing and Information , University of Pittsburgh , and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber) . With secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program, their interdisciplinary work bridges computational methods with social science inquiry. Research interests focus on: Social and political network analysis Computational social science Data mining and visualization Digital accountability in political communication Policy diffusion analytics Trust-distrust dynamics in networked societies Key research themes include: Developing network theories for misinformation and extremism Designing visual analytics for policy diffusion and group bias detection Studying collective behavior during emergencies Creating computational tools for social data analysis Major grants include NSF awards for: Public policy diffusion analytics COVID-19 misinformation mitigation Urban mobility and smart city initiatives Understanding extremism in cyber-social spaces Scientific leadership roles: Editor-in-Chief of ICWSM Editorial board member at Frontiers in Big Data, EPJ Data Science, PLOS ONE, and Nature Scientific Reports Co-chair roles at KDD Humanitarian Mapping Workshop, WWW Web & Society Track, and multiple SBP-BRiMS graduate programs As founder of the Pitt Computational Social Dynamics Lab (PICSO LAB) , they lead research on social media analysis, network modeling, and urban informatics through projects like PittSmartLiving – a $1.4M NSF-funded initiative exploring real-time transit incentives and community engagement.

Elena Zhelevaمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Causal Inference
• +۳ مورد دیگر

Elena Zheleva is an Associate Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). She leads the EDGES Lab, focusing on unifying causal inference, machine learning, and network analysis to address societal challenges. Her research bridges data science, privacy, and AI fairness, with applications in social networks, health, and policy. She earned her Ph.D. from the University of Maryland in 2011. Research Interests: Dr. Zheleva's work spans data science, machine learning, causal inference, graph mining, and privacy. She develops methods to address biases in relational data, designs personalized privacy tools, and studies network interference effects. Key applications include social media analysis, healthcare informatics, and algorithmic fairness. Publication Trends: Her recent articles emphasize causal inference in networked environments , tackling problems like peer effects, diffusion interference, and bias in ranking systems. She frequently publishes in top-tier venues (e.g., UAI, WWW, KDD), showcasing innovations in experimental design, fairness-aware AI, and graph-based learning. Awards & Honors: NSF CAREER Award (2021) COE Research Award (2021) DCFemTech Award (2017) Best Paper Honorable Mention at ICWSM 2020 Advising & Grants: She mentors 5 Ph.D. candidates and has graduated 13+ students. Her lab secured major grants from NSF, DARPA, Adobe, and Anthem for projects on relational causal inference, COVID-19 attitudes, and privacy-aware systems. Key grants include NSF CAREER, TRIPODS, and DARPA EDIFICE. Leadership: Dr. Zheleva co-organizes workshops (e.g., KDD tutorials on causal inference), serves as associate editor for ACM TIST and DAMI, and is program chair for SDM 2025. She leads the EDGES Lab, fostering collaborations in computational social science and AI ethics.

Yu Wangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Machine Learning
• +۷ مورد دیگر

Yu Wang serves as an Assistant Professor in the Department of Computer and Information Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in graph-based machine learning and AI systems. Joining the faculty in September 2024 after completing his PhD at Vanderbilt University, he directs the Graph Machine Learning Lab and actively recruits PhD students for cutting-edge projects at the intersection of data mining, network analysis, and trustworthy AI. His educational background includes a Ph.D. in Computer Science from Vanderbilt University (2019-2024) and a B.S. from Harbin Institute of Technology (2015-2019). During his doctoral studies, he completed research internships at Adobe Research and The Home Depot, focusing on knowledge graph applications for information retrieval. Wang's research centers on graph machine learning, with emphasis on neural-symbolic learning, LLM integration with structured knowledge, and trustworthy AI systems. His work addresses critical challenges in imbalanced/biased graph learning, generative graph models, and social network analysis, driving applications in cyber-security, biochemistry, and infrastructure systems. Current projects explore agentic social simulations and spatial-temporal machine learning for real-world impact. His publication trajectory shows increasing focus on LLM-graph integration and trustworthy AI, with recent work spanning generative graph models, personalized LLMs, and robust networking systems. Key trends include bridging symbolic reasoning with neural approaches, advancing evaluation benchmarks for social network analysis, and developing privacy-preserving techniques for graph-based retrieval systems. Outstanding Doctoral Student Award (Vanderbilt, 2023-2024) Best Paper Award at NeurIPS GLFrontiers Workshop (2023) Vanderbilt Graduate Leadership Anchor Award for Research (2023) KDD Outstanding Dissertation Award Honorable Mention (2025) NSF IIS-III Core Program Grant as Lead PI (2025) Top-10 Most Influential Papers at CIKM'22 and WWW'23 Wang mentors PhD students including Yongjia Lei (SDM Doctoral Forum Honorable Mention recipient) and Riya (Pulse Research Fellow), with research funded through NSF grants and industrial collaborations with Adobe, Visa, and Home Depot. His lab provides substantial computational resources including L40S GPUs and OpenAI API access, supporting both theoretical innovation and real-world deployments through industry internships. Current advising focuses on generative graph models, LLM-agent collaboration, and trustworthy AI frameworks for societal applications. The Graph Machine Learning Lab maintains strong ties with industrial research groups, facilitating student internships at Adobe Research and other tech companies while developing open-source tools like ChemicalX for drug discovery. Ongoing projects include GraphRAG frameworks for security applications and social simulation environments for studying network dynamics.

Ashton Andersonمشاهده پروفایل

دانشیار

• AI and Society
• Computational Social Science
• Human-AI Interaction
• +۳ مورد دیگر

Ashton Anderson is an Associate Professor in the Department of Computer Science at the University of Toronto. He specializes in research at the intersection of AI, data, and society, focusing on human-AI collaboration, algorithmic fairness, and computational social science. Anderson leads the Computational Social Science Lab , which investigates social dynamics through computational models. Education: Master's in Computer Science (Stanford University, 2010) Education: Bachelor of Engineering in Software Engineering (McGill University, 2008) His work bridges artificial intelligence with social science, examining behavioral modeling, algorithmic ethics, and social media dynamics. Recent projects analyze human-AI alignment in chess, transparency in LLMs, and polarization in online discourse. Key publication trends include: Human-AI interaction frameworks Explainability and fairness in algorithmic systems Behavioral stylometry through chess data LLM optimization and contextual learning Social polarization and content virality Scientific recognition includes: Best Paper runner-up at WWW 2014 Google PhD Fellowship in Social Computing (2012) Two Honorable Mentions at CHI 2025 Anderson's research involves collaborations with institutions like Microsoft Research, Google, and Facebook's Core Data Science team. His lab explores societal impacts of large language models and develops methodologies for ethically aligned AI systems.

Huang Fangمشاهده پروفایل

پژوهشگر

• Optimization
• Learning theory
• Data mining
• +۳ مورد دیگر

Huang Fang is a Research Scientist at Baidu Research's Cognitive Computing Lab, specializing in optimization theory, machine learning, and data mining. With a strong academic background from top institutions, Huang has established themselves as a prolific researcher in the field of machine learning, particularly in federated learning, privacy-preserving algorithms, and optimization methods. Ph.D. in Computer Science, 2021, University of British Columbia M.S. in Statistics and Computer Science, 2017, University of California, Davis B.Sc. in Financial Mathematics, 2015, Central University of Finance and Economics (China) Huang Fang's research spans multiple critical areas in machine learning, with a particular focus on optimization theory and its applications. Their work bridges theoretical foundations with practical implementations, especially in federated learning systems where privacy and efficiency are paramount. The research portfolio demonstrates expertise in developing novel algorithms for large-scale learning problems, with particular emphasis on convergence analysis, sparse optimization, and scalable methods for knowledge representation. Huang has made significant contributions to understanding the theoretical properties of optimization algorithms under various constraints and settings. The publication record reveals a strong trajectory in advancing machine learning methodologies, with recent work focusing on differentiable neuro-symbolic reasoning, efficient Markov logic networks, and improved convergence guarantees for privacy-preserving stochastic optimization. The research shows a consistent pattern of addressing fundamental challenges in distributed and large-scale machine learning, particularly in the areas of federated learning fairness, efficient coordinate descent methods, and matrix completion techniques. Huang Fang has demonstrated exceptional productivity in high-impact venues including NeurIPS, ICML, ICLR, WWW, and other top-tier machine learning conferences. The work consistently combines theoretical rigor with practical applicability, making significant contributions to both the foundational understanding of optimization methods and their implementation in real-world systems.

Mary Shawمشاهده پروفایل

استاد

• Software Architecture
• Value-Based Software Engineering
• End-User Software Engineering
• +۶ مورد دیگر

Mary Shaw is the A.J. Perlis Professor of Computer Science at Carnegie Mellon University's School of Computer Science (SCS), with a joint appointment at the Software Engineering Institute (SEI). She co-founded the SEI and served as Chief Scientist (1984-1987). Research Focus: Software architecture, value-based software engineering, end-user software engineering, and software reliability modeling. Education: Pioneered software engineering education frameworks and graduate curriculum guidelines. Grants: Involved in NSF-CCF-0438929, NSF-CCR-0325273, and Sloan Software Industry Center projects. Scientific Awards: National Medal of Technology and Innovation Stibitz Computer Pioneer Award Warnier Prize (1993) Fellow of IEEE Computer Society and AAAS Publications Trends: Recent work spans AI ethics in software, design space theory, adaptive systems, programming language philosophy, and empirical software research. She emphasizes bridging theoretical research with practical applications through projects like Vitruvius for software architecture and EUSES for end-user programming tools.

• Algorithms
• Combinatorial Optimization
• Discrete Metric Spaces
• +۷ مورد دیگر

Hubert Tsz-Hong Chan is an Associate Professor in the Department of Computer Science, School of Computing and Data Science at The University of Hong Kong. He earned his PhD in 2007 from Carnegie Mellon University under the supervision of Anupam Gupta, followed by post-doctoral research at the Max-Planck-Institut für Informatik (2007-2009). Education PhD in Computer Science, Carnegie Mellon University, 2007 Research Interests Dr Chan's research lies at the intersection of algorithms , combinatorial optimisation , discrete metric spaces , and security & privacy . A recurring theme is the design of provably efficient approximation algorithms for geometric and graph-theoretic problems under realistic or adversarial settings. Representative contributions include polynomial-time approximation schemes (PTAS) for TSP and Steiner Forest in doubling metrics, spectral analysis of hypergraph Laplacians, and foundational work on differential obliviousness and oblivious RAM. Publications & Trends With more than 80 peer-reviewed papers in premier venues such as JACM , SIAM Journal on Computing , Algorithmica , FOCS , SODA , EUROCRYPT , ASIACRYPT , CCS , and WWW , his recent output (2018-2021) demonstrates a shift toward privacy-preserving algorithms, differential obliviousness, and socially-aware optimisation models, often combining rigorous theory with practical datasets like Netflix and Twitter. Scientific Awards IPDPS 2019 Best Paper Award WWW 2018 Honorable Mention Students & Mentoring Dr Chan has successfully graduated 17 PhD and MPhil students and currently mentors 7 PhD candidates and 1 MPhil student. His graduates have secured academic and industry positions worldwide, and their theses frequently build on his funded projects. Research Funding & Labs Since 2012 he has been Principal Investigator on 12 competitive grants from the Hong Kong Research Grants Council (RGC), totalling more than HK$8 million, spanning topics from privacy-preserving aggregation to Byzantine-resilient federated learning. While no dedicated laboratory name is advertised, his group operates within the Security & Privacy and Algorithms Labs in the Department of Computer Science.

• Complex systems
• Combinatorial optimization
• Evolutionary computation
• +۵ مورد دیگر

Sebastien Verel is a Researcher at the Laboratoire d'Informatique de l'Université du Littoral Côte d'Opale (LISIC) . His work focuses on complex systems, combinatorial optimization, evolutionary computation, and fitness landscape analysis. Research Interests: Multiobjective optimization, metaheuristics, adaptive algorithms, and simulation-based modeling Collaborations: Active in international conferences (GECCO, EvoCOP, PPSN) with teams from UK, Italy, Australia, and Morocco Key Publications (2023-2025) address topics like: Pareto local optimal networks for multiobjective landscapes Surrogate-assisted optimization using Walsh decomposition Fitness landscape analysis across ML models and protein engineering Urban mobility benchmarks (bus spacing in Calais)

Yixiang Fangمشاهده پروفایل

دانشیار

• Data Management
• Data Mining
• Artificial Intelligence
• +۸ مورد دیگر

Yixiang Fang is an Associate Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Previously, he was a Research Associate at the School of Computer Science and Engineering, the University of New South Wales (UNSW) during 2018-2020, working with Prof. Xuemin Lin. He received his PhD from the Department of Computer Science at the University of Hong Kong (HKU) in 2017, advised by Prof. Reynold Cheng, and was a visiting scholar at Nanyang Technological University in 2016. Dr. Fang's research focuses on data management, data mining, and artificial intelligence over big data, particularly big graph data. His work centers on community search, densest subgraph discovery, path/connectivity computation, and LLM + Graph (including graph-based retrieval augmented generation). He has made significant contributions to the field, particularly in cohesive subgraph search and densest subgraph discovery, with his works on community search being well-recognized by leading IT companies like Huawei and ByteDance. His research has resulted in numerous publications in top-tier conferences and journals including SIGMOD, VLDB, ICDE, NeurIPS, WWW, AAAI, IJCAI, TODS, VLDBJ, and TKDE. Notably, his SIGMOD 2020 paper was selected as one of the Best Papers and was awarded the 2021 ACM SIGMOD Research Highlight Award. 2025 CUHK-Shenzhen Teaching Achievement Award (Excellent Prize) 2021 ACM SIGMOD Research Highlight Award Best Paper Award, International Conference on Spatial Data and Intelligence (SpatialDI) 2021 Presidential Young Fellow at CUHK-Shenzhen, 2021 One of the Best Papers, SIGMOD 2020 CCF-Ant Research Fund (2025) Dr. Fang has advised numerous students including PhD candidates, MPhil students, and undergraduate researchers. He has received research funding from Huawei Cloud, CCF-Huawei Populus euphratica Fund, and ByteDance. Currently, he serves as an Editorial Board Member for Information Processing & Management (IPM) and has held leadership roles in major conferences including serving as PC co-chair for LLM+Graph@VLDB2025 workshop. He is also the coach of the CUHK-Shenzhen Programming Contest Team.

Jiawei Chenمشاهده پروفایل

پژوهشگر ارشد

• Recommender Systems
• Information Retrieval
• Machine Learning
• +۵ مورد دیگر

Jiawei Chen is a "Hundred Talent" Research Fellow at Zhejiang University's College of Computer Science and Technology, with over 60 publications in top-tier venues including WWW, SIGIR, KDD, and NeurIPS. His research focuses on advancing recommender systems through innovative approaches to debiasing, graph-based learning, and large language model integration. His educational background includes: Ph.D. in Computer Science, Zhejiang University (2017-2020) Master's in Computer Science, Zhejiang University (2014-2017) Bachelor's in Micro-electronic, University of Electronic Science and Technology of China (2010-2014) Chen's research centers on solving fundamental challenges in recommender systems, particularly addressing popularity bias through spectral analysis and causal inference. His work bridges graph mining, knowledge representation, and large language models to develop robust recommendation frameworks. Recent contributions include pioneering graph transformers for ranking optimization and counterfactual reasoning to burst filter bubbles. His influential surveys on recommendation debiasing and deep clustering have established foundational taxonomies for the field. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Deepening causal approaches to bias mitigation through counterfactual interventions and distributionally robust optimization, (2) Advancing graph-based architectures with sign-aware transformers and uncertainty-aware structure learning, and (3) Integrating large language models through knowledge distillation techniques to enhance sequential recommendation. His work consistently targets high-impact solutions to popularity bias while maintaining strong theoretical grounding. His research excellence has been recognized with: Best Paper Award at WSDM 2025 for spectral analysis of popularity bias amplification Best Paper Honorable Mention at SIGIR 2023 for offline reinforcement learning in recommendation Chen actively mentors future researchers and seeks self-motivated graduate students for projects in recommendation systems, LLMs, and graph mining. His extensive service as program committee member for WWW, AAAI, KDD, and SIGIR—along with reviewing for IEEE TNNLS, TKDE, and TOIS—demonstrates significant community leadership. He has released valuable community resources including the KuaiRec and KuaiRand datasets for unbiased recommendation research. While specific lab affiliations aren't detailed, Chen maintains strong collaborative ties with researchers across institutions, particularly with Prof. Xiangnan He's group. His work frequently involves large-scale industrial datasets and open-source tools like EasyRL4Rec, indicating leadership in practical recommendation system development and community resource sharing.

• Information Retrieval
• Recommendation Systems
• Conversational AI
• +۷ مورد دیگر

Pengjie Ren is an academic lecturer specializing in information retrieval, recommendation systems, and conversational AI. He holds teaching roles in courses such as Introduction to Big Data Processing Technology, Probability Theory and Mathematical Statistics, Ph.D Professional English, and Statistical Natural Language Processing. As a supervisor, he has guided both PhD and Master's students in research-oriented projects. His research focuses on advancing conversational systems, generative models, and reinforcement learning applications in recommendation systems. He has contributed to top conferences like SIGIR, WWW, AAAI, and ACL, with notable work on knowledge-grounded dialogues, sequential recommenders, and debiasing techniques in AI. Pengjie actively serves the academic community as an organizing committee member (NLPCC 2021, YSSNLP 2021) and workshop co-chair (SIGIR AIIS 2020, WSDM NLP4REC 2020). He is also a program committee member for major conferences and a reviewer for journals like TOIS, TKDE, and IPM. His teaching and research emphasize practical applications of AI in dialogue systems, privacy-aware recommendation, and multi-domain learning, with a focus on bridging theory and real-world implementation.