Kian-Lee Tan is a Tan Sri Runme Shaw Senior Professor and Professor of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a Ph.D. (1994), M.S. (1992), and B.Sc. (1st Class Honours) from NUS. His academic career spans decades of contributions to database systems and data analytics. Ph.D. in Computer Science, National University of Singapore (1994) M.S. in Computer Science, National University of Singapore (1992) B.Sc. in Computer Science (1st Class Honours), National University of Singapore As a leading researcher in database systems, Tan focuses on query processing and optimization in multiprocessor/distributed systems, database performance, security, and multimedia information retrieval. His work extends to computational biology applications like genome databases and real-time influence analysis on social streams. His recent publications highlight trends in GPU-accelerated graph analytics, trajectory pattern mining, and computational journalism. These works emphasize parallel processing, performance optimization, and social/media data analysis. IEEE Technical Achievement Award (2013) President Science Awards, Singapore (2011) NUS Graduate School Excellent Mentor Award (2010/2011) Outstanding University Researchers Award (1997/1998) Tan has supervised numerous research projects and mentored students contributing to database systems. He secured significant grants including a US$1 million Ripple Foundation grant (2024) for financial technology education. His editorial roles include ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He leads the FinTech Lab at NUS Computing and has served on the VLDB Endowment Board (2012-2017). His work bridges database foundations with emerging applications in AI, fintech, and computational journalism.
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.
LU Wen Feng is an Adjunct Associate Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), affiliated with the College of Design and Engineering. His research focuses on advanced manufacturing technologies, including additive manufacturing, robotics, and AI-driven systems. He explores sustainable design methodologies, smart manufacturing innovations, and bioprinting applications. Key areas include optimizing material processes, enhancing mechanical properties of printed materials, and developing autonomous robotic solutions for industrial tasks. Contact: mpelwf@nus.edu.sg , located at E3-02-07. Research Interests : His work bridges AI and manufacturing, emphasizing Knowledge graph integration for additive manufacturing, Autonomous robotic systems in industrial settings, Bioprinting for tissue repair with smart bioinks, Topology optimization for lightweight and sustainable structures, Material characterization and process engineering for 3D-printed composites. Recent Article Trends : LU Wen Feng's 2025 articles highlight advancements in AI-augmented manufacturing systems (e.g., MaViLa, AutoMEX) and sustainable design workflows. His 2024 studies address material anisotropy, corrosion behavior, and topology optimization strategies for lattice structures. These trends reflect his interdisciplinary approach to solving challenges in additive manufacturing, robotics, and biomedical applications. Awards : No scientific awards explicitly mentioned. Advising & Grants : No current graduate students or grants listed. His research likely integrates industry-academia collaborations given the focus on applied manufacturing technologies. Labs/Teams : Not explicitly detailed, but his work suggests involvement in advanced manufacturing labs and AI-robotics teams at NUS.
Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Lizi Liao is an Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , specializing in Artificial Intelligence and Conversational AI . Her research bridges Machine Learning , Natural Language Processing , and Multimodal Systems , focusing on proactive dialogue systems, multimodal conversational search, and task-oriented interactions. Education : PhD in Computer Science (2019) from the National University of Singapore (NUS) , advised by Professor Tat-Seng Chua . Research Interests center on principles of human conversational understanding and machine implementation, particularly in proactive conversational agents , multimodal dialogue systems , and target-driven conversation planning . Key applications include emotional support systems , intelligent shopping assistants , and learning companions . Recent Publications (2024-2025) highlight her work on LLM-based proactive dialogue , multimodal emotion recognition , and dynamic graph modeling , often integrating NLP , Multimedia , and Knowledge Graphs . Collaborative projects with her CoAgent Lab team emphasize human-AI interaction and ethical response generation . Scientific Awards : Google South Asia & Southeast Asia Research Award 2023 Lee Kong Chian Fellow Teaching includes Visual Analytics for Business Intelligence (undergraduate) and Text Analytics and Application (graduate). She also serves as Associate Editor for TOIS and TOMM , and organizes tutorials at ACL , SIGIR , and WSDM .
Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.
Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.
FANG Yuan is a tenured Associate Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He holds the prestigious Lee Kong Chian Fellowship and leads research in artificial intelligence and data science. His institutional affiliation includes: School of Computing and Information Systems, Singapore Management University Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2014) Bachelor of Computing (First Class Honors), National University of Singapore (2009) - Top student in Computer Science Research Focus: Dr. FANG specializes in data mining, machine learning, and AI with emphasis on graph learning, information networks, recommendation systems, and knowledge graph applications. His work bridges theoretical foundations with practical applications in social analytics, biomedical informatics, and digital transformation, often employing advanced neural network architectures. Publication Trends: Recent works (2024-2025) demonstrate strong focus on graph machine learning innovations, including graph foundation models, prompt-based learning for dynamic graphs, and LLM-graph integrations. Key themes include few-shot/zero-shot learning, non-homophilic graph processing, and applications in recommendation systems, bioinformatics, and NLP. Methodological advancements frequently involve contrastive learning, transformer architectures, and explainable AI techniques. Awards & Honors: Lee Kong Chian Fellow World's Top 2% Scientist (2024) by Stanford/Elsevier #1 Most Influential Paper at WWW'23 (GraphPrompt) - Paper Digest (2024-09) Top 5 Most Influential Papers at WWW'23 (GraphPrompt) - Paper Digest (2024-05) Top Computer Science Graduate, NUS (2009) Student Advising: Currently advises doctoral candidates including DONG Viet Hoang, LIU Ran, and NIU Yudong. Recently supervised Dr. Zhongzhou Liu's successful PhD defense (2024) on trustworthy recommendation systems. Professional Engagement: Regularly organizes tutorials at premier venues (WWW, KDD) and delivers invited talks internationally on graph learning advancements. Leads multiple research projects in collaboration with industry partners.
CHAN Chee Yong is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS) . He earned his Ph.D. in Computer Science from the University of Wisconsin-Madison and holds B.Sc. and M.Sc. degrees in Computer Science from NUS. Education : Ph.D., Computer Science, University of Wisconsin-Madison M.Sc., Computer Science, NUS B.Sc., Computer Science (1st Class Honours), NUS His research focuses on database systems , emphasizing query processing and optimization , transaction management , and database usability . He has contributed extensively to XML data dissemination, skyline computation, and multicore database performance optimization, with publications in venues like ACM SIGMOD, VLDB, and IEEE ICDE. Recent publications show increasing emphasis on join optimization , transaction healing , and spatial-keyword queries , reflecting trends in multicore systems, complex query processing, and XML data management. His work combines theoretical rigor with practical applications in distributed databases and data engineering. Notable professional roles include Associate Editor for the VLDB Journal , ACM SIGMOD Record , and IEEE Transactions on Knowledge and Data Engineering . He has served on program committees for major conferences like SIGMOD, ICDE, and VLDB across 2003-2026. Dr. Chan has supervised 9 PhD students and 10 M.Sc./M.Comp. students , including WANG TaiNing (2021), LI Meiying (2020), and TRAN Quoc Trung (2011). His advisees have been placed in institutions like the Institute for Infocomm Research and Huawei Shannon Lab.
Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.
Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.
**FENG Mengling** is an Associate Professor at the National University of Singapore (NUS) and holds primary affiliation with the Saw Swee Hock School of Public Health. She serves as the Domain Leader for the Biostatistics, Modelling, AI and Data Analytics (B.MAD) Domain and Director of the AI for Public Health (AI4PH) Program. Her academic credentials include a Senior Post-doc from Harvard-MIT Health Science Technology Division, a PhD from Nanyang Technological University (2009), and a Bachelor's degree (2003) from NTU. Research & Teaching: Her research focuses on causal inference for evidence-based medicine, generative models for medical time-series analysis, and healthcare data analytics. She teaches courses on big data technologies for healthcare problems and healthcare data analytics. Professional Roles & Awards: She has led the Biomedical and Healthcare Analytics Lab at the Institute for Infocomm Research (2014–2015) and currently serves as an Affiliate Scientist at Harvard-MIT. Notable accolades include the MIT Teaching & Learning Laboratory Kaufman Teaching Certificate and recognition as a finalist in MIT’s 2013 Innovation Showcase. Her work has been featured in prominent media outlets like The Straits Times and Channel NewsAsia, highlighting breakthroughs such as AI nurses and Singlish-speaking healthcare assistants. Publications & Impact: Over 50 peer-reviewed publications span AI-driven clinical decision support, medical imaging analysis, and predictive modeling in critical care. Key contributions include frameworks like MedDreamer (reinforcement learning for EHR analysis) and DivScore (LLM-generated text detection). Her research bridges causal inference, generative AI, and scalable healthcare solutions. Labs & Initiatives: As a leader in NUS’s Public Health AI Innovation Center (launching early 2025), she drives initiatives like FxMammo (AI for breast cancer screening) and the Biomedical and Healthcare Analytics Lab. Her work emphasizes ethical AI deployment and cross-disciplinary collaboration in healthcare.
Heyuan Shi is an Associate Professor at the School of Electronic Information, Central South University since 2023. He earned his B.S. (2015) and Ph.D. (2020) from Central South University and Tsinghua University respectively. His research focuses on software quality assurance with emphasis on kernel fuzz testing , open source software security , and AI application security . Presided over 10+ projects including NSFC General Program (No.62472448) and National Key R&D Sub-Project Published 30+ CCF-A/B papers across software security, machine learning, and quantum testing Supervised 15+ graduate students in software quality assurance areas His recent 2024-2025 publications demonstrate expertise in: LLM-enhanced patch classification Quantum neural network verification Hypergraph adversarial attacks RTOS fuzzing techniques Scientific recognition includes: 2024 Beijing Science & Technology Progress Award (First Prize) Hunan Province Xiaohe Sci-Tech Talent (2023) China Association for Science & Technology Young Talent (2025-2027) Active in academic service as PC member for FM2024 and reviewer for IEEE Transactions journals. Leads industry collaborations with Alibaba and Beijing Institute of Aerospace Metrology.
