Short Biography
Dr. Yanjie Fu is an associate professor in the School of Computing and AI at the Arizona State University. He received his Ph.D. degree from the Rutgers University, the B.E. degree from the University of Science and Technology of China (USTC), and the M.E. degree from the Chinese Academy of Sciences (CAS). He has research experience in industry research labs, such as Microsoft Research Asia and IBM Thomas J. Watson Research Center. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, IEEE TMC, ACM TKDD, ACM SIGKDD, ICLR, AAAI, IJCAI, VLDB, IEEE ICDE, WWW, ACM SIGIR. He currently serves as an Associate Editor of ACM Transactions on Knowledge Discovery from Data (TKDD), ACM AI Letters, ACM Transactions on Spatial Algorithms and Systems (TSAS), ACM Transactions on Intelligent Systems and Technology (TIST). He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair for the IEEE Big Data 2026, a Program Vice-Chair for the IEEE Big Data 2025, a Poster Co-Chair for the ACM SIGKDD 2026, a High School and Teen Research Track Co-Chair for IEEE ICDM 2025 and 2026, a Demo Track Co-Chair for ACM SIGSpatial 2025 and 2026. He has served as a panelist of NSF, DOE, NIH, DOT, DARPA proposal evaluation panels.
His teaching and research have been recognized by: 1) three faculty awards: US NAE Grainger Foundation Frontiers of Engineering early career engineer (2023), US NSF CAREER (2021), and US NSF CRII (2018) awards; 2) several best paper (runner-up, finalist) awards (e.g., ACM CIKM 2025 best research paper candidate, IEEE ICDM 2021 best paper finalist, ACM SIGSPATIAL 2020 best runner-up, ACM SIGKDD 2018 best student paper finalist); 3) several community and industrial recognitions: 2025 and 2024 Stanford Elsevier World’s Top 2% Scientists, 2022 Baidu Scholar global top Chinese young scholars in AI, 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention in Data Mining, 2016 Microsoft Azure Research Award; 4) several university-level awards: ASU Fulton Top 5% Teaching Recognition Award, Reach the Stars Award, University System Research Board Award, and University Interdisciplinary Research Award. He is committed to data science education. His graduated Ph.D. students have joined academia as tenure-track faculty members.
He is broadly interested in data mining, AI, and their interdisciplinary applications. His research involves two major efforts: 1) on knowledge-guided learning (Data for AI), how can the structure knowledge of data and science guide AI? His lab has contributed projects including: D4AI-spatial, D4AI-timeseries, D4AI-causal outliers. 2) on data-centric learning (AI for Data), how can AI augment, reprogram, and reshape data to advance data-centric learning? Several contributed projects includes AI4D-RL, AI4D-GenAI, AI4D-LLM. He has been fortunate to work with collaborators from scientific and social domains, including urban and regional planning, earth and environmental science, learning and educational science, disaster and community resilience, social computing.
