Dr. Yanjie Fu received his Ph.D. degree from Rutgers, the State University of New Jersey in 2016, the B.E. degree from University of Science and Technology of China in 2008, and the M.E. degree from Chinese Academy of Sciences in 2011. 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, AAAI, IJCAI, VLDB, WWW, ACM SIGIR. His research has been recognized by: 1) two federal junior faculty awards: US NSF CAREER and NSF CRII awards; 2) five best paper (runner-up, finalist) awards, including ACM KDD18 Best Student Paper Finalist, IEEE ICDM14, 21, 22 Best Paper Finalist, ACM SIGSpatial20 Best Paper Runner-up; 3) three industrial awards: 2016 Microsoft Azure Research Award, 2022 Baidu Scholar global top Chinese young scholars in AI, 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention in Data Mining; 4) several other university-level awards: Reach the Stars Award, University System Research Board Award and University Interdisciplinary Research Award. He was chosen for the nation’s 100 early career engineers by the 2023 Grainger Foundation Frontiers of Engineering Symposium, the National Academy of Engineering. 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, machine learning, and their interdisciplinary applications. His research aims to develop robust machine intelligence with imperfect and complex data by building tools to address framework, algorithmic, data, and computing challenges. His recent focuses are spatial-temporal AI, graph learning, reinforcement learning, learning with unlabeled data, stream learning and distribution drift. He is a senior member of ACM and IEEE.