Dr. Yanjie Fu

Associate Professor
School of Computing and AI
Ira A. Fulton Schools of Engineering
Arizona State University

Office: Tempe campus, BYENG 476
Email:  yanjie.fu AT asu.edu

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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, the State University of New Jersey in 2016, the B.E. degree from the University of Science and Technology of China in 2008, and the M.E. degree from the 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 early career engineers by the National Academy of Engineering 2023 Grainger Foundation Frontiers of Engineering Symposium. 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 currently serves as an Associate Editor of ACM Transactions on Knowledge Discovery from Data and Mathematics. He is a senior member of ACM and IEEE.

For Prospective Students

[TA/RA positions in PhD program] I am looking for self-motivated, creative and hard-working Ph.D. students. Please feel free to email me your CV if you are interested in my research.

ASU is part of the prestigious Association of American Universities (AAU) that represents a select group of the elite in leading research universities. ASU is classified as a Carnegie Research I (R1) university. The School of Computing and AI is hosting Computer Science, Data Science, Computer Engineering, Industrial Engineering doctoral programs. ASU is ranked #1 for innovation, #34 in Engineering (Undergraduate), #41 in Engineering (Graduate), #46 in Computer Science (#27 among public universities), #21 in Artificial Intelligience (CS Speciality), #16 in Cybersecurity, #36 in Computer Engineering (22 among public universities), #19 in Industrial Engineering (#12 among public universties) according to US News 2024, and is ranked #48 according to CSRankings.ORG and ranked #27 according to Research.COM . ASU School of Computing and AI is proud to have large number of alumni from our Ph.D. programs who have become professors and researchers at top universities and world-class research labs.

We are doing basic research motivated by important applications. We are interested in applications at the intersection of machine learning, complex data, and human factors. But most importantly, we hope our application-motivated research will lead to the development of theories, methodologies, and algorithms that are useful for a wide range of important and interesting problems.

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