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Dr. Yanjie Fu

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

Office: Tempe campus, BYENG 506
Email:  yanjie.fu AT asu.edu

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 in 2016, 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 and ACM AI Letters.

His teaching and research have been recognized by: 1) three junior 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., 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: Fulton Engineering 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 Data for AI (D4AI), how can the structure knowledge of data guide AI? His lab has contributed projects including: D4AI-spatial, D4AI-timeseries, D4AI-causal outliers. 2) on AI for data (AI4D), how can AI augment, reprogram, and knowledgize data? Several contributed projects includes AI4D-RL, AI4D-GenAI, AI4D-LLM. His recent focuses are space-time intelligence, data-centric AI, sim2decision. He also explores emerging topics on multimodal reasoning, LLM and agentic AI with his students. 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.

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Prospective students

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. We not just pursue research ideas and their impact, but also hope to help students to become professors, researchers, and entrepreneurs.

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. ASU is ranked #36 in USA by QS world university ranking 2026. The School of Computing and AI is ranked #39 in Computer Science, #36 in Computer Engineering (#21 among public universities), #24 in Artificial Intelligence (CS Speciality), #18 in Industrial Engineering (#13 among public universities) according to US News 2025, and is ranked #41 according to CSRankings.ORG and ranked #31 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 leading research labs.

Interested in working with me? You should…

  • Have a strong interest in AI, data mining, machine learning, data science
  • Be self-motivated to learn
  • Have strong writing and presentation skills
  • A strong computer science background

To help me to know your background, please send me the following items by email.

  • A resume
  • A copy of your graduate/undergraduate transcript
  • One or two representative papers

Once we have a commitment to each other, I will do my best to help you

  • to build a strong research capability and publication record
  • to proceed with your thesis
  • to prepare for your future career

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