Yanjie Fu
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BYENG 476, Brickyard Engineering 699 S Mill Ave Tempe, AZ 85281
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Mail code: 8809Campus: Tempe
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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.
AI systems, unlike humans, are brittle, not robust, often struggle when faced with novel situations, and highly sensitive to small perturbations, which can lead to catastrophically poor performance. My research aims to develop trusted and safe machine intelligence, by building tools to address learning framework, algorithmic, data, and computing challenges. My perspective is to connect computing issues in representation learning (imperfect data, structure knowledge), self-supervised learning (limitation of labels), interactive learning (weak supervision and uncertain environments), adaptive learning (shift and drifted environment), stream learning (limitation of memory) and more as disruption-robust learning. This connection not only provides a unified understanding, but also paves a principled and innovative way to design trusted and safe systems as a disruption-robust framework. I execute two important steps steps towards this vision. The first step (data representation construct) aims to integrate structure knowledge, self-optimization, explainability to achieve deep robust representation to fight imperfect and complexity data. The second step (learning strategy construct) aims to integrate robust representations with adaptive and interactive learning to fight uncertain and constrained environments.
Ph.D. students graduated from my research group:
- Geospatial Generative AI for Automated Urban Planning and Urban Informatics
- Ph.D. Dissertation Student: Dongjie Wang, an incoming tenure-track assistant professor at the University of Kansas
- Reimagining city configuration: Automated urban planning via adversarial learning (SIGSPatial best paper runner up)Slides
- Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning (AAAI)Slides
- Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation (ACM Trans on SAS): Slides
- Self-optmizing Feature Selection
- Joint Exchange Program Advisee: Meng Xiao, an assistant professor at Chinese Academy of Sciences
- Traceable group-wise self-optimizing feature transformation learning: A dual optimization perspective(TKDD)Slides
- Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection (IEEE ICDM): Slides
- Deep Graph Learning
- Joint Exchange Program Advisee: Ziyue Qiao, an assistant professor at the Great Bay University
- A dual-channel semi-supervised learning framework on graphs via knowledge transfer and meta-learning (ACM TWEB)Slides
- Deep Time Series Learning
- Ph.D. Dissertation Student: Wei Fan (2020-2023), a postdoc at University of Oxford
- DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting. (ICLR22): Slides
- Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting. (AAAI23): Slides
- IN-Flow: Address Distribution Shift in Time Series Forecasting. (NeurIPS submission): Slides
- AI for Smart Education and Learning Science
- Joint Exchange Program Advisee: Lu Jiang, an assistant professor at Dalian Maritime University
- Reinforced explainable knowledge concept recommendation in MOOCs (ACM TIST): Slides
- Reinforcement Learning for Automated Data Science
- Ph.D. Dissertation Student: Kunpeng Liu(2017-2022), a tenure-track assistant professor at Portland State University
- Automated Feature Selection: A Reinforcement Learning Perspective (TKDE): Slides
- Interactive Reinforcement Learning for Feature selection with Decision Tree in the Loop. (TKDE): Slides
- Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy. (ICDM Best Finalist): Slides
- Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning. (KDD): Slides
- Structure Knowledge Guided Spatial-Temporal Graph and Knowledge Graph Representation Learning: Summary
- Ph.D. Dissertation Student: Pengyang Wang (2017-2021), a tenure-track assistant professor at University of Macau
- Mutual Information Based Substructured Representation Learning (IJCAI20): Slides
- Adversarial Substructured Representation Learning (KDD19): Slides
- Peer and Temporal-Aware Representation Learning (KDD'18 Best Student Paper Finalist): Slides
- Collective Embedding with Periodic Spatial-temporal Mobility Graphs (TIST): Slides
- Machine Learning for Human Mobility Modeling: Summary
- Joint Exchange Program Advisee: Pengfei Wang, an associate professor at Chinese Academy of Sciences
- Representing Urban Forms: A Collective Learning Model with Heterogeneous Human Mobility Data(TKDE) : Slides
- Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes(KDD'17) : Slides
- Spotting Trip Purposes from Taxi Trajectories: A General Probabilistic Model (TIST) : Slides
Courses
2024 Summer
| Course Number | Course Title |
|---|---|
| CSE 792 | Research |
2024 Spring
| Course Number | Course Title |
|---|---|
| CSE 792 | Research |
| CSE 790 | Reading and Conference |
| CSE 572 | Data Mining |
2023 Fall
| Course Number | Course Title |
|---|---|
| CSE 792 | Research |
| CSE 580 | Practicum |
| CSE 792 | Research |
| CSE 572 | Data Mining |