Yijun Lin

About Me

I am working with Prof. Yao-Yi Chiang in the Knowledge Computing Lab at UMN CS&E.

I was in the University of Southern California (USC) advised by Prof. Yao-Yi Chiang and Prof. José Luis Ambite.

My research focuses on spatiotemporal prediction and forecasting. I am interested in incorporating prior knowledge about the ''SPACE'' in data-driven methods to understand environments and human behaviors. Here, the ''SPACE'' can be the geographic space on earth or the representation space learned from data.

➪ More information is available in my CV and on our Lab website.

Awards

➪ See the full list of awards in CV

🎉 UMN DSI-ADC Fellowship, 2022-2024

🎉 Student Travel Award, ACM SIGSPATIAL, 2023

🎉 Third-Place, Best Poster Award & Student Travel Award, SIAM International Conference on Data Mining, 2023

🎉 First-place, Map Feature Extraction Challenge, AI for Critical Mineral Assessment Competition, 2022, Duan, W., Li, Z., Lin, F., Lin, Y., Shrotriya, T., Knoblock, C. A., Chiang, Y.-Y.

Publications

➪ See the full list of publications in Google Scholar and CV

Lin, Y. and Chiang, Y.-Y. (2023). Modeling Spatially Varying Physical Dynamics for Spatiotemporal Predictive Learning. In Proceedings of the 31st ACM SIGSPATIAL international conference on advances in geographic information systems (accepted), Hamburg, Germany

Kim, J., Li, Z., Lin, Y., Namgung, M, Jang, L., and Chiang, Y.-Y. (2023). The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps (Demo Paper). In Proceedings of the 31st ACM SIGSPATIAL international conference on advances in geographic information systems (accepted), Hamburg, Germany

Lin, Y. and Chiang, Y.-Y. (2022). A Semi-Supervised Learning Approach for Abnormal Event Prediction on Large Network Operation Time-Series Data. In Proceedings of the 2022 IEEE International Conference on Big Data, pp. 1024-1033, Osaka, Japan

Lin, Y., Chiang, Y.-Y., Franklin, M., Eckel, S. P. and Ambite, J. L. (2020). Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction. In Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 352-361, Sorrento, Italy (9.8% acceptance rate)

Lin, Y., Mago, N., Gao, Y., Li, Y., Chiang, Y.-Y., Shahabi, C. and Ambite, J. L. (2018). Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. In Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 359 – 368, Seattle, WA, USA

Lin, Y., Chiang, Y.-Y., Pan, F., Stripelis, D., Ambite, J. L., Eckel, S. P. and Habre, R. (2017). Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution. In Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, Article No. 25, Redondo Beach, CA, USA

Work Experience

Research Intern, Mentor: John Krumm
Microsoft Corporation, May. 2020 - Aug. 2020

Research Programmer
Spatial Sciences Institute (SSI), USC, Nov. 2017 - Aug. 2018

Professional Services

[Conference Review] : PAKDD 2024, SDM 2023-2024, ACM SIGSPATIAL 2018-2022, ICTAI 2021-2022, W3PHIAI 2021-2024

[Journal Review] : GeoInformatica, Atmospheric Pollution Research, Environmental Technology & Innovation, International Journal of Applied Earth Observation and Geoinformation

[Teaching Assistant] : UMN Data Mining (graduate), UMN Spatial AI (graduate), UMN Database (undergraduate)，USC Data Mining (graduate)