profile

Yiqun Xie

Assistant Professor
Geospatial Information Science
University of Maryland, College Park

Ph.D in Computer Science
University of Minnesota, Twin Cities

Email: xie@umd.edu

CV: Xie_CV.pdf
[Conference Paper*], [Journal Paper], [Workshop Paper], [Book Chapter]
  • By both selectivity and impact, premier computing conferences are preferred than journals (National Academies Press). For example, most revolutionizing deep learning frameworks (e.g., Transformer, ResNet, GAN, U-Net) are published in conferences.
  • Statement by Computing Research Association (CRA) on the publication culture ("For experimentalists conference publication is preferred to journal publication, and the premier conferences are generally more selective than the premier journals ...").

2024

  1. [AAAI'24] Zhihao Wang, Yiqun Xie, Zhili Li, Xiaowei Jia, Zhe Jiang, Aolin Jia and Shuo Xu. SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models. The 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada (acceptance rate: ~24%) (link)
  2. [AAAI'24] Weiye Chen, Yiqun Xie, Xiaowei Jia, Erhu He, Han Bao, Bang An and Xun Zhou. Referee-Meta-Learning for Fast Adaptation of Locational Fairness. The 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada (acceptance rate: ~24%)
  3. [AAAI'24] Erhu He, Yiqun Xie, Alexander Sun, Jacob Zwart, Jie Yang, Zhenong Jin, Yang Wang, Hassan Karimi and Xiaowei Jia. Fair Graph Learning Using Constraint-aware Priority Adjustment and Graph Masking in River Networks. The 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada (acceptance rate: ~24%)
  4. [AAAI'24] Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang, Zhe Jiang, Shigang Chen, Yiqun Xie, Xiaowei Jia, Da Yan and Yang Zhou. Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery. The 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada (acceptance rate: ~24%)
  5. [KDD'24] Xinbo Zhao, Yingxue Zhang, Xin Zhang, Yu Yang, Yiqun Xie, Yanhua Li and Jun Luo. Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing. The 30th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24), Barcelona, Spain (acceptance rate: ~20%)
  6. [SDM'24] Mingzhi Hu, Xin Zhang, Yanhua Li, Yiqun Xie, Xiaowei Jia, Xun Zhou and Jun Luo. Only Attending What Matter within Trajectories -- Memory-Efficient Trajectory Attention. SIAM International Conference on Data Mining (SDM'24), 2024. (acceptance rate: ~29%)
  7. [SDM'24] Erhu He, Yiqun Xie, Licheng Liu, Zhenong Jin, Dajun Zhang and Xiaowei Jia. Knowledge Guided Machine Learning for Extracting, Preserving, and Adapting Physics-aware Features. SIAM International Conference on Data Mining (SDM'24), 2024. (acceptance rate: ~29%)
  8. [SDM'24] Nasrin Kalanat, Yiqun Xie, Yanhua Li and Xiaowei Jia. Spatial-Temporal Augmented Adaptation via Cycle-Consistent Adversarial Network: An Application in Streamflow Prediction. SIAM International Conference on Data Mining (SDM'24), 2024. (acceptance rate: ~29%)
  9. [TKDE] Erhu He*, Yiqun Xie*, Weiye Chen*, Sergii Skakun, Han Bao, Rahul Ghosh, Praveen Ravirathinam and Xiaowei Jia. Learning with Location-based Fairness: A Statistically-Robust Framework and Acceleration. IEEE Transactions on Knowledge and Data Engineering (TKDE). 2024. (link)
  10. [Computers & Electronics in Ag] Yuhao Wang, Kuishuang Feng, Laixiang Sun, Yiqun Xie and Xiao-Peng Song. Satellite-based Soybean Yield Prediction in Argentina: a comparison between Panel Regression and Deep Learning Methods. Computers and Electronics in Agriculture. 2024. (link)
  11. [GeoAI Handbook] Yiqun Xie, Xiaowei Jia, Weiye Chen, Erhu He. Heterogeneity-Aware Deep Learning in Space: Performance and Fairness. In: Gao, S., Hu, Y., & Li, W. (Eds.) Handbook of Geospatial Artificial Intelligence (1st ed.). CRC Press. (link)
  12. [Preprint] Xie, Y., Wang, Z., Chen, W., Li, Z., Jia, X., Li, Y., Wang, R., Chai, K., Li, R. and Skakun, S., 2024. When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery. arXiv preprint arXiv:2404.11797. (link)

2023

  1. [SIGSPATIAL'23] Zhihao Wang, Yiqun Xie, Xiaowei Jia, Lei Ma and George Hurtt. High-Fidelity Deep Approximation of Ecosystem Simulation over Long-Term at Large Scale. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL'23). Hamburg, Germany. 2023. (acceptance rate: 20.1%) (link)
  2. [SIGSPATIAL'23] Yiqun Xie, Zhaonan Wang, Gengchen Mai, Yanhua Li, Xiaowei Jia, Song Gao and Shaowen Wang. Geo-Foundation Models: Reality, Gaps and Opportunities (Vision Paper). ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL'23). Hamburg, Germany. 2023. (link)
  3. [ICDM'23] Mingzhi Hu, Zhuoyun Zhong, Xin Zhang, Yanhua Li, Yiqun Xie, Xiaowei Jia, Xun Zhou, and Jun Luo. Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations. Accepted by: IEEE International Conference on Data Mining. 2023. (acceptance rate: 9.37%) (link)
  4. [CIKM'23] Shengyu Chen, Nasrin Kalanat, Simon Topp, Jeffrey Sadler, Yiqun Xie, Zhe Jiang and Xiaowei Jia. Meta-Transfer-Learning for Time Series Data with Extreme Events: An Application to Water Temperature Prediction. 32nd ACM International Conference on Information and Knowledge Management. Birmingham, UK. 2023. (acceptance rate: 24%) (link)
  5. [ICCV'23] Dongyao Zhu, Bowen Lei, Jie Zhang, Yanbo Fang, Yiqun Xie, Ruqi Zhang and Dongkuan Xu. Rethinking Data Distillation: Do Not Overlook Calibration. Accepted by: International Conference on Computer Vision (ICCV'23). Paris, 2023. (link)
  6. [IJCAI'23] Zhili Li, Yiqun Xie and Xiaowei Jia. Confidence-based Self-Corrective Learning: An Application in Height Estimation Using Satellite LiDAR and Imagery. International Joint Conference on Artificial Intelligence (IJCAI'23). Macao, 2023. (acceptance rate: ~20%) (link)
  7. [IJCAI'23] Erhu He, Yue Wan, Ben Letcher, Jenn Fair, Yiqun Xie and Xiaowei Jia. CGS: Coupled Growth and Survival Model with Cohort Fairness. International Joint Conference on Artificial Intelligence (IJCAI'23). Macao, 2023. (acceptance rate: ~20%) (link)
  8. [AAAI'23] Yiqun Xie*, Zhili Li*, Han Bao, Xiaowei Jia, Dongkuan Xu, Xun Zhou and Sergii Skakun. Auto-CM: Unsupervised Deep Learning for Satellite Imagery Composition and Cloud Masking Using Spatio-Temporal Dynamics. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23). Washington D.C., 2023. (acceptance rate: 19.6%) (link)
  9. [AAAI'23] Zhili Li, Yiqun Xie, Xiaowei Jia, Kara Stuart, Caroline Delaire and Sergii Skakun. Point-to-Region Co-Learning for Poverty Mapping at High Resolution Using Satellite Imagery. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23). Washington D.C., 2023. (acceptance rate: 19.6%) (link)
  10. [AAAI'23] Erhu He*, Yiqun Xie*, Licheng Liu, Weiye Chen, Zhenong Jin and Xiaowei Jia. Physics Guided Neural Networks for Time-aware Fairness: An Application in Crop Yield Prediction. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23). Washington D.C., 2023. (acceptance rate: 19.6%) (link)
  11. [AAAI'23] Zhexiong Liu, Licheng Liu, Yiqun Xie, Zhenong Jin and Xiaowei Jia. Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23). Washington D.C., 2023. (acceptance rate: 19.6%) (link)
  12. [SDM'23] Shengyu Chen, Yiqun Xie, Xiang Li, Xu Liang and Xiaowei Jia. Physics-Guided Meta-Learning Method in Baseflow Prediction over Large Regions. SIAM International Conference on Data Mining (SDM'23), 2023. (acceptance rate: 27.4%) (link)
    Best Application Paper Award
  13. [SDM'23] Xiaowei Jia, Shengyu Chen, Can Zheng, Yiqun Xie, Zhe Jiang, Nasrin Kalanat. Physics-guided Graph Diffusion Network for Combining Heterogeneous Simulated Data: An Application in Predicting Stream Water Temperature. Accepted by: SIAM International Conference on Data Mining (SDM'23), 2023. (acceptance rate: 27.4%)
  14. [SDM'23] Yan Li, Mingzhou Yang, Matthew Eagon, Majid Farhadloo, Yiqun Xie, William Northrop and Shashi Shekhar. Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation. Accepted by: SIAM International Conference on Data Mining (SDM'23), 2023. (acceptance rate: 27.4%)
  15. [SDM'23] Zhe Jiang, Yupu Zhang, Saugat Adhikari, Da Yan, Arpan Man Sainju, Xiaowei Jia and Yiqun Xie. Hidden Markov Forest for Terrain-Aware Flood Inundation Mapping on Earth Imagery. Accepted by: SIAM International Conference on Data Mining (SDM'23), 2023. (acceptance rate: 27.4%)
  16. [KAIS'23] Yiqun Xie*, Weiye Chen*, Erhu He*, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh and Praveen Ravirathinam. Harnessing Heterogeneity in Space with Statistically-Guided Meta-Learning. Knowledge and Information Systems (KAIS). Springer. (link)
  17. [KAIS'23] Shengyu Chen, Nasrin Kalanat, Yiqun Xie, Sheng Li, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Jordan Read and Xiaowei Jia. Physics-Guided Machine Learning from Simulated Data with Different Physical Parameters. Knowledge and Information Systems (KAIS). Springer.(link)
  18. [KAIS'23] Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li and Xiaowei Jia. STORM-GAN+: Spatio-Temporal Meta-GAN for Cross-City Estimation of Heterogeneous Human Mobility Responses to COVID-19. Accepted by: Knowledge and Information Systems (KAIS). Springer.
  19. [ML for Data Science Handbook] Yan Li, Yiqun Xie and Shashi Shekhar (2023). Spatial Data Science. In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. (link)

2022

  1. [AAAI'22] Yiqun Xie*, Erhu He*, Xiaowei Jia, Weiye Chen, Han Bao, Sergii Skakun, Zhe Jiang, Rahul Ghosh and Praveen Ravirathinam. Fairness by "Where": A Statistically-Robust and Model-Agnostic Bi-Level Learning Framework. In: The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22), 2022. (acceptance rate: 15%) (link, pdf, code)
  2. [IJCAI'22, Invited] Yiqun Xie*, Erhu He*, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh and Praveen Ravirathinam. Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract). International Joint Conference on Artificial Intelligence (IJCAI'22), Sister Conference Best Paper Track. 2022. (link, code)
  3. [ICDM'22] Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li and Xiaowei Jia. STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19. Accepted by: IEEE International Conference on Data Mining (ICDM'22), 2022. (acceptance rate: 9.77%)
  4. [SIGSPATIAL'22] Erhu He*, Yiqun Xie*, Xiaowei Jia, Weiye Chen, Han Bao, Xun Zhou, Zhe Jiang, Rahul Ghosh and Praveen Ravirathinam. Sailing in the Location-Based Fairness-Bias Sphere. ACM SIGSPATIAL 2022. (acceptance rate: 23.8%) (link)
  5. [SIGSPATIAL'22] Weiye Chen, Zhihao Wang, Zhili Li, Yiqun Xie, Xiaowei Jia, Anlin Li. Deep Semantic Segmentation for Building Detection Using Knowledge-Informed Features from LiDAR Point Clouds. ACM SIGSPATIAL 2022. (Invited as a top-3 SIGSPATIAL Cup solution)
  6. [KDD'22] Wenchong He, Marcus Kriby, Zhe Jiang, Yiqun Xie, Xiaowei Jia, Da Yan and Yang Zhou. Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery. Accepted by: The 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'22), 2022. (acceptance rate: 15%) (link)
  7. [SDM'22] Xiaowei Jia, Shengyu Chen, Yiqun Xie, Haoyu Yang, Alison Appling, Samantha Oliver and Zhe Jiang. Modeling Reservoir Release in Stream Temperature Prediction Using Pseudo-Prospective Learning and Physical Simulations. SIAM International Conference on Data Mining (SDM'22), 2022. (acceptance rate: 27.8%) (link)
  8. [BigData'22] Xiaohu Zhao, Kebin Jia, Benjamin Letcher, Jennifer Fair, Yiqun Xie, and Xiaowei Jia. VIMTS: Variational-based Imputation for Multi-modal Time Series. Accepted by: IEEE International Conference on Big Data (BigData'22), 2022. (acceptance rate: 19.2%)
  9. [CSUR'22] Yiqun Xie, Shashi Shekhar and Yan Li. 2022. Statistically-robust Clustering Techniques for Mapping Spatial Hotspots: A Survey. ACM Computing Surveys (CSUR). (published version; arXiv pre-print)
  10. [TIST'22] Han Bao, Xun Zhou, Yiqun Xie, Yingxue Zhang and Yanhua Li. 2022. COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-Temporal Generative Adversarial Networks with Enhanced Features. ACM Transactions on Intelligent Systems and Technology (TIST). (link)
  11. [BigSpatial'22] Yan Li, Majid Farhadloo, Santhoshi Krishnan, Yiqun Xie, Timothy L Frankel, Shashi Shekhar and Arvind Rao. CSCD: Towards Spatially Resolving the Heterogeneous Landscape of MxIF Oncology Data. In: 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial'22). (Best Paper Award) (link)

2021

  1. [ICDM'21] Yiqun Xie*, Erhu He*, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh and Praveen Ravirathinam. A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity. IEEE International Conference on Data Mining (ICDM'21), 2021. (acceptance rate: 9.9%) (link, pdf, code)
    Best Paper Award
    Invited to IJCAI's Sister Conference Best Paper Track
  2. [ICDM'21] Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver and Jordan Read. Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems. IEEE International Conference on Data Mining (ICDM'21), 2021. (acceptance rate: 9.9%) (link)
  3. [SIGSPATIAL'21] Yiqun Xie*, Xiaowei Jia*, Han Bao, Xun Zhou, Jia Yu, Rahul Ghosh and Praveen Ravirathinam. Spatial-Net: A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets. Accepted by: Proceedings of the ACM SIGSPATIAL International Conference on Advancements in Geographic Information Systems (SIGSPATIAL'21), 2021. (acceptance rate: 22.4%) (link, code)
  4. [TIST'21] Yiqun Xie, Xiaowei Jia, Han Bao, Xun Zhou and Shashi Shekhar. 2021. Significant DBSCAN+: Statistically Robust Density-based Clustering. ACM Transactions on Intelligent Systems and Technology (TIST), 12(5). (link, code)
  5. [TIST'21] Jayant Gupta, Carl Molnar, Yiqun Xie, Joseph Knight and Shashi Shekhar. 2021. Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach. Accepted by: ACM Transactions on Intelligent Systems and Technology (TIST). (coming)
  6. [Remote Sensing'21] Zhihao Wei, Kebin Jia, Pengyu Liu, Xiaowei Jia, Yiqun Xie, and Zhe Jiang. 2021. Large-Scale River Mapping Using Contrastive Learning and Multi-Source Satellite Imagery. Remote Sensing, 13(15): 2893. (link)
  7. [IJGIS'21] Jiannan Cai, Min Deng, Yiwen Guo, Yiqun Xie and Shashi Shekhar. 2021. Discovering regions of anomalous spatial co-locations. International Journal of Geographical Information Science (IJGIS). (link)

2020 and Before

  1. [SIGSPATIAL'20] Yiqun Xie, Han Bao, Yan Li and Shashi Shekhar. Discovering Spatial Mixture Patterns of Interest. In: Proceedings of the ACM SIGSPATIAL International Conference on Advancements in Geographic Information Systems (SIGSPATIAL'20), 2020. (acceptance rate: 22.1%) (link)
  2. [SIGSPATIAL'20] Han Bao, Xun Zhou, Yingxue Zhang, Yanhua Li and Yiqun Xie. COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks. In: Proceedings of the ACM SIGSPATIAL International Conference on Advancements in Geographic Information Systems (SIGSPATIAL'20), 2020. (acceptance rate: 22.1%) (link)
  3. [TDS'20] Yiqun Xie and Shashi Shekhar. 2020. A Unified Framework for Robust and Efficient Hotspot Detection in Smart Cities. ACM Transactions on Data Science (TDS). (link)
  4. [TIST'20] Yiqun Xie, Xun Zhou and Shashi Shekhar. 2020. Discovering Interesting Sub-Paths with Statistical Significance from Spatio-temporal Datasets. ACM Transactions on Intelligent Systems and Technology (TIST), 11(1). (link)
  5. [TDS'20] Yan Li, Pratik Kotwal, Pengyue Wang, Yiqun Xie, Shashi Shekhar and William Northrop. 2020. Physics-guided Energy-efficient Path Selection Using on-board diagnostics Data. ACM Transactions on Data Science. (link)
  6. [CEUS'20] Jiannan Cai, Yiqun Xie, Min Deng and Shashi Shekhar. 2020. Significant spatial co-distribution pattern discovery. Computers, Environment and Urban Systems. (link)
  7. [DeepSpatial'20] Jayant Gupta, Yiqun Xie and Shashi Shekhar. Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results. In: 1st ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (DeepSpatial). Aug. 2020. (Best Paper Award) (link)
  8. [IWCTS'20] Yan Li, Yiqun Xie, Pengyue Wang, Shashi Shekhar adn William Northrop. Significant Lagrangian Linear Hotspot Discovery. In: 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science (IWCTS). Nov. 2020. (link)
  9. [SIGSPATIAL'19] Yiqun Xie, Shashi Shekhar, Richard Feiock and Joseph Knight. Revolutionizing Tree Management via Intelligent Spatial Techniques: A Vision. ACM SIGSPATIAL International Conference on Advancements in Geographic Information Systems (SIGSPATIAL'19), Chicago, IL, Nov. 2019. (acceptance rate: 15% for vision) (link)
    Best Vision Paper Award
  10. [SSTD'19] Yiqun Xie and Shashi Shekhar. Significant DBSCAN towards Statistically Robust Clustering. International Symposium on Spatial and Temporal Databases (SSTD'19), Vienna, Austria, Aug. 2019. (Overall acceptance rate: 32%) (link, code-python, code-matlab)
    Best Paper Award
  11. [SDM'19] Yiqun Xie and Shashi Shekhar. NN-scan: A Nondeterministic Normalization based Scan Statistics towards Robust Hotspot Detection. In: Proceedings of the 2019 SIAM International Conference on Data Mining (SDM'19), Calgary, Canada, May 2019. (acceptance rate: 22.7%) (link)
  12. [IJGIS'19] Yiqun Xie, Jiannan Cai, Rahul Bhojwani, Shashi Shekhar and Joseph Knight. 2019. A Locally Constrained YOLO Framework for Detecting Small and Densely Distributed Building Footprints. International Journal of Geographical Information Science (IJGIS). (link)
  13. [ICDM'18] Yiqun Xie, Han Bao, Shashi Shekhar and Joseph Knight. A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets. In: IEEE International Conference on Data Mining (ICDM'18), Singapore, Nov. 2018. (acceptance rate: 19.94%) (link)
  14. [SIGSPATIAL'18] Yiqun Xie, Rahul Bhojwani, Shashi Shekhar and Joseph Knight. An Unsupervised Augmentation Framework for Deep Learning based Geospatial Object Detection: A Summary of Results. In: Proceedings of the ACM SIGSPATIAL International Conference on Advancements in Geographic Information Systems (SIGSPATIAL'18), Seattle, WA, Nov. 2018. (acceptance rate: 20%) (link)
  15. [ISC2'18] Yiqun Xie, Jayant Gupta, Yan Li and Shashi Shekhar. Transforming Smart Cities with Spatial Computing. In: IEEE International Smart Cities Conference (ISC2 2018), Kansas City, MO, Sep. 2018. (link)
  16. [SSTD'17] Yiqun Xie and Shashi Shekhar. FF-SA: Fragmentation-Free Spatial Allocation. In: International Symposium on Spatial and Temporal Databases (SSTD'17). Arlington, VA, Aug. 2017. (acceptance rate: 32.8%) (link)
  17. [BigDataCongress'17] S. K. Prasad, D. Aghajarian, Mi. McDermott, D. Shah, M. Mokbel, S. Puri, S. J. Rey, S. Shekhar, Y. Xie, R. R. Vatsavai, F. Wang, Y. Liang, H. Vo and S. Wang: Parallel Processing over Spatial-Temporal Datasets from Geo, Bio, Climate and Social Science Communities: A Research Roadmap. In: Proceedings of IEEE BigData Congress. June 25 - June 30, 2017, Honolulu, Hawaii, USA. (link)
  18. [ISPRS IJGI'17] Yiqun Xie, Emre Eftelioglu, Reem Ali, Xun Tang, Yan Li, Ruhi Doshi and Shashi Shekhar. 2017. Transdisciplinary Foundations of Geospatial Data Science. ISPRS International Journal of Geo-Information, 2017, 6(12). (link)
  19. [ISPRS IJGI'17] Yiqun Xie, Bryan Runck, Shashi Shekhar, Len Kne, David Mulla, Nicholas Jordan and Peter Wiringa, 2017. Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation. ISPRS International Journal of Geo-Information, 2017,6(7) (link)
  20. [SIGKDD Explore'17] Naoki Abe, Yiqun Xie, Shashi Shekhar, Chid Apte, Vipin Kumar, Mitch Tuinstra, and Ranga Raju Vatsavai. 2017. Data Science for Food, Energy and Water: A Workshop Report. SIGKDD Explor. Newsl. 18, 2 (March 2017), 1-4. (link)
  21. [AI/OR for Social Good'17] Yiqun Xie, KwangSoo Yang, Shashi Shekhar, Brent Dalzell, David Mulla. Spatially constrained Geodesign optimization (GOP) for improving agricultural watershed sustainability. In: The Thirty-first AAAI Conference on Artificial Intelligence (AAAI-17), Workshop on AI and OR for Social Good. San Francisco, CA, Feb. 2017. (link)
  22. [ICDM'14] Eftelioglu, E., Shekhar, S., Oliver, D., Zhou, X., Evans, M.R., Xie, Y., Kang, J.M., Laubscher, R. and Farah, C., 2014, December. Ring-shaped hotspot detection: a summary of results. In Data Mining (ICDM), 2014 IEEE International Conference on (pp. 815-820). (acceptance rate: 19.5%) (link)
  23. [GRSL'13] Yiqun Xie, Guoan Tang, Shijiang Yan and Hui Lin, 2013. Crater detection using the morphological characteristics of Chang'E-1 digital elevation models. IEEE Geoscience and Remote Sensing Letters, 10(4), pp.885-889. (link)

Presentations/Talks (not listed above)

  1. Yiqun Xie. AI for Spatial Data: Fairness, Adaptation, and Social Good. Value-Centered Artifitial Intelligence (VCAI), University of Maryland. March, 2024.
  2. Yiqun Xie. Fairness over Locations: Formulation and Generalization. Lightning talk at NSF/Amazon Fairness in AI PI meeting. Amazon Headquarter 2, Arlington, VA. January, 2024.
  3. Yiqun Xie. Fast Approximation of Ecosystem Projection with Deep Learning. Invited Innovation Session panel talk at 2023 American Geophysical Union (AGU) Fall Meeting. San Francisco, CA. December, 2023.
  4. Yiqun Xie. AI-powered Ecosystem Simulation. Department Open Research Day, University of Maryland. December, 2023.
  5. Yiqun Xie. Harnessing AI Challenges for Earth Science Problems: From Space to Physics. iHARP: NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions. University of Maryland Baltimore County, October 2023.
  6. Yiqun Xie. AI for Geospatial Problems: Gaps, Risks & Opportunities. National Geospatial Advisory Committee (NGAC) meeting. Washington D.C., US, May 2023.
  7. Yiqun Xie. Opportunities & Risks of GeoAI for Digital Resilience: A Technical Perspective. Invited Panel Talk at the National Academies’ of Science, Engineering and Medicine, Meeting on GeoAI and the Future of Mapping: Implications for 21st-Century Digital Resilience, Washington D.C., US, May 2023.
  8. Yiqun Xie. Heterogeneity-Aware Learning in Space: Performance and Fairness. Keynote Talk at ACM SIGSPATIAL ARIC Workshop, Seattle, WA. Nov. 2022.
  9. Yiqun Xie. Harnessing Distribution Shift: When Machine Learning Meets Spatial Big Data. Keynote Talk at Seoul Big Data Forum, Seoul, South Korea. Nov. 2022.
  10. Yiqun Xie. Heterogeneity-Aware Learning in Space: Performance and Fairness. Seminar Talk at the Department of Computer Science, University of Maryland. Oct. 21, 2022.
  11. Yiqun Xie, Weiye Chen, Xiaowei Jia and Erhu He. Fairness-Aware Machine Learning in Space: Tackling Bias Related to Locations. Social Data Science (SoDa) Center Workshop, University of Maryland. Sep. 21, 2022.
  12. Jia Yu, Yiqun Xie, Kyle Duncan and Sinead Louise Farrell. (2022). ICESpark-EarthCube-2022-Annual-Meeting (ec2022v2). Zenodo. (DOI link)
  13. Yiqun Xie. Deep learning for data with heterogeneity. AI Time. May 18, 2022.
  14. Yiqun Xie. Advancing Deep Learning for Geospatial Data. Departmental Seminar. University of Maryland, College Park. Mar. 31, 2022.
  15. Yiqun Xie, Kara Stuart and Caroline Delaire. Mapping vulnerable populations with novel AI techniques. Google AI for Social Good Mid-Program. Feb. 8, 2022.
  16. Jia Yu, Yiqun Xie, Kyle A. Duncan and Sinead Louise Farrell. Apache Sedona in Action: Analyzing Large-scale Arctic Observations Using an Open-source Big Data Platform. AGU Fall Meeting 2021. New Orleans, LA. Dec. 2021.
  17. Yiqun Xie and Xiaowei Jia. Spatial-Net: A Statistically-Guided and Model-Agnostic Deep Learning Framework for Earth Observation with Spatially Heterogeneous Datasets. UMD/NASA Workshop on AI and Machine Learning in Earth Sciences. University of Maryland. Sep. 2021.
  18. Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver and Jordan Read. Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems. UMD/NASA Workshop on AI and Machine Learning in Earth Sciences. University of Maryland. Sep. 2021.
  19. Yiqun Xie. Spatial Data Science: Challenges and New Techniques. Departmental Seminar. University of Maryland, College Park. Nov. 12, 2020.
  20. Yiqun Xie, Shashi Shekhar, Richard Feiock and Joseph Knight. Intelligent Spatial Technologies for Urban Tree Mapping. USDA-NIFA Next Generation Land-Use Change Methodology Project Workshop 2: Machine learning and data fusion for aerial imagery interpretation of land use change. Virtual via Zoom. June 10-11, 2020.
  21. Yiqun Xie and Shashi Shekhar. Spatial Computing. Inaugural meeting of the Center for Excellence in Remote Sensing (CERS), University of Minnesota, May 16, 2018.
  22. Xie, Y., Shekhar, S., Dalzell, B., Mulla, D.: Fragmentation-Free Land Allocation: A Spatial Optimization Approach. At: Machine Learning: From Farm to Table Workshop, Midwest Big Data Hub, University of Illinois at Urbana-Champaign, Apr. 2017. (link)
  23. Yiqun Xie, Zhe Jiang, Emre Eftelioglu, Shashi Shekhar, Brent Dalzell, David Mulla. Geodesign Optimization towards Improving Agricultural Watershed Management (Poster), ACM SIGKDD Conference on Knowledge and Data Discovery, Workshop on Data Science for Food, Energy and Water, San Francisco, CA, Aug. 2016. (link)
  24. Yiqun Xie. On the road with collaborative Geodesign. in: ESRI Developer Summit, Palm Springs, CA, Mar. 2014.
  25. Yiqun Xie, Guoan Tang, Shijiang Yan and Hui Lin. Digital terrain analysis for detecting impact craters on the lunar surface. In: Geoinfomatics2012, Hong Kong, June, 2012. (link)