For the full publication list please refer to Google Scholar

S. Chen, N. Kalanat, S. Topp, J. Sadler, Y. Xie, Z. Jiang, and X. Jia. “Meta-Transfer-Learning for Time Series Data with Extreme Events: An Application to Water Temperature Prediction.” The 32nd ACM International Conference on Information and Knowledge Management (CIKM), 2023.

M. Hu, Z. Zhong, X. Zhang, Y. Li, Y. Xie, X. Jia, X. Zhou, and J. Luo. "Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations. " The 23rd IEEE International Conference on Data Mining (ICDM), 2023.

K. Tayal, A. Renganathan, R. Ghosh, X. Jia, and V. Kumar. “Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling.” The 23rd IEEE International Conference on Data Mining (ICDM), 2023.

E. He, Y. Wan, B. H Letcher, J. H Fair, Y. Xie, and X. Jia. “CGS: Coupled Growth and Survival Model with Cohort Fairness.” The 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023.

Z. Li, Y. Xie, and X. Jia. “Confidence-based Spatial Self-Corrective Learning to Expand Height Data in High Latitudes.” The 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023.

S. Chen, Y. Xie, X. Li, X. Liang, X. Jia. “Physics-Guided Meta-Learning Method in Baseflow Prediction over Large Regions.” In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp. 217-225, Best Applied Data Science Paper Award, 2023.

X. Jia, S. Chen, C. Zheng, Y. Xie, Z. Jiang, N. Kalanat. “Physics-guided Graph Diffusion Network for Combining Heterogeneous Simulated Data: An Application in Predicting Stream Water Temperature.” In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp. 361-369, 2023.

E. He, Y. Xie, L. Liu, W. Chen, Z. Jin, and X. Jia. “Physics Guided Neural Networks for Time-aware Fairness: An Application in Crop Yield Prediction”. AAAI Conference on Artificial Intelligence (AAAI), 2023.

Z. Liu, L. Liu, Y Xie, Z. Jin, and X. Jia. “Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability.” AAAI Conference on Artificial Intelligence (AAAI), 2023.

Z. Li, Y. Xie, X. Jia, K. Stuart, C. Delaire, and S. Skakun. “Point-to-Region Co-Learning for Poverty Mapping at High Resolution Using Satellite Imagery.” AAAI Conference on Artificial Intelligence (AAAI), 2023.

Y. Xie, Z. Li, H. Bao, X. Jia, D. Xu, X. Zhou, S. Skakun. “Auto-CM: Unsupervised Deep Learning for Satellite Imagery Composition and Cloud Masking Using Spatio-Temporal Dynamics”. AAAI Conference on Artificial Intelligence (AAAI), 2023.

S. Chen, J. A Zwart, X. Jia. “Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks”. The 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.

R. Ghosh, A. Renganathan, K. Tayal, X. Li, A. Khandelwal, X. Jia, C. Duffy, J. L Nieber, V. Kumar. “Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology”. The 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.

W. He, Z. Jiang, M. Kriby, Y. Xie, X. Jia, D. Yan, Y. Zhou. “Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery”. The 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.

T. Bao*, S. Chen*, T. T Johnson, P. Givi, S. Sammak, and X. Jia. "Physics Guided Neural Networks for Spatio-temporal Super-resolution of Turbulent Flows." In The 38th Conference on Uncertainty in Artificial Intelligence (UAI). 2022.

X. Jia, S. Chen, Y. Xie, H. Yang, A. Appling, S. Oliver, and Z. Jiang. Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature. SIAM International Conference on Data Mining (SDM), 2022.

K. Tayal, X. Jia, R. Ghosh, J. Willard, J. Read, and V. Kumar. Invertibility aware Integration of Static and Time Series Data: An Application to Lake Temperature Modeling. SIAM International Conference on Data Mining (SDM), Best Applied Data Science Paper Award, 2022.

Y. Xie*, E. He*, X. Jia, W. Chen, S. Skakun, H. Bao, Z. Jiang, R. Ghosh, and P. Ravirathinam. Fairness by "Where": A Statistically-Robust and Model-Agnostic Bi-Level Learning Framework. AAAI Conference on Artificial Intelligence (AAAI), 2022. (*equal contribution)

C. Zheng, Y. Wang, X. Jia. Graph-Augmented Cyclic Learning Framework for Similarity Estimation for Medical Clinical Notes. IEEE International Conference on Healthcare Informatics (ICHI), 2022.

J. Willard*, X. Jia*, S. Xu, M. Steinbach, and V. Kumar. Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems. ACM Computing Surveys (CSUR) (*equal contribution).

Xie, W., M. Kimura, K. Takaki, Y. Asada, T. Iida, and X. Jia. "Interpretable Framework of Physics‐guided Neural Network with Attention Mechanism: Simulating Paddy Field Water Temperature Variations." Water Resources Research (WRR), 2022

J. Sadler, A. Appling, J. Read, S. Oliver, X. Jia, X. Jia, J. Zwart, V. Kumar. Multi-task deep learning of daily streamflow and water temperature. Water Resources Research (WRR), 2022.

X. Jia, Y. Xie, S. Li, S. Chen, J. Zwart, J. Sadler, A. Appling, S. Oliver, and J. Read. Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems. IEEE International Conference on Data Mining (ICDM), 2021.

S. Chen, A. Appling, S. Oliver, H. Corson-Dosch, J. Read, J. Sadler, J. Zwart, and X. Jia. Heterogeneous Stream-reservoir Graph Networks with Data Assimilation. IEEE International Conference on Data Mining (ICDM), 2021.

T. Bao, X. Jia, J. Zwart, J. Sadler, A. Appling, S. Oliver, and T. Johnson. Partial Differential Equation Driven Dynamic Graph Networks for Predicting Stream Water Temperature. IEEE International Conference on Data Mining (ICDM), 2021.

Y. Xie*, E. He*, X. Jia, H. Bao, X. Zhou, R. Ghosh, and P. Ravirathinam. A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity. IEEE International Conference on Data Mining (ICDM), 2021 (*equal contribution).

J. Willard, J. S Read, A. P Appling, S. K Oliver, X. Jia, and V. Kumar. Predicting water temperature dynamics of unmonitored lakes with meta transfer learning. Water Resources Research (WRR), 2021.

Y. Xie*, X. Jia*, H. Bao, X. Zhou, J. Yu, R. Ghosh and P. Ravirathinam. Spatial-Net: A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets. The 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 2021 (*equal contribution).

W. Zhong, Q. Suo, A. Gupta, X. Jia, C. Qiao, L. Su. MetaTP: Traffic Prediction with Unevenly-Distributed Road Sensing Data via Fast Adaptation. ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp), 2021.

W. Zhong, Q. Suo, X. Jia, A. Zhang, L. Su. Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values. IEEE International Conference on Distributed Computing Systems (ICDCS), 2021.

X. Jia, J. Willard, A. Karpatne, J. S Read, J. Zwart, M. Steinbach and V. Kumar. Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. ACM Transactions on Data Science, 2021

X. Jia, J. Zwart, J. Sadler, A. Appling, S. Oliver, S. Markstrom, J. Willard, S. Xu, M. Steinbach, and V. Kumar. Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks. SIAM International Conference on Data Mining (SDM), 2021

X. Jia, B. Lin, J. Zwart, J. Sadler, A. Appling, S. Oliver, and J. Read. Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks. SIAM International Conference on Data Mining (SDM), 2021

X. Jia, H. Zhao, Z. Lin, A. Kale, and V. Kumar. Personalized Image Retrieval with Sparse Graph Representation Learning. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020.

H. Yao, X. Jia, V. Kumar, and Z. Li. "Learning with Small Data." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD tutorial), pp. 3539-3540. 2020.

K. Tayal, S. Agrawal, N. Rao, X. Jia, K. Subbian, and V. Kumar. Regularized Graph Convolutional Networks for Short Text Classification. The 28th International Conference on Computational Linguistics (COLING), 2020.

G. Nayak, R. Ghosh, X. Jia, V. Mithal, and V. Kumar. Multi-view Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data. SIAM International Conference on Data Mining, 2020.

X. Jia, A. Khandelwal, J. S Gerber, K. M Carlson, P. C West, L. H Samberg, and V. Kumar. Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations. Remote Sensing, 2020.

X. Jia, M. Wang, A. Khandelwal, A. Karpatne, and V. Kumar. Recurrent generative networks for multi‐resolution satellite data: An application in cropland monitoring. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2628-2634, 2019.

X. Jia*, J. Willard*, A. Karpatne, J. Read, J. Zwart, M. Steinbach, and V. Kumar. Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 558-566). Society for Industrial and Applied Mathematics, 2019. (*Equal contribution)

J. S Read, X. Jia, J. Willard, A. Appling, J. A Zwart, S. K Oliver, A. Karpatne, G. J.A. Hanson, W. Watkins, M. Steinbach, and V. Kumar. Process-guided Deep Learning Predictions of Lake Water Temperature. Water Resources Research (WRR), 2019.

X. Jia, S. Li, H. Zhao, S. Kim, and V. Kumar. Towards robust and discriminative sequential data learning: When and how to perform adversarial training?. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1665-1673), 2019.

X. Jia, A. Khandelwal, D. Mulla, P. G Pardey, and V. Kumar. Bringing Automated, Remote-sensed, Machine Learning Methods to Monitoring Crop Landscapes at Scale. Agricultural Economics, 2019.

X. Jia, X. Li, N. Du, Y. Zhang, V. Gopalakrishnan, G. Xun, and A. Zhang. Tracking Community Consistency in Dynamic Networks: An Influence-based Approach. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.

X. Jia, A. Khandelwal, J. Gerber, K. Carlson, P. West and V. Kumar. Plantation Mapping in Southeast Asia. Frontiers in Big Data, 2019.

X. Jia, S. Li, A. Khandelwal, G. Nayak, A. Karpatne and V. Kumar. Spatial Context-Aware Networks for Mining Temporal Discriminative Period in Land Cover Detection. SIAM International Conference on Data Mining (SDM), pp. 513-521, 2019

X. Jia, G. Nayak, A. Khandelwal, A. Karpatne and V. Kumar. Classifying Heterogeneous Sequential Data by Cyclic Domain Adaptation: An Application in Land Cover Detection. SIAM International Conference on Data Mining (SDM), pp. 540-548, 2019.

X. Jia, A. Khandelwal, G. Nayak, J. Gerber, K. Carlson, P. West, and V. Kumar. Incremental dual-memory lstm in land cover prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 867-876), 2017.

X. Jia, A. Khandelwal, G. Nayak, J. Gerber, K. Carlson, P. West and V. Kumar. Predict Land Covers with Transition Modeling and Incremental Learning. SIAM International Conference on Data Mining (SDM), pp. 171-179, 2017.

G. Xun, X. Jia, V. Gopalakrishnan, and A. Zhang. A survey on context learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(1), pp.38-56, 2016.

G. Xun, X. Jia, and A. Zhang. Detecting Epileptic Seizures with Electroencephalogram via a Context-learning Model. BMC Medical Informatics and Decision Making, no. 2, 70, 2016.

X. Li, X. Jia, H. Li, H. Xiao, J. Gao and A. Zhang. DRN: Bringing Greedy Layer-wise Training into Time Dimension. IEEE International Conference on Data Mining (ICDM), pp. 859-864, 2015.

N. Du, X. Jia, J. Gao, and A. Zhang. Tracking Temporal Community Strength in Dynamic Networks. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11), pp.3125-3137, 2015.

X. Jia, N. Du, J. Gao, and A. Zhang. Analysis on Community Variational Trend in Dynamic Networks. The 23rd ACM International Conference on Information and Knowledge Management (CIKM), pp. 151-160, 2014.