PhD, Pennsylvania State University, 2023

BS, Oklahoma State University, 2017

BS, Southwest Jiaotong University, 2017

Soil Mechanics (CE 34500)

Foundation Engineering (CE 59000)

Peer-Reviewed Journal Publications:

• Xiong, J., Pei, T., and Qiu, T. (2024). "A novel framework for spatial-temporal susceptibility prediction of rainfall-induced landslides: a case study in Western Pennsylvania." Remote Sensing. (Accepted)
• Pei, T., Qiu, T. (2024). “Landslide Susceptibility Mapping Using Physics-Guided Machine Learning: An Application in Colorado Front Range.” Acta Geotechnica.
• Pei, T., T. Qiu, and C. Shen. (2024). “Closure to ‘applying knowledge-guided machine learning to slope stability prediction.’” J. Geotech. Geoenviron. Eng., 150 (9).
• Li, Z., Pei, T., Ying, W., Zhang, R., Yoon, J., Dabo, I., Srubar, W., and Radlinska, A. (2024). “Can domain knowledge benefit machine learning for concrete property prediction?” Journal of the American Ceramic Society, 1-21.
• Pei, T., and Qiu, T. (2024). “Machine Learning with Monotonic Constraint for Geotechnical Engineering Applications: An Example of Slope Stability Prediction.” Acta Geotechnica.
• Pei, T., Qiu, T., and Shen, C. (2023). “Applying Physics-Guided Machine Learning to Slope Stability Prediction.” Journal of Geotechnical and Geoenvironmental Engineering, 149(10),04023089.
• Palese, M., Pei, T., Qiu, T., Zarembski, A. M., Shen, C., and Palese, J. W. (2023). “Hazard assessment framework for statistical analysis of cut slopes using track inspection videos and Geospatial Information.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 17(4), 771–786.
• Pei, T., and Qiu, T. (2022). “A Numerical Investigation of Laterally Loaded Steel Fin Pile Foundation in Sand.” International Journal of Geomechanics, 22(7), 04022102.
• Pei, T., and Qiu, T. (2022). “DEM Investigation of Energy Dissipation at Particle Contacts in Granular Soil Under Cyclic Torsional Shear.” International Journal of Geomechanics, 22(4), 04022016.
• Nagendra, S., Kifer, D., Mirus, B., Pei, T., Lawson, K., Manjunatha, S., Li, W., Nguyen, H., Qiu, T., Tran, S. and Shen, C. (2022). “Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, pp. 4349-4370.
• Pei, T., and Yang, X. (2018). “Compaction-Induced Stress in Geosynthetic-Reinforced Granular Base Course – A Discrete Element Model.” Journal of Rock Mechanics and Geotechnical Engineering, 10(4), 669–677.

Conference Publications/Presentations:

• Goldberg, R., Pei, T., Moshary, F., and Tian, Y. (2025). “Advancing Landslide Susceptibility Mapping Across Heterogeneous Regions with Deep Learning-Based Domain Adaptation.” in Proc. of Geotechnical Frontiers 2025 Conference. (Accepted)
• Pei, T., and Luo, N. (2025). “Prediction of Ultimate Bearing Capacity of Shallow Foundations Using Monotonically Constrained Neural Networks.” in Proc. of Geotechnical Frontiers 2025 Conference. (Accepted)
• Pei, T., Goldberg, R., Moshary, F., and Tian, Y. (2024). “Cross-Ecoregion Landslide Susceptibility Prediction Using Model-Agnostic Meta-Learning.” in Proc. AGU Fall Meeting Abstr., vol. 2024, 2024, Art. no. NH33C-2212.
• Pei, T., Maroufi, M., and Tian, Y. (2024). “A Physics-Informed Machine Learning Approach for Predicting Rainfall-Induced Landslides.” in Proc. AGU Fall Meeting Abstr., vol. 2024, 2024, Art. no. NH11C-2234.
• Xiong, J., Pei, T., and Qiu, T. (2024). “Spatiotemporal prediction of rainfall-induced landslides using machine learning techniques.” in Proc. of Geo-Shanghai International Conference 2024. (Best Paper Award)
• Li, Z., Pei, T., Ying, W., Zhang, R., Yoon, J., Dabo, I., Srubar, W., and Radlinska, A. (2024). “Simulation-assisted transfer learning for concrete strength prediction.” in Proc. of 77th RILEM Annual Week and the 1st Interdisciplinary Symposium on Smart & Sustainable Infrastructures (ISSSI 2023).
• Pei, T., Liu, J., Shen, C., and Kifer, D. (2023). “Impact of Cross-Validation Strategies on Machine Learning Models for Landslide Susceptibility Mapping: A Comparative Study.” in Proc. AGU Fall Meeting Abstr., vol. 2023, 2023, Art. no. NH13D-0717.
• Pei, T., and Qiu, T. (2023). “Landslide susceptibility mapping in Colorado Front Range, USA: a comparison of physics-based and data-driven approaches.” Accepted for publication in Proc. of 8th International Conference on Debris Flow Hazard Mitigation.
• Palese, M., Pei, T., Zarembskia, A., Qiu, T., Shen, C., and Palese, J. (2023). “Risk assessment framework for statistical analysis of cut slopes using track inspection videos and satellite imagery.” in Proc. of 2023 Georisk Conference.
• Pei, T., and Qiu, T. (2023). “Landslide susceptibility mapping using machine learning methods: a case study in Colorado Front Range, USA.” in Proc. of 2023 Geo-Congress Conference.
• Xiong, J., Pei, T., and Qiu, T. (2023). “A machine learning-based method with integrated physics knowledge for predicting bearing capacity of pile foundations.” in Proc. of 2023 Geo-Congress Conference.
• Pei, T., Nagendra, S., Banagere Manjunatha, S., He, G., Kifer, D., Qiu, T., and Shen, C. (2021). “Utilizing an interactive AI-empowered web portal for landslide labeling for establishing a landslide database in Washington state, USA.” in Proc. of EGU Gen. Assem. Conf. Abstr., 2021, Art. no. EGU21- 13974.
• Liu, J., Shen, C., Pei, T., Lawson, K., Kifer, D., Nagendra, S., and Manjunatha, B. (2021). “A new rainfall-induced deep learning strategy for landslide susceptibility prediction.” in Proc. of AGU Fall Meeting Abstr., vol. 2021, 2021, Art. no. NH35E-0504.
• Pei, T., Qiu. T., and Laman, J. (2020) “A numerical investigation of laterally loaded steel fin pile foundations.” in Proc. of 2020 Joint-Rail Conference, V001T08A012. New York: ASME.
• Pei, T., Nagendra, Manjunatha, B., S., He, G., Kifer, D., Qiu, T., and Shen, C. (2020). “Cloud-based interactive database management suite integrated with deep learning-based annotation tool for landslide mapping.” in Proc. AGU Fall Meeting Abstr., vol. 2020, 2020, Art. no. NH030-0011.
• Nagendra, S., Manjunatha, B., Shen, C., Kifer, D., Pei, T. (2020). “An efficient deep learning mechanism for cross-region generalization of landslide events.” in Proc. of AGU Fall Meeting Abstr., vol. 2020, 2020, Art. no. NH030-0010.

• Department of Energy (DOE), "Resilience to Growing Extreme Natural Hazards: Developing a Northeast Hazards Workforce." Co-PI
• National Science Foundation (NSF) Award # RISE-2425802, "Unifying Landslide Domain Knowledge and AI to Understand Landslide Causality." PI
• 2024 CUNY Interdisciplinary Research Grant, "Empowering Landslide Prediction and Community Resilience with Physics-Guided Knowledge Transfer." PI
• PSC-CUNY Research Award Program, "Towards Reliable Data Science Applications in Landslide Prediction: Systematic Investigation into Data Dependencies to Create Widespread Community Awareness." PI