[1] Wu, N. and Mu, L., 2023. Impact of COVID-19 on online grocery shopping discussion and behavior reflected from Google Trends and geotagged tweets. Computational Urban Science 3(1), 7. [link]

[2] Tan, C., Cao, Q., Li, Y., Zhang, J., Yang, X., Zhao, H., Wu, Z., Liu, Z., Yang, H., Wu, N., et al., 2023. On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications. arXiv preprint arXiv:2312.17016. [link]

[3] Wu, N., Cao, Q., Wang, Z., Liu, Z., Qi, Y., Zhang, J., Ni, J., Yao, X., Ma, H., Mu, L. and Ermon, S., 2024. TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning. Advances in Neural Information Processing Systems 37 (NeurIPS 2024). [link]

[4] Cao, Q., Wu, N., Wang, Z., Liu, Z., Qi, Y., Zhang, J., Ni, J., Yao, X., Ma, H., Mu, L. and Ermon, S., 2024. TorchSpatial: A Python Package for Spatial Representation Learning and Geo-Aware Model Development. 3rd ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications. [link]

[5] Dai, H., Wu, N., Dong, Z., Ren, J., Gao, Y. and Zhao, B., 2025. Comparison and evaluation of machine learning models for predicting indoor PM2.5 concentrations on a large spatiotemporal scale. Building Simulation 18(6), 1453–1466. [link]

[6] Wang, Z., Zhang, J., Zhou, Z., Cao, Q., Wu, N., Liu, Z., Mu, L., Song, Y., Xie, Y., Lao, N., et al., 2025. LocDiffusion: Identifying Locations on Earth by Diffusing in the Hilbert Space. arXiv preprint. [link]

[7] Wang, Z., Wu, N., Cao, Q., Xia, J., Liu, Z., Xie, Y., Nambi, A., Ganu, T., Lao, N., Liu, N., et al., 2025. GeoBS: Information-Theoretic Quantification of Geographic Bias in AI Models. arXiv preprint arXiv:2509.23482. [link]

[8] Wang, Z., Liu, Z., Zhang, J., Zhou, Z., Cao, Q., Wu, N., Mu, L., Song, Y., Xie, Y., Lao, N., et al., 2026. LocDiff: Identifying Locations on Earth by Diffusing in the Hilbert Space. Advances in Neural Information Processing Systems 38, 620–647. [link]

[9] Tiwari, B.B., Shin, E., Wu, N., Mu, L., Khan, M.M., Atkins, E., Benjamin, E., et al., 2026. Designated and non-designated trauma centers and trauma patients: a retrospective analysis of non-fatal trauma discharges in Georgia, 2021. Injury Epidemiology. [link]

[10] Tiwari, B.B., Wu, N., Mu, L., Khan, M.M. and Rajbhandari, J., 2026. Right Care, Right Place, Right Time: Optimizing Georgia's Trauma Access Landscape. 2026 Annual Research Meeting. [link]

[11] Mai, G., Lao, N., Zhang, J., Mao, L., Wang, Z., Wu, N., Janowicz, K., Wu, K., Rao, J., et al., 2026. On the Ethics of Generative GeoAI: Explainability, Bias, Hallucination, Accountability, Privacy, and Trust. Geography According to Foundation Models, 215–232. [link]

[12] Mai, G., Liu, Z., Lao, N., Sun, W., Ma, Y., Song, J., Meng, C., Ma, H., Rao, J., Cao, Q., Wu, N., et al. SSIF: Physics-Inspired Implicit Representations for Spatial-Spectral Image Super-Resolution. [link]

[1] Wu, N., Mai, G., Mu, L., Lao, N., 2024. Integrating Spatial Heterogeneity and Temporal Variability to Human Mobility Origin-Destination Flow Prediction with Spatio-Temporal Explicit Deep Gravity Model (STExDGM).

[6] Wu, N., Mu, L., Tiwari, B. B., Thapa, J. R.*, Khan, M., (2024) "Urban-Rural Disparities of Trauma Care Accessibility in Georgia". place at the 10th Annual Health Services Research Day at Emory University, Atlanta, U.S. (The 1st prize of Poster Presentation)

[5] Wu, N., Mu, L., Mai, G., Lao, Ni., (2024) "Spatiotemporal Explicit Deep Gravity Model (STExDGM) for Origin-Destination Flow Generation". AAG, Honolulu, U.S. (The 1st prize of GISS-SG Graduate Student Honors Paper Competition Award)

[4] Wu, N., Mu, L., Mai, G., Lao, Ni., (2023) "Spatial Contextual Effects". AAG, Honolulu, HI8.on GIS Day at UGA, Athens, GA, U.S. (The 1st prize of Graduate student lightning talk competition)

[3] Wu, N., Mu, L., Mai, G. (2023) "Recreational Use of Green Space Patterns during the COVID-19 Outbreak in Georgia, US". AAG, Denver, CO, U.S.

[2] Wu, N., Mu, L. Ni, L., Mai, G. (2022) "A Spatially Explicit Machine Learning Model for Visit Prediction to Georgia Recreational Vehicle Parks". SEDAAG, Atlanta, GA, U.S.

[1] Wu, N., Mu, L. (2022) "Health Consciousness of obesity on Twitter: Sentiment Analysis and Topic Modeling Study". UGA Obesity Initiative Symposium, Athens, GA, U.S.