Publications

• Ding, L., Hu Y., Denier N., Shi E., Zhang J., Hu Q., Hughes K. D., Kong L., and Jiang B., (2024). Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach, Advances in Neural Information Processing Systems, Accepted.
• Shen, K., Ding, L., Kong, L., & Liu, X. (2024). From physical space to cyberspace: Recessive gender biases in social media mirror the real world. Cities, 152, 105149.
• Shi, E., Ding, L., Kong, L., & Jiang, B. (2024, June). Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 5960-5975).
• Jiang, Y., Chang, X., Liu, Y., Ding, L., Kong, L. and Jiang, B. (2023). Gaussian Differential Privacy on Riemannian Manifolds. Advances in Neural Information Processing Systems, 36, 14665-14684.
• Liu, Y., Hu, Q., Ding, L., & Kong, L. (2023, July). Online Local Differential Private Quantile Inference via Self-normalization. In International Conference on Machine Learning (pp. 21698-21714). PMLR.
• Mosquera, L., El Emam, K., Ding, L., Sharma, V., Zhang, X. H., Kababji, S. E., … & Eurich, D. T. (2023). A method for generating synthetic longitudinal health data. BMC Medical Research Methodology, 23(1), 1-21.
• Ding, L., Yu, D., Xie, J., Guo, W., Hu, S., Liu, M., Kong, L., Dai, H., Bao, Y. and Jiang, B., 2022, June. Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 11, pp. 11864-11872).

• Liu, M., Ding, L., Yu, D., Liu, W., Kong, L., and Jiang B., (2022). Conformalized Fairness via Quantile Regression. Advances in Neural Information Processing Systems, 35, 11561-11572.

• Hu, Y., Tarafdar, M., Al-Ani, J. A., Rets, I., Hu, S., Denier, D., Hughes, K. D., Konnikov, A., & Ding, L. (2022). Gendered STEM workforce in the United Kingdom: The role of gender bias in job advertising. BIAS project evidence submission to the ‘Diversity in STEM’ inquiry, Science and Technology Committee, House of Commons, UK Parliament.

• Konnikov, A., Rets, I., Hughes, K., Alshehabi Al-Ani, J., Denier, N., Ding, L., Hu, S., Hu, Y., Jiang, B., Kong, L. , Tarafdar, M. and Yu, D. (2022). Responsible AI for labour market equality (BIAS), In: How to Manage International Multidisciplinary Research Projects. Edward Elgar, Cheltenham.

• Konnikov, A., Denier, N., Hu, Y., Hughes, K.D., Al-Ani, J.A., Ding, L., Rets, I. and Tarafdar, M., 2022. BIAS Word inventory for work and employment diversity,(in) equality and inclusivity (Version 1.0).

• Hu, S., Al-Ani, J.A., Hughes, K.D., Denier, N., Konnikov, A., Ding, L., Xie, J., Hu, Y., Tarafdar, M., Jiang, B. and Kong, L., 2022. Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation. Frontiers in Big Data.

• Zhang, Z., Lan, Q., Ding, L., Wang, Y., Hassanpour, N. and Greiner, R., 2019. Reducing selection bias in counterfactual reasoning for individual treatment effects estimation. arXiv preprint arXiv:1912.09040.

• Ding, L., Li, Y., Wang, H. and Xu, K., 2020, July. Measurement and Analysis of Cloud User Interest: A Glance From BitTorrent. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1300-1301). IEEE.

On-going papers:

• Ding, L., Jiang, B., Iravani, M. (2022). Evaluation of Machine Learning Methods to Predict Total Soil Carbon: A Case Study of Alberta.