Publications
Hong, J., Wang, Z., & Zhou, J. (2022). Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent. 11-35 . https://doi.org/10.1145/3531146.3533070
Li, Q., Hong, J., Xie, C., Tan, J., Xin, R., Hou, J., Yin, X., Wang, Z., Hendrycks, D., Wang, Z., Li, B., He, B., & Song, D. (2024). LLM-PBE: Assessing Data Privacy in Large Language Models. Proceedings of the VLDB Endowment 17 (11) , 3201-3214 . https://doi.org/10.14778/3681954.3681994
Li, Z., Hong, J., Li, B., & Wang, Z. (2024). Shake to Leak: Amplifying the Generative Privacy Risk through Fine-tuning. https://arxiv.org/abs/2403.09450
Malagavalli, J., & Kockelman, K. (2024). Smartphone-based method for automated speed enforcement.
Malagavalli, J., Wang, T., & Kockelman, K. (2023). Catching Speeders Via Mobile Phones and Machine Learning: An Opportunity to Improve Speed Enforcement. https://www.caee.utexas.edu/prof/kockelman/public_html/TRB24CVforSpeedEnforcement.pdf
Perin, G. J., Chen, R., Chen, X., Hirata, N. S. T., Wang, Z., & Hong, J. (2025). LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning. https://doi.org/10.48550/arXiv.2506.15606
Sanders, A. (2024). Adding Some Bite to Their Bark: Using AI to Transform the Way the Press Covers the Judiciary. Communications Lawyer Winter 2024 . https://www.americanbar.org/groups/communications_law/publications/communications_lawyer/2024-winter/adding-some-bite-their-bark-using-ai-transform-way-press-covers-judiciary/
Sanders, A. K., & Stewart, D. R. (2023). Let's Not Be Dumb: Government Transparency, Public Records Laws, and "Smart City" Technologies. University of Florida Journal of Law & Public Policy 33 (2) , 167-181 . https://web.s.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=1&sid=b0e71da2-1894-426d-aece-49f9137f6009%40redis
Sanders, A. K., Steward, D. "., & Molchanov, S. (2022). Is It Just Dumb Luck? The Challenge of Getting Access to Public Records Related to Smart City Technology. https://utexas.box.com/s/a8rhwc2mfayusojlilu2wma3x33o3a3t
Strover, S. (2025). The Risk of Risk: Empirical Implications of Ethical AI Frameworks.
Strover, S., & Lalwani, S. (2024). Cities, AI and Local Policies: Laboratories of Limitation. https://ssrn.com/abstract=4913636
Strover, S., & Shaffer, G. (2025). Filling the Void: Cities and AI. https://utexas.app.box.com/file/1903637870032
Strover, S., Cao, T., Esteva, M., & Park, S. (2021). Smart Cities and Ethical Policies: The Challenges of Public Cameras and AI. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3898220
Strover, S., Esteva, M., Cao, T., & Park, S. (2021). PUBLIC POLICY MEETS PUBLIC SURVEILLANCE. AoIR Selected Papers of Internet Research . https://doi.org/10.5210/spir.v2021i0.12247
Strover, S., Lalwani, S., & El-Masri, A. (2024). Uneven Eyes: The Impact of Inconsistent Local Surveillance Policies on Public Trust. 1-5 . https://doi.org/10.1145/3686038.3687088
Wang, H., Hong, J., Zhou, J., & Wang, Z. (2023). How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts. Transactions on Machine Learning Research . https://doi.org/https://openreview.net/pdf?id=11pGlecTz2
Wang, Y., Chen, R., Li, B., Cho, D., Deng, Y., Zhang, R., Chen, T., Wang, Z., Grama, A., & Hong, J. (2025). More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment. https://doi.org/10.48550/arXiv.2504.02193