Violet Chen
Assistant Professor
School of Business
Education
- PhD (2022) Carnegie Mellon University (Operations Research)
- BS (2017) Georgia Institute of Technology (Applied Mathematics; Business Administration)
Research
My research interests are broadly related to fairness and ethical AI. Currently, I work on equity in allocation, outcome-centric algorithmic fairness, modeling and inferring moral judgment. Along these directions, I am interested in applications from urban mobility, infrastructure systems, supply chain and healthcare.
Institutional Service
- Search committee for Tenure Track Position in Analytics Member
- Search committee for Tenure Track Position in Analytics Member
Professional Service
- INFORMS Education Strategy Committee
Honors and Awards
Best paper award, The 21st International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2024
Professional Societies
- POMS – Production and Operations Management Society Member
- INFORMS – The Institute for Operations Research and the Management Sciences Member
Grants, Contracts and Funds
National Science Foundation CMMI-2309668. Collaborative Research: Advancing Fairness for Emerging Infrastructure Systems with High Operational Dynamics. July 2023-June 2026.
Selected Publications
Maasch, J., Gan, K., Chen, V., Orfanoudaki, A., Akpinar, N. J., Wang, F. (2025, April). Local Causal Discovery for Structural Evidence of Direct Discrimination. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 18, pp. 19349-19357).
Chen, V., Hooker, J.N., Leben, D. (2024). Assessing Group Fairness with Social Welfare Optimization. In: Dilkina, B. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024. Lecture Notes in Computer Science, vol 14742. Springer, Cham.
Chen, V., Hooker, J. N. (2023). A Guide to Formulating Fairness in an Optimization Model. Annals of Operations Research, 326(1), 581-619.
Cui, N., Wang, X., Wang, W. H., Chen, V., Ning, Y. (2023, December). Equipping federated graph neural networks with structure-aware group fairness. In 2023 IEEE International Conference on Data Mining (ICDM) (pp. 980-985). IEEE.
Chen, V., Williams, J., Leben, D., Heidari, H. (2023, June). Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 5, pp. 5956-5964).
Chen, V., Hooker, J. N. (2022). Combining Leximax Fairness and Efficiency in a Mathematical Programming Model. European Journal of Operational Research, 299(1), 235-248.
Chen, V., Hooker, J.N., Leben, D. (2024). Assessing Group Fairness with Social Welfare Optimization. In: Dilkina, B. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024. Lecture Notes in Computer Science, vol 14742. Springer, Cham.
Chen, V., Hooker, J. N. (2023). A Guide to Formulating Fairness in an Optimization Model. Annals of Operations Research, 326(1), 581-619.
Cui, N., Wang, X., Wang, W. H., Chen, V., Ning, Y. (2023, December). Equipping federated graph neural networks with structure-aware group fairness. In 2023 IEEE International Conference on Data Mining (ICDM) (pp. 980-985). IEEE.
Chen, V., Williams, J., Leben, D., Heidari, H. (2023, June). Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 5, pp. 5956-5964).
Chen, V., Hooker, J. N. (2022). Combining Leximax Fairness and Efficiency in a Mathematical Programming Model. European Journal of Operational Research, 299(1), 235-248.
Courses
BT223 - Applied Models and Simulation
MIS637 - Data Analytics and Machine Learning
MIS637 - Data Analytics and Machine Learning