Violet Chen (vchen3)

Violet Chen

Assistant Professor

School of Business

Babbio Center 646
(201) 216-3521
Website

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 ethics of artificial intelligence. Currently, I work on modeling fairness and equity in optimization, outcome-centric social welfare perspectives of fair machine learning, human-centric aspects of ethical AI with a focus on modeling and inferring moral preferences. Along these directions, I am interested in applications from healthcare, infrastructure systems and supply chain.

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

Jack Howe Fellowship, Stevens Institute of Technology. September 2022- August 2025.

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

Conference Proceeding

  1. Chen, V.; Hooker, J.; Leben, D. (2024). Assessing Group Fairness with Social Welfare Optimization. International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Lecture Notes in Computer Science. (vol. 14742, pp. 208–225). Springer.
    https://link.springer.com/chapter/10.1007/978-3-031-60597-0_14.
  2. Cui, N.; Wang, X.; Wang, H.; Chen, V.; Ning, Y. (2023). Equipping Federated Graph Neural Networks with Structure-aware Group Fairness. IEEE International Conference on Data Mining (ICDM).. IEEE International Conference on Data Mining (ICDM)..
  3. Chen, V.; Williams, J.; Leben, D.; Heidari, H. (2023). Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations. No. AAAI Conference on Artificial Intelligence (5 ed., vol. 37, pp. 5956-5964). Association for the Advancement of Artificial Intelligence.
    https://doi.org/10.1609/aaai.v37i5.25737.
  4. Chen, V.; Hooker, J. (2020). A Just Approach Balancing Rawlsian Leximax Fairness and Utilitarianism. No. AIES 20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 221-227). Association for Computing Machinery.
    https://dl.acm.org/doi/abs/10.1145/3375627.3375844.

Journal Article

  1. Chen, V.; Hooker, J. (2023). A Guide to Formulating Equity and Fairness in an Optimization Model. No. Annals of Operations Research (vol. 326, pp. 581-619). Springer.
    https://link.springer.com/article/10.1007/s10479-023-05264-y.
  2. Chen, V.; Hooker, J. (2022). Combining leximax fairness and efficiency in a mathematical programming model. No. European Journal of Operational Research (1 ed., vol. 299, pp. 235-248). ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S0377221721007281.

Courses

MIS637 - Data Analytics and Machine Learning