Violet Chen (vchen3)

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 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

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.; 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.
  2. 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