معرفی
Jamie Morgenstern is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. She was previously an assistant professor at Georgia Tech and a Warren Center Fellow at University of Pennsylvania.
- Expertise: Ethics & Fairness, Human-Centered AI, Machine Learning
- Education: PhD in Computer Science from Carnegie Mellon University (2015)
Her research examines the social impact of machine learning and ensuring ML models do not exacerbate societal inequalities. She investigates robustness to human-generated training data, fairness in clustering and active learning, and algorithmic equity in recommendation systems.
Recent publications focus on interactive ML systems, fairness constraints, and privacy-preserving methods across conferences like NeurIPS, ICML, and AIES. Key subfields include multimodal learning, membership inference attacks, and data equity.
Scientific Awards:
- NSF Career award for "Strategic and Equity Considerations in ML"
- Simons collaboration project
- Simons Award for Graduate Students in Theoretical Computer Science (2014-2016)
- NSF GFRP fellowship
- Microsoft Research Graduate Women's Scholarship
- Spotlight presentation at NeurIPS 2015
Mentoring: She advises current PhD students Rachel Hong, Jie (Claire) Zhang, and Yuanyuan (Chloe) Yang. Former advisees include Daniel Jiang (MS), Bhuvesh Kumar (PhD), and Angel (Alex) Cabrera (BS).
Grants: Funded by NSF Career award and Simons collaboration projects. Previously supported by Simons, NSF, and Microsoft Research fellowships.
Labs & Collaborations: Collaborates with researchers like Michael Kearns, Aaron Roth, and Avrim Blum. Affiliated with the Allen School's Artificial Intelligence research group.
