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Annie Ulichney

Statistics PhD Student
UC Berkeley
annie_ulichney (at) berkeley.edu


I am a fourth-year PhD student in Statistics at UC Berkeley advised by Michael I. Jordan and Nika Haghtalab.

My research develops responsible and reliable learning and decision-making algorithms by integrating tools from statistics, computer science, and economics. I ask how limited information and strategic behavior shape tradeoffs between competing objectives, particularly in public policy and healthcare settings.

I am grateful to have my work supported by the NSF Graduate Research Fellowship.

Previously, I earned a Bachelor of Science from Yale University with majors in Applied Mathematics and Mechanical Engineering.

Selected Papers

  1. The Statistical Fairness-Accuracy Frontier
    Alireza Fallah, Michael I. Jordan, Annie Ulichney
    NeurIPS Workshop: Reliable ML from Unreliable Data, 2025
    Foundations of Responsible Computing (FORC), 2026 [highlight track]
    ESIF Economics and AI+ML Meeting, 2026
    PDF

  2. Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
    Annie Ulichney, Amanda Coston
    Conference on Neural Information Processing Systems (NeurIPS), 2026
    PDF

  3. Revisiting mean estimation over ℓp balls: Is the MLE optimal?
    Liviu Aolaritei, Michael I. Jordan, Reese Pathak, Annie Ulichney
    The Annals of Statistics, 2026
    PDF

  4. Fair Allocation in Dynamic Mechanism Design
    Alireza Fallah, Michael I. Jordan, Annie Ulichney
    Conference on Neural Information Processing Systems (NeurIPS), 2024
    ICML Workshop: Humans, Algorithmic Decision-Making and Society, 2024
    PDF

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