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me.HEIC

Ashia C. Wilson

I am a Lister Brothers Career Development Assistant Professor at MIT. Here is a list of some information about me (CV, Publications, Contact).  Short Bio: I obtained my B.A. from Harvard with a concentration in applied mathematics and a minor in philosophy, and my Ph.D.  from UC Berkeley in statistics. Before joining MIT, I held a postdoctoral position in the machine learning and statistics group at Microsoft Research.  ​​

Affiliations 

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Teaching

  • 6.401 - Introduction to Data Science and Statistics (Spring 2021)

  • 6.036 - Introduction to Machine Learning (Spring 2022, Spring 2023)

  • 6.3950 - AI, Decision Making and Society (Fall 2022, Fall 2023, Fall 2024 (syllabus))

  • 6.S964: Topics in Data Science for Society (Spring 2025 (syllabus))

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Recent Publications & Working Papers

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Latest publications

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  • Adaptive Backtracking Line Search

        Joint with Laurent Lessard and Joao Cavalcanti. 2024.

        International Conference on Learning Representations (ICLR). 2025

        [arXiv]

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  • ​High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm

        Joint with Vishwak Srinivasan and Andre Wibisono.​

       International Conference on Algorithmic Learning Theory (ALT). 2025.

​        [arXiv] ​

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  • Algorithmic Pluralism: A Structural Approach To Equal Opportunity

        Joint with Shomik Jain, Vinith Suriyakumar, and Kathleen Creel

       ACM Conference on Fairness, Accountability and Transparency  (FAccT, best paper). 2024.

       ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO, non-archival). 2023. 

​       [arXiv] ​

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  • Allocation Multiplicity: Evaluating the Promises of the Rashomon Set​​

        Joint with Shomik Jain, Margaret Wang, and Kathleen Creel

        ACM Conference on Fairness, Accountability and Transparency  (FAccT). 2025. 

       [arXiv]​​​

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  • Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized

        Joint with Shomik Jain and Kathleen Creel​ 

        International Conference on Machine Learning (ICML). 2024.

        [arXiv]​​​

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Latest preprints

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  • The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches

​        Joint with Omri Lev, Vishwak Srinivasan, Moshe Shenfeld, Katrina Ligett, and Ayush Sekhari​. 2025.

       [arXiv]

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  • Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models

        Joint with Vinith Suriyakumar, Rohan Alur, Ayush Sekhari and Manish Raghavan. 2024.

        ICLR 2025 Workshop on Navigating and Addressing Data Problems for Foundation Models

        [arXiv]

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