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me.HEIC

Ashia C. Wilson

I am a Lister Brothers Career Development Assistant Professor at MIT. My research focuses on designing scalable, reliable and socially responsible AI systems using tools from dynamical systems theory, statistics, and optimizationHere 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 

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)

 

Recent Publications & Working Papers

  • As an AI Language Model, "Yes I Would Recommend Calling the Police": Norm Inconsistency in LLM Decision-Making

        Joint with Shomik Jain and D. Calacci.

        [arXiv]

  • 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]

  • Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin Algorithm

        Joint with Vishwak Srinivasan and Andre Wibisono.

        Conference on Learning Theory (COLT).  2024

        [arXiv]

  • Mean-field underdamped Langevin dynamics and its spacetime discretization

        Joint with Qiang Fu. 

        International Conference on Machine Learning (ICML). 2024.

        [arXiv]

 

  • Algorithmic Pluralism: A Structural Approach To Equal Opportunity

        Joint with Shomik Jain, Vinith Suriyakumar, and Kathleen Creel

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

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

        [arXiv] 

 

  • Algorithms that Approximate Data Removal: New Results and Limitations   

        Joint with Vinith Suriyakumar

        Advances in Neural Information Processing Systems (NeurIPS). 2022.

        [arXiv, code] 

  • Accelerated Stochastic Optimization Methods under Quasar-convexity 

        Joint with Qiang Fu and Dongchu Xu​

        International Conference on Machine Learning (ICML). 2023.

        [arXiv, code

  • Sufficient conditions for non-asymptotic convergence of Riemannian optimisation methods

        Joint with Vishwak Srinivasan

        OPTML Workshop, Neurips (Spotlight). 2022.

        [arXiv

 

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