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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, Fall 2024 (syllabus))

 

Recent Publications & Working Papers

  • Adaptive Backtracking for Faster Optimization

        Joint with Laurent Lessard and Joao Cavalcanti. 2024.

        [arXiv]

  • Faster Machine Unlearning via Natural Gradient Descent

        Joint with Omri Lev. 2024.

        [arXiv]

        

  • Homogenous Algorithms Can Reduce Competition in Personalized Pricing

        Joint with Nathan Jo, Kathleen Creel, and Manish Raghavan

        ICML Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact. 2024.

  • The Revealed Preferences of Pre-authorized Licenses and Their Ethical Implications for Generative Models

        Joint with Vinith Suriyakumar, Peter Menell and Dylan Hadfield-Menell

        The 2nd Workshop on Generative AI and Law (GenLaw). 2024.

        [link

  • Automating Transparency Mechanisms in the Judicial System Using LLMs: Challenges and Opportunities 

        Joint with Ishana Shastri, Shomik  Jain and Barbara Engelhardt

       Conference on AI, Ethics and Society (AIES). 2024. 

       [arXiv

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

        Joint with Shomik Jain and Dana Calacci 

        Conference on AI, Ethics and Society (AIES). 2024. 

        [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, best paper). 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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