About Me

Contents

Background

I am a doctoral researcher in computer science specializing in mechanism design, artificial intelligence, and computational social choice. Originally from Milton, Massachusetts, I completed my undergraduate studies at Harvard University where I was advised by Professor Ariel Procaccia and Dr. Jamie Tucker-Foltz. Currently, I am pursuing doctoral research at the University of Oxford under the supervision of Professor Edith Elkind.

My research interests lie at the intersection of theoretical computer science and its societal applications, with particular focus on mechanism design, differential privacy, and algorithmic fairness. I actively seek collaborative research opportunities and welcome recommendations for potential research directions.

Research Interests

  • Computational Social Choice - Developing algorithms and mechanisms for fair collective decision making.
  • Differential Privacy - Exploring the mathematical foundations and applications of privacy-preserving data analysis.
  • Algorithmic Fairness - Investigating methods to ensure equitable outcomes from algorithmic systems.
  • Mechanism Design - Creating incentive-compatible systems for multi-agent environments.
  • Mechanistic Interpretability - Researching approaches to understand the internal mechanisms of neural networks and other complex AI systems.
  • Cooperative AI - Exploring the foundations of cooperation between autonomous agents and among AI systems and humans.
  • Societal Resilience - Investigating how computational and AI systems can enhance resilience to societal challenges.

Current Work

Participatory Budgeting

Developing computational social choice methods for equitable distribution of municipal budgets across community projects. This work involves algorithmic approaches to preference aggregation with constraints.

Differential Privacy

Examining applications of differential privacy in artificial intelligence, data valuation, and mechanism design frameworks. I'm particularly interested in the privacy-utility tradeoff in machine learning contexts.

Fair Division

Researching fair division mechanisms under practical constraints including limited information, resource constraints, and computational complexity. Recently focusing on optimal algorithms for school assignment problems.

Education

University of Oxford

2023 — Present

Ph.D. in Computer Science, Rhodes Scholar, supervised by Professor Edith Elkind

Harvard University

2018 — 2023

Bachelor of Arts in Computer Science, Phi Beta Kappa, Summa Cum Laude

Phillips Exeter Academy

2014 — 2018

High School

Research

Contents

Working Papers

Obvious Independence of Clones

Exploring strategy-proofness properties in voting mechanisms with a focus on independence of clones. This paper investigates the theoretical foundations of voting rules that maintain their properties when similar candidates (clones) are added or removed from the ballot.

Read preprint →

Augmenting Fairness With Welfare: A Framework for Algorithmic Justice

Integrating mechanism design principles with algorithmic fairness metrics to develop comprehensive frameworks for algorithmic justice. We propose a novel approach that balances individual fairness with global welfare considerations.

Read preprint →

Publications

    • School Redistricting: Wiping Unfairness Off the Map

    Robinson, I., Procaccia, A.D., and Tucker-Foltz, J.

    Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2024.

    This paper addresses the challenge of fairly redistricting school catchment areas to balance demographic representation and minimize travel distances. We develop algorithms with theoretical guarantees for this computationally complex problem and demonstrate their application on real-world data.

    Read paper →

    Augmenting Fairness With Welfare: A Framework for Algorithmic Justice

    Robinson, I., Casacubuerta Puig, S., and Wagaman, C.

    European Workshop on Algorithmic Fairness (EWAF), 2023.

    We present a novel framework that combines notions of fairness with welfare considerations to provide a more comprehensive approach to algorithmic justice. Our work bridges the gap between individual fairness metrics and socially optimal outcomes.

    Read paper →

    Obvious Independence of Clones

    Berker, R.E., Ong, C., Casacubuerta Puig, S., and Robinson, I.

    arXiv preprint arXiv:2210.04880, 2022.

    This manuscript explores the property of obvious strategy-proofness in the context of voting rules that satisfy independence of clones. We provide theoretical results characterizing voting rules that exhibit both properties and discuss implications for practical voting system design.

    Read paper →

    Contact

    Get in Touch

    I welcome research collaborations, speaking opportunities, and general inquiries. Please feel free to reach out through any of the following methods:

    Email

    isaac.robinson@univ.ox.ac.uk (Academic)

    robinsonaisaac@gmail.com (Personal)

    Location

    Department of Computer Science

    University of Oxford

    Oxford, Oxfordshire, United Kingdom

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