Anas Buhayh

Hi, I'm Anas, a PhD student in Information Science at the University of Colorado Boulder. My research focuses on mechanism design and multiobjective optimization in recommender systems, investigating how algorithms balance competing goals such as accuracy, fairness, diversity, and stakeholder welfare. I am advised by Robin Burke at That Recommender Systems Lab.

I study how recommender systems can be designed to optimize across multiple objectives — not only engagement or accuracy, but also social and ethical outcomes that affect users, providers, and communities. My work combines algorithmic modeling, simulation, and participatory methods to analyze how trade-offs emerge and how stakeholders can meaningfully shape optimization processes.

I’m interested in building recommender systems that adapt to plural values — systems that don’t fix a single goal, but navigate the space between fairness, visibility, and utility through transparent, multiobjective frameworks.

Teaching

  • INFO 4604: Applied Machine Learning, Summer 2025
  • INFO 2201: Programming for Information Science, Summer 2023 & 2024

Projects

  • SMORES: A simulation framework to evaluate algorithm stores in multi-stakeholder recommender ecosystems.
  • SCRUF: Modeling fairness in recommendation as allocation & aggregation via social choice mechanisms.
  • CORGI: A governance-oriented recommendation systems architecture.

Publications