Luise Ge EmailCV

I am a Computer Science PhD candidate at Washington University in St. Louis, coadvised by Prof. Yevgeniy Vorobeychik and Prof. Gregory Kehne. My current research interests cover the foundations of AI (e.g., integrating learning and reasoning, and knowledge discovery) and collective decision making (e.g., AI alignment, and computational social choice).

I started out as a cognitive science student at the University of Edinburgh, with the passion to figure out how the mind works. While this curiosity has never diminished, it just happened that the beauty of maths struck me greatly. I ended up getting an MSc in pure maths at Imperial College London under my amazing advisor Prof. Paolo Cascini. My master's thesis was on algebraic geometry, inspired by a fantastic idea called "the periodic table of shapes". I have turned back to CS due to both its philosophical considerations and societal impact. Outside of research, you may find me reading, writing, watching films and plays, making art, or traveling.

Research

C = conference, W = workshop, T = thesis. (α-β) denotes alphabetical author order.

Publications

• C9
  Linear Social Choice with Few Queries: A Moment-Based Approach

  Luise Ge, Daniel Halpern, Gregory Kehne, Yevgeniy Vorobeychik

  SODA 2027 (previously presented at the 2026 ESIF Economics and AI+ML Meeting)

  [arXiv] [Slides]
• C8
  Query-Driven Learning for Lifted Reasoning

  (α-β) Luise Ge, Brendan Juba

  IJCLR 2026 (International Joint Conference on Learning & Reasoning)

  [Slides]
• C7
  Lifted Relational Probabilistic Inference via Implicit Learning

  (α-β) Luise Ge, Brendan Juba, Kris Nilsson, Alison Shao

  UAI 2026

  [arXiv]
• C6
  COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication

  Ben Rachmut, Luise Ge, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik

  ECML PKDD 2026 ✦ Best Research Paper Nominee ✦

  [arXiv] [Slides]
• C5
  Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs

  Luise Ge, Yongyan Zhang, Yevgeniy Vorobeychik

  ACL 2026 ✦ Outstanding Paper Award ✦

  [arXiv] [Slides]
• C4
  Optimized Distortion in Linear Social Choice

  Luise Ge, Gregory Kehne, Yevgeniy Vorobeychik

  AAAI 2026 ✦ Oral ✦

  [arXiv] [Slides]
• C3
  Polynomial-Time Relational Probabilistic Inference in Open Universes

  (α-β) Luise Ge, Brendan Juba, Kris Nilsson

  IJCAI 2025

  [arXiv]
• C2
  Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks

  Luise Ge, Michael Lanier, Anindya Sarkar, Bengisu Guresti, Yevgeniy Vorobeychik, Chongjie Zhang

  ICML 2025

  [arXiv] [Slides]
• C1
  Axioms for AI Alignment from Human Feedback

  (α-β) Luise Ge, Daniel Halpern, Evi Micha, Ariel D. Procaccia, Itai Shapira, Yevgeniy Vorobeychik, Junlin Wu

  NeurIPS 2024 ✦ Spotlight ✦

  [PDF]
• W1
  Learning Linear Utility Functions From Pairwise Comparison Queries

  Luise Ge, Brendan Juba, Yevgeniy Vorobeychik

  AAMAS 2024 Workshop / ADT 2024 (Abstract)

  [arXiv] [Slides]
• T1
  Fair Allocation: Implementing and Evaluating an Algorithm for Competitive Allocation of Chores

  Luise Ge, supervised by Kousha Etessami

  Undergraduate Thesis, University of Edinburgh, 2022 ✦ Outstanding Thesis ✦

  [PDF]

Ongoing Projects

• Pluralistic Alignment: A Survey Bridging Theory and Practice

  with Elinor Pool-Dayan

Misc