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I am a Computer Science PhD candidate at Washington University in St. Louis, advised by Prof. Yevgeniy Vorobeychik. My current research interests are the foundations of AI (learning, reasoning, planning) and the computational aspects of social issues (alignment and mechanism design). I started out as a cognitive science student at the University of Edinburgh, with the passion to figure out how mind works. While this curiosity has never diminished, it just happened that the beauty of maths struck me greatly. I ended up getting a MSc in pure maths at Imperial College London under the guidance of the amazing Paolo Cascini. My master 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. |
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Lifted Relational Probabilistic Inference via Implicit Learning
(α-β) Luise Ge, Brendan Juba, Kris Nilsson, Alison Shao
2026 UAI
[arXiv] -
COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
Ben Rachmut, Luise Ge, William Yeoh , Ning Zhang , Yvegeniy Vorobeychik
2026 ECML
[arXiv] -
COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
Ben Rachmut, Luise Ge, William Yeoh , Ning Zhang , Yvegeniy Vorobeychik
2026 ECML
[arXiv] -
Linear Social Choice with Few Queries: A Moment-Based Approach
Luise Ge, Daniel Halpern, Gregory Kehne, Yvegeniy Vorobeychik
2026 ESIF AI+ML meeting
[arXiv] -
Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs
Luise Ge, Yongyan Zhang, Yvegeniy Vorobeychik
Main Conference (✦ Oral ✦) at ACL 2026
[arXiv] -
Optimized Distortion in Linear Social Choice
Luise Ge, Gregory Kehne, Yvegeniy Vorobeychik
✦ Oral ✦ at AAAI 2026
[arXiv] -
Polynomial-Time Relational Probabilistic Inference in Open Universes
(α-β) Luise Ge, Brendan Juba, Kris Nilsson
IJCAI 2025
[arXiv] [tutorial] -
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] [Shorter slides] [Invited Talk at MIT] -
Axioms for AI Alignment from Human Feedback
(α-β) Luise Ge, Daniel Halpern, Evi Micha, Ariel D. Procaccia, Itai Shapira, Yevgeniy Vorobeychik, Junlin Wu
✦ Spotlight ✦ at NeurIPS 2024
[pdf] -
Learning Linear Utility Functions From Pairwise Comparison Queries
Luise Ge, Brendan Juba, Yevgeniy Vorobeychik
AAMAS 2024 Workshop/ ADT 2024 abstract
[pdf] [arXiv] [slides] -
Fair Allocation: Implementing and Evaluating an Algorithm for Competitive Allocation of Chores
Outstanding undergraduate thesis supervised by Kousha Etessami
University of Edinburgh, 2022
[pdf]