I am a Master’s student in Data Science at ETH Zürich and a Research Assistant at the ETH AI Center. My research focuses on LLM evaluation, interpretable capability assessment, and privacy in language agents.
Previously, I studied Computer Science and Mathematics at Sorbonne University. I also worked on time-series forecasting at ESIEE Paris, supervised by Dr. You Jiang.
I work on evaluating language models beyond aggregate benchmark scores, including privacy–utility trade-offs, ranking robustness, and interpretable skill profiles.
A diagnostic benchmark with 7,852 samples across 10 domains for evaluating the trade-off between task utility and user-defined privacy constraints in LLM agents.
We study the robustness of near-tied LLM rankings across five benchmarks using item response theory and differential item functioning, with owner-disjoint evaluation folds and matched-random controls.
SkillEval: Learning Interpretable Ability Profiles of LLMs via Cognitive Diagnosis Models
Combining LLM-based skill discovery with neural cognitive diagnosis to construct interpretable ability profiles for 3,811 LLMs across 9,523 items from five benchmarks.
I contributed translation test cases and reviewed English, Chinese, and French benchmark submissions.
Research Experience
ETH AI Center
2026–present
Research Assistant · Zurich, Switzerland
Developing a multilingual benchmark for legal compliance in LLMs, including reproducible scenario-generation, review, and evaluation pipelines. Collaborating with legal experts and investigating applications for LLM post-training.
ESIEE Paris
2023
Research Assistant · Paris, France
Implemented Transformer-based models and PatchTST for time-series forecasting under Dr. You Jiang, adapting training pipelines and comparing model performance and computational efficiency on synthetic datasets. [code]
Education
ETH Zürich
2025–present
MSc in Data Science
Sorbonne University
2022–2025
BSc in Computer Science / Mathematics
Selected Projects
Aperiodic Tiling Generation
2024
Implemented Penrose, Wang, and Hat tiling algorithms in Python; optimized generation with duplicate detection and spatial pruning.
Q-Learning and SARSA in Grid Worlds
2023
Compared Q-learning and SARSA across 15 custom environments. Built a map editor and environments adopted for master’s-level teaching at UPEC.
A little more about me
I speak Chinese, English, and French. Outside research, I enjoy playing drums and saxophone, cooking, reading, and calligraphy.