I am a PhD student at the Max-Planck Institute for Intelligent Systems and the ELLIS Institute of Tubingen under the supervision of Maksym Andriushchenko in the AI Safety and Alignment group. I completed my Masters in Artificial Intelligence at Université de Montréal, Mila and CRCHUM, co-advised by Prof. Bang Liu and Dr. Quoc Nguyen as part of the Applied Computational Linguistics Lab.

My current research focuses on understanding and evaluating long-horizon capabilities of AI agents, with particular emphasis on quantifying potential risks associated with increasingly autonomous AI systems. My main focus revolves around forecasting and automated AI R&D, which I believe to be essential benchmarks for assessing AI capabilities. During my Masters, I explored how large language models can be aligned and calibrated by leveraging insights from model interpretability, with a focus on concept-based explanations.

I am broadly interested in:

• Evaluation of AI agent capabilities
• AI Safety and LLM alignment
• LLM Post-training

During my undergraduate and graduate studies, I also led the UdeM AI undergraduate club, organizing networking and conference events. Outside of research, I am a big fan of racket sports.

Selected Publications

QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals
arXiv preprint, 2026
We introduce a benchmark of 1,000 real-world numerical forecasting questions to assess whether LLMs can produce calibrated 90% prediction intervals, revealing systematic overconfidence across frontier models.
[Paper] [Website]

Activation Steering for Conditional Molecular Generation
AI4Mat Workshop @ NeurIPS 2025
We enable conditional molecular generation by directly manipulating internal LLM representations using concept bottleneck models and activation steering.
[Paper]

Multilingual Assessment of Stereotypes in Large Language Models
NAACL 2025
We build and assess multilingual stereotypes across different LLMs.
[Paper]

Calibrating Large Language Models with Concept Activation Vectors for Medical QA
We propose a novel framework for calibrating LLM uncertainty through Concept Activation Vectors, improving safety and calibration in high-stakes medical decision making.

Atypicality-Aware Calibration of LLMs for Medical QA
Findings of EMNLP 2024
We propose a novel method for eliciting LLM confidence in Medical QA by leveraging insights from medical atypical presentations.
[Paper]