Portrait of Cédric Caruzzo

Cédric Caruzzo

AI Research Scientist at Lunit

Reliable foundation models under distribution shift

Representation learning · Post-training · Evidence-grounded biomedical AI

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Research Profile

I study how foundation models behave when their data, tasks, and evidence change. My research spans representation learning, post-training, retrieval, and evaluation, with a focus on reliable biomedical AI.

At Lunit, I work on self-supervised vision models for whole-slide pathology and post-training and evaluation of large medical language models. My first-author paper on deceptive grounding, accepted at NeurIPS 2026, studies how clinical RAG can cite real evidence while assigning it to the wrong medical entity.

At KAIST, I developed CellPainTR, a Transformer for cross-dataset Cell Painting analysis that generalizes to unseen datasets without target-dataset fine-tuning. Earlier, at Institut Pasteur, I built machine-learning systems for phenotypic drug discovery and infectious-disease research.

I am particularly interested in adaptive and multimodal learning, reliable post-training, and medical AI systems whose evidence and intermediate reasoning can be independently verified.

Publications and Research Outputs

NeurIPS 2026
Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation
Cédric Caruzzo, Donggeun Yoo, Tae Soo Kim
Advances in Neural Information Processing Systems (NeurIPS), 2026

Studies how clinical RAG assigns real evidence to the wrong drug, measures the failure in a 740-pair deployment sample, and evaluates a claim-level entity-attribution check.

Preprint PDF Project
arXiv 2025
CellPainTR: Generalizable Representation Learning for Cross-Dataset Cell Painting Analysis
Cédric Caruzzo, Jong Chul Ye
arXiv preprint arXiv:2509.06986, 2025

Learns batch-robust cellular representations across heterogeneous studies and generalizes to an unseen dataset without target-dataset fine-tuning.

PDF Code Project
SLAS Europe 2024 Poster
Cellular Phenotypic Profiling: Combining Live Imaging and Cell Painting Techniques
Soonju Park, Cédric Caruzzo, Nakyung Lee,
Poster presented at SLAS Europe 2024, Barcelona, Spain

Combines longitudinal live-cell imaging with endpoint Cell Painting for richer phenotypic profiling in high-content drug discovery.

Poster
Scientific Reports 2021
Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
Antsa Rakotonirina, Cédric Caruzzo, Valentine Ballan,
Scientific Reports 11, 21355, 2021

Uses machine learning with MALDI-TOF mass spectrometry to detect Wolbachia infection in Aedes aegypti mosquitoes for vector-control research.

Paper

Selected Experience

Lunit, Seoul
Mar 2025 - Present
AI Research Scientist
  • Own post-training and evaluation pipelines for large medical language models, including benchmark design, retrieval, and knowledge-grounded reasoning.
  • Develop self-supervised Vision Transformers for whole-slide pathology and distributed inference systems for clinical-scale image collections.
  • Lead research on evaluation blind spots in medical RAG, including the first-author Deceptive Grounding study.
KAIST, Kim Jaechul Graduate School of AI
Feb 2023 - Feb 2025
M.S. Researcher, advisor: Prof. Jong Chul Ye

Full research experience

Selected Research

Deceptive Grounding failure structure
NeurIPS 2026 · First author

Deceptive Grounding

Clinical RAG can cite a real source while assigning its evidence to the wrong drug. We measured the failure in one deployment and tested an explicit entity-attribution check.

Clinical RAG Entity attribution Evaluation blind spot
Preprint Project page
CellPainTR cross-dataset representation-learning concept
First-author preprint and code · 2025

CellPainTR

A Transformer for Cell Painting representations that preserves biological signal across batch and feature shifts, including generalization to an entirely unseen dataset without fine-tuning.

Cross-dataset OOD No target fine-tuning Open source
Paper Code Project page

Selected Technical Writing

Technical article · May 2026

From REINFORCE to Dr.GRPO

A ground-up derivation from REINFORCE through Actor-Critic, PPO, RLHF, GRPO, and Dr.GRPO, with each method motivated by one concrete failure.

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All research and writing