Scholar

Daniel RACOCEANU

Google Scholar ID: 2eBRLj0AAAAJ

Sorbonne University, Paris Brain Institute (ICM), Inria team "Aramis"

Biomedical Image ComputingExplainable AI

Citations & Impact

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Citations

6,502

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Publications

Co-authors

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Publications

20 items

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Resume (English only)

Research Experience

Professor at Sorbonne University
Principal Investigator at Paris Brain Institute, leading the Aramis INRIA team
Research areas include: effective detection, segmentation, and feature analysis in high-content biomedical images; dynamic tracking in video-microscopy; efficient 3D registration and reconstruction in computational histopathology
Studying Alzheimer's Disease patient stratification through tau-pathology analysis in whole slide images
Developing a responsible AI framework for modeling Parkinson's disease progression
Applying explainable AI and mathematical modeling to study metabolic plasticity and heterogeneity in melanoma using whole slide and Hyperion images

Background

Professor at Sorbonne University specializing in Biomedical Image Analysis, Pattern Recognition, and Data Analytics
With a strong focus on Machine and Deep Learning
Principal Investigator at the Paris Brain Institute, leading the INRIA 'Aramis Lab' team
Research centers on microscopic biomedical image analysis, pattern recognition, and computational integrative pathology
Passionate about developing explainable AI mechanisms to support medical and biomedical experts

Co-authors

14 total