Automated Hot Flash Detection (Elocare/NUS)
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Summary
Led machine learning development for automated hot flash detection using skin conductance data in collaboration with Elocare and NUS, Singapore.
About
PhD Candidate in AI for Biomedical Imaging, specializing in self-supervised learning and generative models for advanced image restoration and analysis. Proven ability to translate cutting-edge research into practical solutions, evidenced by contributions to high-impact publications (Science Advances, Communications Biology) and successful deployment of deep learning models in industry settings. Eager to leverage expertise in machine learning, computer vision, and bioimaging to drive innovation in a research-focused or advanced engineering role.
Work
LadHyX, École Polytechnique & LTCI, Télécom Paris
|PhD Candidate
Paris, Île-de-France, France
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Summary
Leading advanced deep learning research to develop end-to-end ML pipelines for quantifying cellular nuclear deformations in microscopy imaging, enabling in-vitro diagnostics for laminopathies and breast cancer.
Dassault Systèmes
|Research Engineer Intern
Paris, Île-de-France, France
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Summary
Developed and deployed advanced 3D deep learning models for automated tumor segmentation on volumetric CT scans, leveraging multi-GPU clusters for large-scale experimentation in a production environment.
LOB, École Polytechnique
|Research Intern
Paris, Île-de-France, France
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Summary
Developed advanced segmentation algorithms and optimized computational workflows for pSHG and THG microscopy imaging, contributing to cutting-edge research in bioimage analysis.
Education
École Polytechnique
Paris, Île-de-France, France
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PhD
Biomedical Engineering (AI for Biomedical Imaging)
École Polytechnique
Paris, Île-de-France, France
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MSc
Biomedical Engineering
École Polytechnique
Paris, Île-de-France, France
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BSc
Mathematics and Computer Science
Grade: 4.0
Projects
ML Models for Electrical Impedance Spectroscopy in Cellular Monitoring (Sensome)
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Summary
Developed machine learning models for analyzing electrical impedance spectroscopy data to monitor cellular activity in collaboration with Sensome.
Publications
Log focal frequency loss for bioimage restoration
Published by
IEEE International Symposium on Biomedical Imaging (ISBI)
Summary
First-authored research on the development of a novel loss function for enhanced bioimage restoration, presentation at a leading international symposium.
Label-free multimodal non-linear microscopy to probe metabolism and myelin distribution
Published by
Communications Biology
Summary
Collaborative study investigating label-free multimodal non-linear microscopy techniques to analyze metabolism and myelin distribution in biological samples.
Noninvasive real-time monitoring of cellular spatiotemporal dynamics via machine learning-enhanced electrical impedance spectroscopy
Published by
Science Advances
Summary
Collaborative research on developing a machine learning-enhanced method for noninvasive, real-time monitoring of cellular spatiotemporal dynamics using electrical impedance spectroscopy.
Changes in ascending aorta and aortic arch secondary flow patterns following endovascular repair
Published by
European Journal of Vascular and Endovascular Surgery
Summary
Collaborative study analyzing changes in ascending aorta and aortic arch secondary flow patterns after endovascular repair procedures.
Microscale topography triggers dynamic 3D nuclear deformations
Published by
Advanced Science
Summary
Collaborative research demonstrating how microscale topography influences dynamic 3D nuclear deformations in cells, published in a high-impact journal.
Myoblast mutation classification via microgroove-induced nuclear deformations
Published by
Medical Imaging with Deep Learning (MIDL)
Summary
First-authored research focusing on classifying myoblast mutations through deep learning analysis of microgroove-induced nuclear deformations.
Modeling and predicting second harmonic generation from protein molecular structure
Published by
Physical Review X
Summary
Collaborative work on developing models to predict second harmonic generation signals directly from protein molecular structures.
Skills
Machine Learning & Deep Learning
PyTorch, JAX, TensorFlow, Self-supervised Learning, Generative Models, Deep Learning, Computer Vision, Multi-GPU Training.
Programming Languages
Python, Julia, R, Matlab, C/C++, JavaScript, HTML/CSS.
MLOps & Development Tools
Git, Docker, PySpark, SQL, Pandas.
Bioimaging & Medical Imaging
NIS-Elements, ImageJ/FIJI, Icy, CellProfiler, ITK, 3D Slicer, Imaris, ParaView, MONAI, DICOM, NIFTI, HDF5, ND2 formats, Image Restoration, Tumor Segmentation, Microscopy.
Research & Analysis
Biomedical Imaging, Cellular Mechanics, Data Analysis, Scientific Writing, Statistical Modeling.
Languages
Chinese
Native
English
Native
French
Proficient