cv
Basics
| Name | Nicolas Pielawski |
| Label | Researcher |
| nicolas@pielawski.fr | |
| Summary | Nicolas is a postdoctoral researcher working at the intersection of healthcare and artificial intelligence, with a focus on deep learning, Bayesian statistics and information theory. His background includes work in medical imaging, and his current research explores how principled approaches to uncertainty, prediction, and inference can drive meaningful progress in health data science and clinical AI. |
Work
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2023 - Present Postdoctoral Researcher
Postdoctoral Researcher at Uppsala University
I am developing methods for predicting the risks incurred by patients visiting the emergency department using both deep learning and statistical methods.
- Deep learning
- Bayesian statistics
- Uncertainty quantification
- Fairness in AI
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2022 - 2022 Intern
Internship at the Broad Institute of MIT and Harvard
I worked on the development of a new method for reconstructing tissues spatially from transcriptomic data.
- Transcriptomics
- Wasserstein distance
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2018 - 2023 PhD Student in Biomedical Image Analysis
Uppsala University
I developped methods for multimodal image registration using deep learning. I also contributed to the development of TissUUmaps, a tool for interactive visualization and exploration of large-scale spatial omics data.
- Deep learning
- Interactive visualization
- Biological data analysis
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2016 - 2016 Intern
ThyssenKrupp Budapest
I added functionalities to a custom software for testing steering wheel embedded system.
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2014 - 2017 Apprentice engineer in embedded systems
ThyssenKrupp Presta France
I implemented softwares for improving the efficiency of steering wheels production lines. I also worked on the development of a network device for debugging defective production line automatons.
Education
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2018 - 2023 Uppsala, Sweden
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2014 - 2017 Nancy, France
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2012 - 2014 Schoeneck, France
Publications
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2025.01 Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models
NLDL 2025 proceedings
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2020 In Silico Prediction of Cell Traction Forces
ISBI 2020 proceedings
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2020 CoMIR: Contrastive Multimodal Image Representation for Registration
NeurIPS 2020 proceedings
Skills
| Computer Science | |
| Deep Learning | |
| Machine Learning | |
| Computer Vision | |
| Large Language Models |
| Mathematics | |
| Bayesian statistics | |
| Optimization | |
| Gaussian Processes | |
| Information Theory |
Languages
| English | |
| Fluent |
| French | |
| Native speaker |
| Swedish | |
| Intermediate |