Marc Vincent

Senior Data Scientist @Institut Imagine

Paris, FR
MOBILE NUMBERS
+91 *********19

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WORK HISTORY

Sep 2018 — Present

Senior Data Scientist @Institut Imagine

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Paris, FR

Project supervision- Data modeling- Deep learning applied to Natural Language Processing- Research- Sofware development

EDUCATION

2005

Université Paris Cité

Master's degree, Bioinformatics

2004

Pierre and Marie Curie University

Master's degree, Biology, specialty developmental physiology

2006 — 2009

Università degli Studi di Firenze

Doctor of Philosophy (PhD), Ingénierie informatique

ABOUT MARC VINCENT

Broadly my areas of expertise/interest cover development of Artificial Intelligence / Machine Learning / Data Mining methods and their use to solve problems arising in different fields such as clinical data analysis, natural language processing, ecommerce and marketing, bioinformatics, image processing.I currently work on adapting Large Language Models (LLMs) to the (french) clinical domain, from insuring the proper resources are available for (open source) LLMs to run to testing models, prompting strategies and LLMs oriented frameworks.In my daily work, I perform various tasks including project management, applied research, machine learning engineering, data analysis, software development.In terms of machine learning technology, I currently work with deep learning models, including -but not limited to- large language models. I started my early career by experimenting with Support Vector Machines, taking advantage of the capacity of ad-hoc Mercer kernels to integrate various kinds of information in the learning process. I also do fancy penalized GLMs for the purpose of feature selection and of course deep learning models for their capacity to deal with complex structures in data and to produce high level representations.In terms of technical skills I mainly work on linux, am a (neo)VIM guy, develop with languages such as python, C++, R, scala, java, javascript, even perl. I have been working with data science libraries/frameworks such as pytorch, tensorflow, mxnet, scikit-learn and a variety of other tools from the python data scene (pandas, jupyterlab, seaborn.) as well as R.

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