Data Scientist, Materials scientist, and «everything related to AI » enthusiast. After being introduced to electron microscopy and spectroscopy during an internship at CIME (EPFL) in Prof. C. Hebert group, I pursued a master degree in Materials science/quantum devices. I obtained my PhD in Physics in 2018 during which I studied surface thermodynamics and reactivity of nanoalloys by environmental TEM at the University Paris Cité. Between 2019 and 2022, I was a part of the GAME-MURI project funded by Air Force Office of Scientific Research as an electron microscopy expert for the study of the crystal and electronic properties of β-Ga2O3 heterostructures at Penn State University.

Over the past 10 years, I have also acquired extensive experience in Python-based tools development for the analysis and processing of scientific data. In addition, I have completed a 9-month training program in Data Science to further enhance my skills in AI. My diverse educational background and practical experience enable me to bring a unique perspective to any IA project and tackle complex problems with innovative solutions.


Data Scientist certification

Work history

2009-2018 PhD in Physics at Paris-Cité Universit

Title: ‘Surface thermodynamics and reactivity of Cu-Au nanoalloy by environmental transmission electron microscopy under gaseous condition

2022-2023: Data Scientist certification at Datascientest: co-certified by MINES ParisTech|PSL
RNCP level 7 (block 3 RNCP 36129)

350 hours of courses: Python, Data Analysis, Data Visualization, Big Data, Data Base (SQL, PySPark), Machine Learning (regression, classification, time series..), Deep Learning (Tensorflow, Keras..), Database, Pipeline, Text Mining, Web Scrapping..

Data project: written report and oral presentation in front of a group of experts

02/2022 – today CEMES-CNRS, Toulouse – Contract researcher:
Development of tools (Python) for polarization measurement in Perovskite materials for improving 5G technology

01/2019 – 12/2021 Penn State University / US Air Force – USA – Postdoctoral researcher:
Development of tools for the study of structural and electronic properties of semiconductors by Python and Transmission Electron Microscopy

Coding skills

Materials Science skills



Data Visualisation
(e.g. Matplotlib, Seaborn, Bokeh…)

Data handling/analysis
(e.g. Numpy, Scipy, Pandas, …)

Machine Learning
Supervised/Unsupervised Learning, Reinforcement learning (Monte Carlo learning, Q-Learning, SARSA..), Deep Reinforcement learning (Deep Q learning..)

Deep Learning
(e.g. Pytorch, Tensorflow, Keras..)

Web Scrapping
(Beautiful soup..)

Python for Science
(e.g. Hyperspy, Atomap, TEMUL..)


FEI Titan, FEI Talos, FEI Tecnai, FEI Helios, FEI Scios, JEOL ARM 200F, JEOL 2200F, JEOL 2010, Hitachi I2TEM, Zeiss, Leica

Characterization techniques
HRTEM/STEM, XEDS, mono STEM-EELS, SEM, Gas cell, Electric biasing cell, HRTEM/STEM simulations

Sample preparation
PLD (nanoparticles), FIB (thin films, heterostructures, devices), Wedge-mechanical polishing, experience with clean room

Python, MATLAB, TIA, GMS3, JEMS, QSTEM, Dr. Probe, MacTempasX, Prismatic, CrystalMaker, SingleCrystal, CrystalDiffraction, Bruker Esprit, Microscoft Office, Latex, Fiji, Igor, Origin

General skills


Project management
Writing of proposals
Working in a competitive environment
Designing experiments
Technical/scientific article writing
Oral presentation/vulgarization
Student mentoring
Individual/group work
Respect of milestones

Recipient of the $2500+ award by French Society for Microscopies and IFSM Young Scientist Assembly to attend the International Microscopy Congress IMC19 in Sydney, Australia. Two oral presentations were given in front of a large audience.