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Hi, I'm Ines

I'm a data scientist who is passionate about multiculturalism, running, and playing the piano and saxophone.

My research interests are transfer learning, foundational models, and causal inference.

  • 🎓 I am a second year Data Science MS student in the ICME department at Stanford University.

  • 📊 I am currently a research assistant at Summer Han Lab, where I work on evaluating the efficacy of surveillance strategies for lung cancer patients with causal inference and deep learning.

  • 💉 Over the past year, I worked as a research assistant at Boussard Lab, where I finetuned a BERT model for delirium identification in unstructured data.

  • 🎓 I completed my math & engineering undergraduate studies at Centrale Supélec and Lycée privé Sainte Geneviève, near Paris.

  • 👗 I did a 3-month summer internship as a data scientist at Inditex in 2023.

  • 🌍 I am French and Spanish, have lived and worked in three countries, and am consistently eager to explore new places and engage in cultural exchange experiences.

⚡ Technologies

Python Pytorch Numpy Pandas scikit-learn R C++ C Git Latex Ubuntu Vim VScode Microsoft Azure Amazon AWS

Top languages Github Stats

Ines Dormoy's Projects

42-piscine-2017 icon 42-piscine-2017

42 Paris Coding School 1-month ientry examination bootcamp. Paticipated in july 2017 and got admitted (top 5%)

cme211-idormoy icon cme211-idormoy

Stanford CME 211 class: Software Development for Scientists and Engineers Basic usage of the Python and C/C++ programming languages are introduced and used to solve representative computational problems from various science and engineering disciplines

cme213 icon cme213

Stanford CME213 class Introduction to parallel computing using MPI, openMP, and CUDA

dynamiclm icon dynamiclm

Add dynamic stacking feature for time-varying covariates

intercentrales icon intercentrales

Inter Centrales AI Competition 2022 Ayoub Bao Ines Valentin. Computer vision for data augmentation.

st7-optimization icon st7-optimization

CentraleSupélec group project: Assignment, scheduling, and routing of multi-skilled technicians to serve customers with unique service time windows, skills requirements, and geographical locations with Gurobi implementation of mathematical modeling.

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