About Me

Hi there! I'm a passionate Data Scientist who loves building AI systems that make a real difference. Whether it's helping nurses provide better care with automated LLM Agents or making the web more accessible to the blind and low-vision with advanced computer vision systems, I'm energized by the potential of AI to solve complex problems.

Beyond the world of algorithms and data, I am an avid sports enthusiast and an outdoor activities fan. My weekends are often spent exploring the great outdoors, whether it's hiking through nature trails or ice skating on a frozen lake. I have a profound love for animals and find peace in strolling through nature.

Work Experience

Data Scientist @ Appox - Full Time

Montreal, Canada, Aug 2024-Now

As a Data Scientist at Appox, I help our clients meet their productivity challenges. To achieve this, I apply state of the art data science techniques such as domain understanding, meticulous exploratory data analysis, predictive and generative machine learning models, and efficient reporting. In a recent NLP project, our clients saw a 57% increase in productivity, helping save thousands of work hours over a year, using a solution I designed and implemented.

Tools: Python, SQL, Scikit-Learn, Pandas, Seaborn, HF Transformers, Spark, Databricks, Azure, MLFlow, Git.

Machine Learning Engineer @ Shared Reality Lab - Part Time

Montreal, Canada, 2022-Aug 2024

As a Machine Learning Engineer at the Shared Reality Lab (SRL), I led the development of an LLM-based assistant that helped nurses provide better care for dementia patients. This solution reduced administrative workload for healthcare staff while improving patient engagement through conversational AI.

During another project, I enhanced web accessibility for visually impaired users by developing computer vision models that extract and translate visual web content into accessible formats. This system now serves thousands of blind and low-vision users daily, significantly improving their ability to participate in online information exchange and decision-making processes.

Tools: Python, SQL, PyTorch, LangChain, Scikit-Learn, Pandas, openCV, HF Transformers, AWS, Docker, Git, Linux.

Deep Learning Researcher @ Ecole Normale Supérieure - Internship

Paris, France, May-Aug 2022

As a research intern at ENS, I worked on using deep learning to generate images from molecule formulas that could streamline the drug discovery process for cancer treatment. By adapting the DALL-E architecture, we were able to reduce the need for time-consuming and expensive wet-lab experiments.

Tools: Python, PyTorch, openCV, albumentation, HF Transformers, Docker, Git, Linux.

Education

Master of Science in Computer Engineering @ McGill University

Montreal, Canada, 2022-2024

At McGill, I've had the opportunity to dive deep into the latest techniques in AI and machine learning. From developing transformer models for cancer treatment discovery to fine-tuning language models for disaster response, I've gotten hands-on experience applying AI to a wide range of domains. For my thesis, I'm working on using large language models to automatically summarize charts and graphs - a tool that could make data insights more accessible for the Blind or Low Vision.

Courses: Artificial Intelligence, Machine Learning, Deep Learning, Graph Neural Networks, Statistics, Human-Computer Interaction.

Master of Science in Computer Science @ ENSTA Paris

Paris, France, 2020-2022

My studies at ENSTA gave me a rock-solid foundation in machine learning, from the mathematical underpinnings to classic techniques like computer vision and reinforcement learning. Highlights included building a model to predict energy prices from real world indicators and developing an RL AI-agent that learns how to play a custom C++ game.

Courses: Artificial Intelligence, Machine Learning, Deep Learning, Feature Engineering, Reinforcement Learning, Statistics, Distributed Systems, Software Development, Cloud Computing, SQL, C++, Java, Git, Linux.

Bachelor of Science in Engineering

Paris, France, 2017-2020

My undergraduate studies in engineering laid a strong foundation in mathematics, physics, and computer science - all critical for my future work in AI. Courses in linear algebra, probability, statistics, algorithms, and data structures equipped me with the analytical thinking and problem-solving skills needed to tackle complex challenges in machine learning and data science.

Projects

I'm always tinkering on side projects to learn new skills and try out ideas. If you want to know more about them, feel free to check out some of my blog posts.