Hey there 👋

I'm Marc-Antoine Nadeau

B.Eng Software Engineering Student at McGill University

I have experience in Data Science & AI at La Caisse (Caisse de dépôt et placement du Québec), Electronic Trading at Morgan Stanley, and Power System Automation & Cybersecurity at Hydro-Québec.

Passionate about the intersection of finance and technology, I am seeking opportunities in the financial industry where I can apply my technical skills and analytical mindset to solve complex problems.

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Project Spotlight:

Trading Deep Q-Networks (TDQN) - A Reinforcement Learning Approach to Trading
Reinforcement Learning

Enhanced a Trading Deep Q-Network (TDQN) based on academic research by integrating Rainbow DQN components (Dueling Architecture, Noisy Nets, PER) and implementing an Actor-Critic algorithm. Achieved improved performance on Tesla and Apple stocks, with consistent profitability even during market downturns.

Comparative Analysis of LLMs and Traditional Models for Emotion Classification on the GoEmotions Dataset
Large Language Models

This study evaluated DistilGPT-2, GPT-2, BERT, Multinomial Naive Bayes, and Random Forest models on the GoEmotions dataset, focusing on accuracy, recall, and efficiency. Multinomial Naive Bayes showed moderate performance, leveraging Count Vectorization and Laplace smoothing effectively for text processing, but class imbalance led to lower recall for less frequent emotion labels.

Comparing MLPs, CNNs, and Pre-trained MobileNetV2 for Organ Classification on the OrganAMNIST Dataset
Computer Vision

The complexity of modern medical imaging datasets has driven the need for advanced machine learning models capable of achieving accurate and efficient classification. This repository explores the OrganAMNIST dataset, a challenging benchmark derived from medical imagery, to evaluate the effectiveness of different neural network architectures for organ classification.

View More Projects

Trading Deep Q-Networks (TDQN) - A Reinforcement Learning Approach to Trading Reinforcement Learning

Comparative Analysis of LLMs and Traditional Models for Emotion Classification on the GoEmotions Dataset Large Language Models

Comparing MLPs, CNNs, and Pre-trained MobileNetV2 for Organ Classification on the OrganAMNIST Dataset Computer Vision

Trading Deep Q-Networks (TDQN) - A Reinforcement Learning Approach to Trading
Reinforcement Learning

Comparative Analysis of LLMs and Traditional Models for Emotion Classification on the GoEmotions Dataset
Large Language Models

Comparing MLPs, CNNs, and Pre-trained MobileNetV2 for Organ Classification on the OrganAMNIST Dataset
Computer Vision