Mohammed Benslimane

WORK DONE
• Built a domain-specific conversational AI system using Transformer-based dialogue models like DialoGPT and RAG (retrieval-augmented generation).
• Integrated the model with knowledge retrieval systems for real-time, contextually relevant responses, achieving state-of-the-art intent accuracy.

Machine learning is a relatively new approach to optimizing a trader's portfolio in financial trading. In this sense, predicting the rise or the fall of asset prices is good leverage that can be exploited in buying an asset before the price rises and shorted before it declines. Subsequently, we used reinforcement learning methods to optimize the allocation between risky and riskless assets in this project. Sending signals and receiving rewards from the environment is the key to any reinforcement learning algorithm. So for this purpose, we presented Q-learning to backtest the strategy in terms of cumulative profits by maximizing the Sharpe ratio's value function. Hence, a recurrent reinforcement learning (RRL) algorithm was deduced from this strategy. Simple examples based on a method described above and tested on ”real world” demonstrated promising results and a Sharpe ratio of four or above.

This project is a robust object tracking algorithm using a collaborative model as the main challenge for object tracking is to account for drastic changes in appearance. I proposed a robust appearance model that exploits holistic templates and local representations.

I developed a sparsity-based discriminative classifier (SDC) and a sparsity-based generative model (SGM). Results outperformed state-of-the-art technology (MOT challenge) in complex scenes.