Recent Projects…

I’ve been inspired by the increasing desire to efficiently tune machine learning hyper-parameters and make better generalization under distributional shifts. I am looking for rigorously analyse conventional and non-conventional assumptions inherent to Bayesian optimisation and approximate inference. Automating the process of choosing the best forecasting model and its corresponding parameters can result to improve a widerange of real-world applications.

Financial Time Series Forcasting with Transformser ∙ Morgan Stanley, NY, USA ∙2022

Benchmarks for Out-of-Distribution Generalization in Time Series Tasks ∙ MILA, Montreal, Canada ∙2021

Improved Deep Learning Workflows Through Hyperparameter Optimization with Oríon ∙ IBM, Canada ∙2020

Machine Learning enthousiast researcher,
curious about all intersections of data and society.
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Nadhir Hassen

Machine Learning Research