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Amine Mohamed Aboussalah is an Industry Assistant Professor in the Department of Finance and Risk Engineering at the NYU Tandon School of Engineering. He joined NYU in Fall 2022 after earning his Ph.D. in Artificial Intelligence and Operations Research from the University of Toronto, where he was funded by the Natural Sciences and Engineering Research Council of Canada Alexander Graham Bell Canada Graduate Fellowship and the Fonds de recherche du Québec – Nature et technologies fellowship. Aboussalah holds dual bachelor's and master's degrees in Engineering Physics, Aerospace Engineering, Astrophysics, and Applied Mathematics from a joint program at ISAE-SUPAERO in Toulouse and Polytechnique Montréal, completed in 2013. He also obtained a postgraduate diploma in Innovation Management from HEC Paris, specializing in aerospace sector strategies. His prior experience includes research assistant positions at the Canadian Space Agency, where he conducted a master's thesis on black holes; the Cancer University Institute of Toulouse (Oncopole); the French Alternative Energies and Atomic Energy Commission (CEA-Saclay) on an astrophysics project; and NASA-JPL on a planetology project for the MARS InSight Mission. Additionally, he founded DeepAlpha, a quantitative research firm, and Maidan Analytics, a political risk consultancy, and collaborated with the World Bank Group.
Aboussalah's research focuses on artificial intelligence and dynamical systems, applying concepts such as information geometry to create machine learning algorithms for real-world dynamical systems applications. He advances reinforcement learning algorithms using the financial domain as a testbed, exploiting topological properties of time-series data and partial differential equations to solve and control dynamical systems. Selected publications include 'Gaussian Mixture Models Based Augmentation Enhances GNN Generalization' presented at the International Conference on Machine Learning (2025); 'Quantum computing reduces systemic risk in financial networks' in Nature Scientific Reports (2023); 'Recursive Time Series Data Augmentation' at the International Conference on Learning Representations (2023); 'A Deep Reinforcement Learning Framework For Column Generation' at Neural Information Processing Systems (2022); 'What is the value of the cross-sectional approach to deep reinforcement learning?' in Quantitative Finance (2021); and 'Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization' in Expert Systems with Applications (2020). As an educator, he blends theory and practice, drawing from his research and industry background, including teaching machine learning for financial engineers.