Amine Mohamed Aboussalah is an Industry Assistant Professor in the Department of Finance and Risk Engineering at the NYU Tandon School of Engineering. He earned his Ph.D. in Artificial Intelligence and Operations Research at the University of Toronto. His research interests lie broadly in artificial intelligence and dynamical systems. He enjoys applying theoretical mathematical concepts such as information geometry to develop new machine learning algorithms for a variety of practical real-world dynamical systems applications. He uses the financial application domain as a challenging real-world dynamical systems environment in which to advance reinforcement learning. Professor Aboussalah's primary research interest is improving reinforcement learning algorithms for solving and controlling dynamical systems by exploiting topological properties of time-series data and partial differential equations. As a teacher, he likes to mix theory and practice by sharing both his research and his industry experiences.
Publications
- Aboussalah, A. M., et. al. (2025) Gaussian Mixture Models Based Augmentation Enhances GNN Generalization. International Conference on Machine Learning (ICML).
- Aboussalah, A. M., Chi, C., & Lee, C. G. (2023). Quantum computing reduces systemic risk in financial networks (PDF). Nature Scientific Reports, 13(1), 3990
- Aboussalah, A. M., Kwon, M., Patel, R. G., Chi, C., & Lee, C. G. (2023) Recursive Time Series Data Augmentation (PDF). In The Eleventh International Conference on Learning Representations (ICLR).
- Aboussalah, A.M., C Chi, EB Khalil, J Wang, Z Sherkat-Masoumi. (2022) A Deep Reinforcement Learning Framework For Column Generation. Neural Information Processing Systems (NeurIPS).
- Aboussalah, A.M.,, Z Xu, CG Lee. (2021) What is the value of the cross-sectional approach to deep reinforcement learning?. Quantitative Finance.
- Aboussalah, A. M., Lee, C. G. (2020) Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization. Expert Systems with Applications.
