Amine Mohamed Aboussalah

I am a Ph.D. Candidate at the University of Toronto, Department of Mechanical and Industrial Engineering. I am working with Professor Chi-Guhn Lee at the Dynamic Optimization & Operations Management Lab. I am funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) – Alexander Graham Bell Canada Graduate Fellowship and by the Fonds de recherche du Québec – Nature et technologies (FRQNT) fellowship.

I earned Dual Bachelor and Master Degrees in Engineering Physics, Aerospace Engineering, Astrophysics and Applied Mathematics from ISAE-SUPAERO and Polytechnique Montréal, as well as a Postgraduate Diploma in Innovation Management from HEC Paris. In these programs, I focused on physics, applied mathematics, engineering, and strategy.

I worked at the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making (CERC-DS4DM) led by Professor Andrea Lodi. I also worked on an Astrophysics project at the French Alternative Energies and Atomic Energy Commission (CEA-Saclay), a Radiation Oncology Physics at the Cancer University Institute of Toulouse (Oncopole), and a Planetology project related to the MARS InSight Mission with NASA-JPL.

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Research

I am interested in the development of new artificial intelligence and quantum computing algorithms for real-world applications. My thesis topic is “High-dimensional continuous reinforcement learning for finance”. My goal is to use the financial application domain as a challenging real-world environment in which to advance reinforcement learning (RL). My thesis research is about improving reinforcement learning algorithms by exploiting topological properties (symmetries) of dynamical systems and time series data. I am also interested in econophysics problems which involve using quantum field theory and quantum computing to study financial networks and systemic risk.

Reinforcement Learning

We hypothesize that the concept of symmetry augmentation is fundamentally linked to learning. We propose a new representation that enables symmetry augmentation and show that it can enhance any reinforcement learning model. Mathematical background and proofs are presented.

We hypothesize that the concept of symmetry augmentation is fundamentally linked to learning. We propose a new representation that enables symmetry augmentation and show that it can enhance any reinforcement learning model.

We propose a methodology that allows us to assess the quality of the actions taken by the RL agent. This could allow portfolio manager practitioners to better understand the investment RL policy.

A deep recurrent neural network-based reinforcement learning algorithm is capable of providing continuous control over multiple assets with an objective of maximizing the portfolio return under some financial constraints.

Other Research Projects

Analysis of brain tissues from Glioblastoma multiforme (GBM) patients shows that lipid droplets are highly enriched in tumor tissues while undetectable in normal brain tissues.

Global warming is a much discussed topic as it sparks debate for shaping government policy and how humans should behave in reaction to climate change. Global warming can be evaluated with a local perspective by looking at temperature trends in an isolated region. In this work we predict a local warming trend for Canada’s capital city Ottawa, Ontario up to the year 2040 using optimization and machine learning techniques.

We present the results of the infrared (IR) and optical observations of the counterpart of the black hole (BH) X-ray transient Swift J1745-26 during September 2012 rise and March 2013 decay outburst. We determined the system is a low-mass X-ray binary (LMXB).

This work aims at understanding the role played by the innovative supply chain strategy put in place by Boeing in the numerous problems encountered by its 787 Dreamliner program.

Teaching

Entrepreneurial

Quantitative research firm applying scientific techniques, AI, and Quantum Computing to find patterns in large, noisy real-world financial data sets. Currently in R&D phase.

Political Risk Consultancy leveraging AI and Quantum Computing to forecast protest-related risk.

Carpooling start-up to address the problematic isolation of regions that are poorly served by public transportation.