Mahmoud Mahfouz

AI Research Lead @ J.P. Morgan AI Research & PhD Student @ Imperial College London

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I am an AI Research Lead at J.P. Morgan’s AI Research lab, an academic research lab led by Prof. Manuela Veloso, and a part-time Ph.D. student at Imperial College London, supervised by Prof. Danilo Mandic.

My research investigates the development of autonomous agents for complex, multi-agent financial market environments. I am interested in solving sequential decision-making problems in these dynamic, non-stationary, and partially observable environments. My work employs techniques such as deep reinforcement learning, behavioral cloning, agent-based modeling, and LLM-powered multi-agent systems. I primarily focus on applying these methods to (1) general decision-making problems in finance and (2)algorithmic trading problems within limit order book markets, covering areas like universe selection, asset allocation, and optimal trade scheduling and execution.

At J.P. Morgan, I began my career as a front-office software engineer developing and supporting global trader-facing software systems. I later transitioned into a data scientist role, designing machine learning models for time-series anomaly detection and anti-money laundering screening.

Before joining J.P. Morgan, I interned as a software engineer at Goldman Sachs, robotics engineer at Culham Centre for Fusion Energy (CCFE) and applications engineer at Keysight Technologies. My most interesting summer experience was definitly at the Culham Centre for Fusion Energy (CCFE), where I performed learnt a lot about the remote-handling robotic systems used in the Joint European Torus (JET) nuclear fusion tokamak.

I obtained my Master of Engineering (MEng) in Mechatronic Engineering from The University of Manchester, graduating with First Class (Hons) and ranked 3rd in the Electrical & Electronic Engineering department. During my studies, I built small and large robotic systems for industrial (electronics assembly) and research (landmine detection) purposes. My final year group project focused on (1) building a testbed for emulating landmine detection sweeping and (2) investigating the integration of various sensory data for speeding up the landmine detection algorithms developed by the group. The project was sponsored by Find A Better Way, a charity founded by Sir Bobby Charlton.

news

Mar 12, 2025 Happy to share our latest work, “Entropy-Aware Branching for Improved Mathematical Reasoning,” which is now available as a preprint on arXiv.
Nov 12, 2024 Our paper, “The State of the Art of Large Language Models on Chartered Financial Analyst Exams,” was presented at the EMNLP 2024 Industry Track in Miami, Florida.
May 20, 2024 Our work on “Capacity planning and scheduling for jobs with uncertainty in resource usage and duration” has been published in The Journal of Supercomputing.
Nov 02, 2023 Our patent application for a “Method and system for solving reconciliation tasks by integrating clustering and optimization” has been published.
Apr 13, 2023 Our patent application for a “Method and system for providing dynamic workspace scheduler” has been published.
Feb 13, 2023 Presented our work, “Towards Asset Allocation Using Behavioural Cloning and Reinforcement Learning,” at the AAAI 2023 Bridge Program on AI for Financial Services.
Jan 20, 2023 Our paper, “FinRDDL: Can AI planning be used for quantitative finance problems?,” has been published in the proceedings of the FinPlan-2023 Workshop.
Oct 13, 2022 Our work, “Towards Robust Representation of Limit Orders Books for Deep Learning Models,” is available on arXiv. This work explores the stability of LOB representations.
Sep 22, 2022 Our patent, “System and method for institutional risk identification using automated news profiling and recommendation,” has been filed.
Nov 03, 2021 Pleased to present our paper, “Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets,” at the ACM International Conference on AI in Finance (ICAIF-2021).
Sep 17, 2021 Our new preprint, “A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling,” is now available on arXiv.
Jul 23, 2021 Presented our research, “How Robust are Limit Order Book Representations under Data Perturbation?,” at the ICML 2021 Workshop on Representation Learning for Finance and E-Commerce Applications.
Oct 15, 2020 Our paper, “Generating synthetic data in finance: opportunities, challenges and pitfalls,” was presented at the ACM International Conference on AI in Finance (ICAIF-2020).
Dec 14, 2019 Presented our paper, “Get Real: Realism Metrics for Robust Limit Order Book Market Simulations,” at the NeurIPS 2019 Workshop on Robust AI in Financial Services.
Dec 14, 2019 Our paper, “On the Importance of Opponent Modeling in Auction Markets,” was presented at the NeurIPS 2019 Workshop on Robust AI in Financial Services.
Jun 14, 2019 Presented “How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?” at the ICML 2019 Workshop on AI in Finance.
Mar 14, 2019 Our preprint, “Compression and interpretability of deep neural networks via tucker tensor layer,” is available on arXiv.