Bio

I am a Ph.D. student at École Polytechnique. I work in parallel at BNP Paribas CIB. Based in Paris, I am part of the Data & AI Lab within the quantitative resarch team of global markets, where I conduct fundamental and applied research on graph machine learning for financial markets.

I am mainly interested in graph representation learning and graph signal processing. Curently, my work invovles modelling the dynamic network of user-item interactions for various downstream tasks, such as market prediction and product recommendations.

In April 2021, after 6 years of higher eduction, I have graduated with a Master’s degree in Applied Mathematics from École Polytechnique, a Master of Engineering in Image Processing and Data Science from Télécom Paris, and a Master of Engineering in Telecommunication and Computer Science from Lebanese University.

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News

• September 2024: My second paper got accepted at ICAIF ‘24 and will be presented in New York!
• June 2023: My first Ph.D. paper got accepted and will be presented at RecSys ‘23 in Singapore.
• February 2023: I am teaching labs on “Machine Learning for Time Series” at École Polytechnique Executive Education.
• June 2022: I started my CIFRE Ph.D. at École Polytechnique & BNP Paribas CIB.
• October 2021: I defended my Master’s thesis on Generating Synthetic Financial Time Series with GANs.

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Publications

2024

• Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation
  A. Ghiye, B. Barreau, L. Carlier, M. Vazirgiannis
  5th ACM Conference on Artificial Intelligence for Finance (ICAIF 2024)
  [DOI]

2023

• Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
  A. Ghiye, B. Barreau, L. Carlier, M. Vazirgiannis
  17th ACM Conference on Recommender Systems (RecSys 2023)
  [DOI] [arXiv]