Abed Hammoud
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    • Reinforcement Learning, Optimal Control, and Bayesian Filtering in Data Assimilation
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    • Speaking at AGU Fall Meeting 2025 — Developments in ML across Earth-System Modeling
    • Awarded the Gordon and Betty Moore Foundation Postdoctoral Fellowship!
    • Convening a session at AGU 2025
    • Abstract accepted to AMS BLT '25!
    • Presented at the Princeton Fluids Seminar!
    • I was awarded with the KAUST PSE dean's award! (First to achieve the highest number of awards possible within a PhD)
    • NCAR Exploratory Computing Allocation awarded (UPRI0030)
    • Invited talk at the RWTH-Aachen Uncertainty Quantification Seminar
    • Started my postdoc at Princeton University
    • Selected as a Princeton–NOAA GFDL CIMES Fellow
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    • Defended my PhD at KAUST!
    • I was awarded with the KAUST PSE dean's award for 2024!
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    • Accepted to the Les Houches Summer School for 200 Years of Navier Stokes and Turbulences!
    • Gave a Talk at the Ocean Colloquium!
    • I was awarded with the inaugural KAUST PSE dean's award!
    • Gave a Talk at ISDA 2022!
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  • Publications
    • From Standard to Bayesian: Revisiting Ocean Color Model Evaluation
    • The new Climate Change Center of Saudi Arabia — A big step towards understanding and predicting the distinct climate of the Arabian Peninsula
    • On the potential of Bayesian neural networks for estimating chlorophyll-a concentration from satellite data
    • Identification of Moving Sources in Stochastic Fields: A Bayesian Inferential Approach with Application to Marine Traffic in the Mediterranean Sea
    • Downscaling Using CDAnet Under Observational and Model Noises: The Rayleigh-Bénard Convection Paradigm
    • Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
    • Two-step AI-aided Bayesian source identification of urban-scale pollution
    • Oil spill risk analysis for the NEOM shoreline
    • Global Sensitivity Analysis of an Oil Spill Model: a Regularized Regression Approach.
    • Semantic Segmentation of Mesoscale Eddies in the Arabian Sea: A Deep Learning Approach
    • CDAnet: A Physics-Informed Deep Neural Network for Downscaling Fluid Flows
    • A Physics-Informed Deep Neural Network for Backward in Time Prediction: Application to Rayleigh-Bénard Convection
    • Continuous and discrete data assimilation with noisy observations for the Rayleigh-Bénard convection: a computational study
    • Moving source identification in an uncertain marine flow: Mediterranean Sea application
    • Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea
  • Publication_feats
    • Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
    • CDAnet: A Physics-Informed Deep Neural Network for Downscaling Fluid Flows
    • A Physics-Informed Deep Neural Network for Backward in Time Prediction: Application to Rayleigh-Bénard Convection
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Invited talk at the RWTH-Aachen Uncertainty Quantification Seminar

Oct 21, 2024 · 1 min read

Gave an invited seminar on AI for state estimation in chaotic systems.

Last updated on Oct 21, 2024
Abed Hammoud
Authors
Abed Hammoud
Postdoctoral Research Associate
← NCAR Exploratory Computing Allocation awarded (UPRI0030) Mar 15, 2025
Started my postdoc at Princeton University Aug 1, 2024 →

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