Curriculum Vitae

Eduardo Alberto Aguilar-Bejarano

5 City Road, Beeston, Nottingham, UK, NG9 2LQ

eduardo.aguilar-bejarano@nottingham.ac.uk | LinkedIn | GitHub

Download Full CV (PDF)

Education

  • University of Nottingham, UK - PhD in Chemistry, AI Doctoral Training Centre (Expected: September 2026). Supervisors: Prof. Simon Woodward, Prof. Ender Özcan, Dr. Grazziela Figueredo.
  • Universidad de Costa Rica, Costa Rica - BSc Chemistry with Honors, GPA: 9.23/10 (2018-2022).

Research Experience

  • Data Scientist Engineer - University of Nottingham (2024 - Present)

    • Working for the EPSRC Large Grant in Designing bio-instructive materials for translation-ready medical devices developing ML models for polymer-based biomaterials property prediction, using generative models for novel material discovery (generation of topographies and monomers), and as software engineer collaborating to the development of Helix .

  • PhD Researcher - University of Nottingham (2022 - Present)

    • Working with Prof. Woodward, Dr. Figueredo, and Prof. Özcan on GNN models for catalysis optimization and materials prediction.

  • Research Assistant - University of Costa Rica (2021 - 2022)

    • Developed cheminformatic models for predicting bioactive molecule profiles and isomerization constants.

Publications

  • Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis - iScience, 2025. Read More
  • Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach - Molecules, 2025. Read More
  • Describing Chiral Ligands in Palladium-catalyzed Asymmetric Allylic Allylation - ChemRxiv, 2024. Read More
  • Explainable GNN-Derived Structure-Property Relationships in Interstitial-Alloy Materials - Research Square, 2024. Read More

Conferences & Presentations

  • University of Nottingham Postgraduate Symposium (2024) - Poster Presentation on HCat-GNet.
  • Faraday Community Poster Symposium (2023) - Feature Identification in Molybdenum Carbides.
  • Machine Learning for Atomistic Modelling (2023) - Poster on GNN vs Human Empirical Search.

Honors & Awards

  • Christopher J. Moody Synthesis and Catalysis Poster Prize - University of Nottingham, 2024.
  • Artificial Intelligence Doctoral Training Centre Scholarship - University of Nottingham, 2022.
  • Bachelor of Science with Honors - Universidad de Costa Rica, 2022.

Technical Skills

  • Machine Learning & AI: Deep Learning, Graph Neural Networks (GNNs), Explainable AI, Model Optimization, Transfer Learning, Self-Supervised Learning.
  • Programming & Development: Python, PyTorch, PyTorch Geometric, TensorFlow, Scikit-Learn, RDKit, NumPy, Pandas, Bash, R, C.
  • AI Infrastructure & Deployment: High-Performance Computing (HPC), GPU Acceleration, Model Deployment, Git, GitHub.
  • Data Science & Visualization: Data Preprocessing, Feature Engineering, Matplotlib, Seaborn, Plotly, Streamlit.
  • Scientific Computing & Cheminformatics: RDKit, OpenBabel, DataWarrior, Pymol, Molecular Modeling.
  • Laboratory Techniques: HPLC, Gas Chromatography, UV/Vis Spectrophotometry.
  • Languages: Spanish (native), English (fluent).