Curriculum Vitae

Eduardo Alberto Aguilar-Bejarano

Nottingham, UK  |  eduardo.aguilar-bejarano@nottingham.ac.uk

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

  • Post-Doctoral Researcher — University of Nottingham (Nov 2024 – Present)

    • Working on the EPSRC Large Grant: Designing bio-instructive materials for translation-ready medical devices (Advisor: Dr. Grazziela Figueredo). Responsibilities include: development and deployment of ML models for polymer-based biomaterial discovery; delivering data science training; curating and modelling experimental datasets; leading development of PolyNet (end-to-end GNN research software in Python/Streamlit); and co-developing Helix via CI/CD lifecycle for traditional ML model training and interpretation.

  • PhD Researcher — University of Nottingham, Woodward–Figueredo–Özcan Groups (Oct 2022 – Present)

    • Research areas: (1) Interpretable GNNs for enantioselective catalyst optimisation — ML pipeline for catalyst design including graph representation, GNN training and interpretation; (2) Explainable AI for GNN-based prediction of interstitial alloy properties using a novel 3D masking approach; (3) Ensemble GNN models for polymer biomaterial multi-property virtual screening.

  • Research Assistant — Universidad de Costa Rica, CBIO3 Group (Jan 2021 – Oct 2022)

    • Developed cheminformatic models for predicting physical-chemical profiles of bioactive compounds — including logP (Water/Toluene) prediction using MLR, SVM, RF, and gradient boosting — and predictive models for tautomer isomerization constants in multiple solvents. Advisor: Prof. William Zamora-Ramírez.

Publications

  • Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data — Cell Patterns, 2026. Read More
  • Explainable GNN-Derived Structure–Property Relationships in Interstitial-Alloy Materials — Physical Chemistry Chemical Physics, 2025. Read More
  • Homogeneous Catalyst Graph Neural Network: A Human-Interpretable GNN 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 (Preprint), 2024. Read More

Conferences & Presentations

  • University of Nottingham School of Chemistry Postgraduate Symposium — Nottingham, UK, July 2024.
    "HCat-GNet: an Interpretable Graph Neural Network for Catalysis Optimization" (poster)
  • The GSK Prosperity Partnership — GSK, Stevenage, UK, March 2024.
    "HCat-GNet: An Interpretable Graph Neural Network for Catalysis Optimization" (lecture)
  • Faraday Community Poster Symposium — Burlington House, London, UK, November 2023.
    "Feature Identification in Molybdenum Carbides: Graph Neural Networks vs. Human Empirical Search – Who's the Winner?" (poster)
  • Machine Learning for Atomistic Modelling Autumn School — Daresbury Laboratory, UK, September 2023.
    "Feature Identification in Molybdenum Carbides: Graph Neural Networks vs. Human Empirical Search – Who's the Winner?" (poster)
  • School of Chemistry Seminar Series — Universidad de Costa Rica, San José, Costa Rica, August 2023.
    "Chemistry in the Artificial Intelligence Era" (lecture)
  • Sciences Week Scientific Poster Contest — San José, Costa Rica, September 2022.
    "Development of a Novel Lipophilicity Descriptor from Predictions of Partitions Coefficients Toluene/Water" (poster)
  • University Week Scientific Poster Contest — San José, Costa Rica, April 2022.
    "Machine Learning Methods to Determine the Toluene/Water Partition Coefficient as an Efficient Lipophilic Descriptor" (poster)
  • Gulf Coast Undergraduate Research Symposium (GCURS) — Rice University, Houston, TX, October 2021.
    "Cheminformatic and Quantum Mechanics Approaches for Quantitative Prediction of Tautomerism in Bioactive Molecules" (lecture)

Honors & Awards

  • Christopher J. Moody Synthesis and Catalysis Poster Prize — University of Nottingham School of Chemistry, July 2024.
  • Second Place, PGR Symposium Poster Contest — AI Doctoral Training Centre, University of Nottingham, 2024.
  • AI Doctoral Training Centre Scholarship — University of Nottingham, 2022. Full scholarship (tuition + stipend, 4 years).
  • Bachelor of Science with Honors — Universidad de Costa Rica, June 2022. Awarded for academic excellence.
  • CeNAT-CONARE Research Fellowship — National Center of High Technology, Costa Rica, June 2022. Grant ($4,300) for the project "Cheminformatic and Quantum Mechanics Approaches for Quantitative Prediction of Tautomerism in Bioactive Molecules".
  • Best Scientific Poster — Chemistry Students Association, Universidad de Costa Rica, April 2022.
  • GCURS Acceptance and Travel Award — Rice University, October 2021. Travel grant to present at the Gulf Coast Undergraduate Research Symposium, Houston, TX.
  • Academic Excellence Scholarship — Universidad de Costa Rica, 2019–2022. Annual scholarship awarded to students with an overall GPA of 9.0/10 or above.

Teaching Experience

  • MSc Project Co-Supervisor — University of Nottingham (May 2023 – Jul 2023)

    • Co-supervised Machine Learning in Science MSc students with Dr. Grazziela Figueredo and Dr. Kristaps Ermanis on deep learning projects in the chemical sciences.

  • Programming, ML & Data Analysis Tutor — CBIO3 Group, Universidad de Costa Rica (Jul 2022 – Sep 2022)

    • Teaching assistant for Prof. William Zamora's Cheminformatics course. Topics: programming, data visualisation, ML algorithms, and cheminformatic tools.

  • Organic & Analytical Chemistry Tutor — Universidad de Costa Rica, Campus Atlántico (Sep 2021 – Nov 2021)

    • University-hired tutor to reinforce organic and analytical chemistry for undergraduate students.

  • Chemistry Olympiad Tutor — Scientific High School of San Pedro, Costa Rica (Mar 2021 – Oct 2021)

    • Prepared high school students for the Costa Rican Chemistry Olympiad through classes and practice exercises.

  • Laboratory Teaching Assistant — Universidad de Costa Rica (2019 – 2021)

    • Assisted in six laboratory courses across Physical Chemistry, Analytical Chemistry, Organic Chemistry, and General Chemistry.

  • Chemistry Tutor for New Students — Universidad de Costa Rica (Mar 2021, Mar 2022)

    • Designed and delivered chemistry lessons for incoming students with limited prior background in the subject.

Professional Memberships

  • Royal Chemical Society — Member since September 2023.
  • American Chemical Society — Member, July 2021 – July 2022.
  • Costa Rica ACS Student Chapter — Member, June 2021 – June 2022.

Technical Skills

  • Machine Learning & AI: Deep Learning, Graph Neural Networks (GNNs), Explainable AI (XAI), Model Optimisation, Transfer Learning, Self-Supervised Learning.
  • Programming & Development: Python, PyTorch, PyTorch Geometric, scikit-learn, XGBoost, RDKit, NumPy, Pandas, Bash, R, C.
  • AI Infrastructure & Deployment: HPC / SLURM, GPU Acceleration, Docker, Git, GitHub, Streamlit.
  • Data Science & Visualisation: Data Preprocessing, Feature Engineering, Matplotlib, Seaborn, Plotly, Ray Tune, Weights & Biases.
  • Cheminformatics: RDKit, ASE, OpenBabel, SMILES/InChI, Molecular Modelling, DataWarrior, PyMOL.
  • Laboratory Techniques: HPLC, Gas Chromatography, UV/Vis Spectrophotometry.
  • Languages: Spanish (native), English (fluent).