Cillian Hourican

Education

• 2022 – present

  PhD in Computational Science

  University of Amsterdam

  Doctoral research on “Dynamic Symptom Networks to study Multimorbidity” — at the intersection of information theory, network science and causality; supervised by Dr. Rick Quax. Defense scheduled.

• 2021

  MSc Computational Science (GPA 8)

  University of Amsterdam

  Thesis on non-intrusive uncertainty quantification of a 3D in-stent restenosis model using active learning and adaptive surrogate modelling.

• 2018 – 2019

  Research Student, Optimisation & Machine Learning

  Trinity College Dublin

  Online convex optimisation and bandits; deep learning coursework (CNNs, image classification).

• 2018

  BA (Mod.) Mathematics, First-Class Honours

  Trinity College Dublin

  Final-year project on estimating social networks with stochastic block models and multiplex networks.

Selected publications

Full list with plain-language summaries on the publications page.

• Hourican, C., Peeters, G., Melis, R.J.F., Kok, A., van Schoor, N.M., Wezeman, S., Lees, M., Olde Rikkert, M.G.M., Quax, R. (2026). Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach. PLOS Computational Biology. ↗
• Peeters, G., Hourican, C., Lees, M., Quax, R., Melis, R., Kok, A., van Schoor, N.M., Olde Rikkert, M. (2026). Symptoms provide predictive information on daily functioning beyond signs and diseases in middle-aged and older adults, particularly those with multimorbidity. Age and Ageing. ↗
• Li, J., Bosch, J.A., Rydin, A.O., Hourican, C., Koloi, A., Tassi, S.C., Mishra, P.P., Mishra, B.H., Kähönen, M., Lehtimäki, T., Raitakari, O.T., Laaksonen, R., Keltikangas-Järvinen, L., Juonala, M., Quax, R. (2025). A multilayer network analysis of cardiovascular–depression comorbidity reveals symptom-specific molecular biomarkers. Psychological Medicine. ↗
• Gehlen, J., Li, J., Hourican, C., Tassi, S.C., Mishra, P.P., Lehtimäki, T., Kähönen, M., Raitakari, O., Bosch, J.A., Quax, R. (2024). Bias in O-Information Estimation. Entropy. ↗
• Koloi, A., Loukas, V.S., Hourican, C., Sakellarios, A.I., Quax, R., Mishra, P.P., Lehtimäki, T., Raitakari, O.T., Papaloukas, C., Bosch, J.A., März, W., Fotiadis, D.I. (2024). Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data. European Heart Journal – Digital Health. ↗
• Hourican, C., Li, J., Mishra, P.P., Lehtimäki, T., Mishra, B.H., Kähönen, M., Raitakari, O.T., Laaksonen, R., Keltikangas-Järvinen, L., Juonala, M., Quax, R. (2024). Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets. Entropy. ↗
• Hourican, C., Peeters, G., Melis, R.J.F., Wezeman, S.L., Gill, T.M., Olde Rikkert, M.G.M., Quax, R. (2023). Understanding multimorbidity requires sign–disease networks and higher-order interactions, a perspective. Frontiers in Systems Biology. ↗

Experience

• Apr 2026 – present

  Postdoctoral Researcher

  Computational Science Lab, University of Amsterdam

  Research on higher-order interactions, networks and causality in health and complex systems.

• 2019 – 2021

  Teaching Assistant

  University of Amsterdam

  Tutorials and assignments for Modelling System Dynamics (Vensim, AnyLogic).

• 2017 – 2019

  Teaching Assistant

  Trinity College Dublin

  Lab tutorials for first-year statistics (R) and introductory probability.

• 2017

  Summer Research Student

  Trinity College Dublin

  Changepoint-detection algorithms: frequentist and Bayesian (Gibbs sampling) approaches.

• 2016

  Summer Research Student

  LM Ericsson, Ireland

  Graph-theoretic methods for self-organising networks.