More than the sum of its parts
Complex data and interconnected systems, made clear.
Cillian Hourican
Postdoctoral researcher, University of Amsterdam · independent consultant.
Computational science · machine learning · networks · information theory.
01What I do
Three threads, one toolkit.
The same toolkit — computational science, machine learning, networks and information theory — runs through my research, my consulting, and my teaching.
Research
Postdoctoral research at the University of Amsterdam's Computational Science Lab — the information theory of health: disease and symptom networks, higher-order interactions, and causality in multimorbidity.
Consulting
Independent computational modelling, data science and machine-learning work — turning messy, interconnected data into models teams can actually act on. Co-founder of CausalixAI.
Teaching
Years of lecturing and supervising in computational science at UvA — from stochastic simulation and data analysis to student thesis projects.
02Selected work
Recent publications.
A few highlights — the full, categorised list lives on the publications page, or explore everything as an interactive map.
- 2026Multimorbidity & networksPLOS Computational Biology
Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
In older adults, some symptoms, signs and behaviours only reveal their effect on health when they occur together rather than one at a time. This work introduces an open, bias-aware workflow — partial information decomposition plus a "BUST" map — that detects and interprets these "together-only" synergistic combinations across a large ageing cohort, flagging feature pairs that conventional association measures overlook.
- 2024Information theoryEntropy
Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets
Some meaningful patterns in data only emerge when several variables are considered jointly (synergy), but finding these combinations usually means checking an astronomically large number of possibilities. This paper introduces stochastic search strategies that locate synergistic sets efficiently without exhaustive enumeration, making the analysis practical on large biomedical datasets.
- 2023Multimorbidity & networksFrontiers in Systems Biology
Understanding multimorbidity requires sign–disease networks and higher-order interactions, a perspective
Multimorbidity research usually counts diseases or looks at them in pairs, which hides how signs, symptoms and diseases actually combine. This perspective argues for networks that link signs, symptoms and diseases together and capture higher-order (synergistic) interactions as hypergraphs — showing, via a synthetic model, that pairwise thinking can miss the best intervention or produce unexpected "side-effects" a hypergraph reveals.
03Across domains
- Cardiovascular disease
- Depression & mental health
- Ageing & multimorbidity
- Genomics (GWAS)
- Gut microbiome
- Economic systems
04Consulting
Freelance modelling & data work.
Computational modelling, data science and statistics · machine learning · network and causal analysis · workshops and training. Available for freelance engagements alongside my academic work.