Postdoctoral Research Fellow
School of Mathematics, University of Birmingham
Research areas
Geometric graphs, biomedical imaging, weak supervision, spectral methods, detection limits, reproducible scientific computing.
Contact
b.cardoen@bham.ac.uk
Spectral-geometric methods for biological discovery under limited observability
I develop mathematical and computational methods for inference, reconstruction, and identifiability in noisy geometric data. My work combines graph signal processing, spectral analysis, topology, and uncertainty-aware algorithms to study biological systems where interaction and function cannot be directly observed.
The common thread is inference under limited observability: when biological structure is only partially measured, I ask what can still be recovered, how stable the recovery is, and what uncertainty should remain.
Interaction graph between two SMLM point-cloud datasets.
Research Profile
- Inference limits: identifiability, observability, detectability, and uncertainty bounds.
- Stable reconstruction: spectral methods, graph signal processing, topology, and geometric graphs.
- Measurement-limited discovery: biological imaging and spatial data where direct observation is incomplete.
Limits
Stability
Discovery
Selected Publications
Heavy-tailed noise in geometric graphs
Spectral effects of vertex noise in geometric graphs, with implications for when structure can be recovered from noisy spatial measurements.
Under review at SIAM SIMAX, 2026.
Computational reconstruction in super-resolution microscopy
Frameworks and methods for reconstructing biological interaction when acquisition limits prevent direct observation.
Patterns, 2025.
Subprecision interaction detection
An algorithmic approach that made nanoscale organelle contact structure measurable in 3D microscopy data.
Journal of Cell Biology, 2023.
Full publication record: ORCID
Teaching
Signal Processing for Biological Graphs
I designed and delivered a de novo ten-lecture component for fourth-year Topics in Applied Mathematics at the University of Birmingham. The unit used biological graph data as a route into graph induction, graph spectra, noise effects, spectral clustering, and spectral filtering.
Vision: build conceptual understanding and mathematical judgement under uncertainty, so students can transfer methods to new problems rather than follow recipes.
Execution: created lectures, continuous assignments, and an exam for a research-led applied mathematics topic.
Evidence: archived the course materials and the AI-assisted teaching workflow used to make them reproducible and auditable.
Evaluation: across the ten evaluated items, student responses averaged 23% Agree and 77% Strongly agree (13/24 responses), with strongest scores for intellectual challenge, critical thinking, applied learning, academic support, and contribution to knowledge and skills.
Lecture materials: Signal Processing for Biological Graphs 
Presentation: Improving Accuracy and Reproducibility in Research-Topic STEM Lectures 
Supervision And Mentoring
I currently co-supervise two PhD projects, each at 40% supervision allocation: one on fractal geometry, and one on hormetic systems modelling. I have also supervised or assessed MSc, MSci, and undergraduate projects across graph-based inference, microscopy analysis, and reproducible scientific computing.
My mentoring focuses on decision-making under uncertainty: helping students distinguish what is supported by evidence from what is merely plausible, choose models whose assumptions match the data-generating process, and communicate conclusions with appropriate uncertainty.
Presentations
Improving Accuracy and Reproducibility in Research-Topic STEM Lectures
IDAI presentation on agentic paths for auditable, reproducible lecture production.
Agentic Coding for STEM Research
COSIMO-IDAI tutorial on agentic coding workflows for research.
