2024 Conference

The third BioInference conference is taking place at the University of Warwick on 5th-7th June 2024. This year’s conference will combine a data-driven meeting on day 1 (see Initiatives for further information) with the main two-day conference taking place on 6th-7th June 2024.

This year’s conference is organised by Enrico Bibbona (Politecnico di Torino); Ioana Bouros (Oxford); Julia Brettschneider (Warwick); Raiha Browning (Warwick); Fergus Cooper (Oxford); Marina Evangelou (Imperial College London); Aden Forrow (Maine); Constandina Koki (Warwick); Ben Lambert (Oxford); Chon Lok Lei (Macau); Massimiliano Tamborrino (Warwick); Tom Thorne (Surrey); Yongchao Huang (Aberdeen).

The conference was generously sponsored by:

Logo of the University of Warwick
Logo of Elsevier
Logo of the Centre for Research in Statistical Methodology at the University of Warwick
Logo of the European Society for Mathematical and Theoretical Biology

Conference Information

The conference will consist of contributed talks and poster sessions, with more than 20 poster presentations. The full lists of featured talks and posters can be found below.

Click to access the schedule of the conference, and the book of abstracts.

Registration

You can register for the main conference by completing the registration form at https://warwick.ac.uk/fac/sci/statistics/news/bioinference2024/registration until the 26th May 2024.

Note that the registration is considered complete and successful only after having paid the £70 registration fee (covering all coffee breaks, lunch breaks, wine and food reception on the 6th June 2024).

Practical information

We kindly ask participants to arrange their own transportation and accommodation. On-campus, there are two options for accommodation at the conference centres Scarman and Radcliffe. Alternatively, one could easily stay in Coventry, Kenilworth or Leamington Spa. Further details on how to get to campus are available here.

Featured Talks

  • Hannah Bensoussane (University of Warwick) - Bayesian individual-level infectious disease modelling: heterogeneous transmission and dealing with costly likelihood evaluation when estimating missing data
  • Alex Browning (University of Oxford) - “Little data” in mathematical oncology
  • Helena Coggan (University College London) - An agent-based modelling framework to study cell plasticity in non-small cell lung cancer
  • Sarah Filippi (Imperial College London) - Variational Bayes for high-dimensional proportional hazard models
  • Guglielmo Gattiglio (University of Warwick) - Nearest Neighbor GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers
  • Hong Ge (University of Cambridge) - TBC
  • Andonis Gerardos (AMU) - MiSFI, a robust algorithm to select a minimal model for dynamical data with large sampling intervals
  • Petar Jovanovski (Chalmers University of Technology and University of Gothenburg) - Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations
  • Cathal Mills (University of Oxford) - A multi-disciplinary approach for wavelet analysis, climate- based modelling, and probabilistic ensemble forecasting of dengue epidemic dynamics
  • Hamid Rahkooy (University of Oxford) - Algebraic identifiability of partial differential equation models
  • Nicolas Rubido (University of Aberdeen) - Small-worldness favours network inference in synthetic neural networks
  • Heba Sailem (King’s College London) - Deep learning approaches for identifying predictive biomarkers from the tumour microenvironment.
  • Vahid Shahrezaei (Imperial College London) - Bayesian model discovery for revers-engineering biochemical networks from data
  • Catalina Vallejos (University of Edinburgh) - Using routine healthcare data to predict future health
  • Andrea Mario Vergani (Human Technopole & Politecnico di Milano) - Prediction of incident cardiovascular events using cardiac MRI-derived latent factors
  • Qiquan Wang (Imperial College London) - A Topological Gaussian Mixture Model for Bone Marrow Morphology in Leukaemia
  • Huizi Zhang (University of Edinburgh) - Bayesian modelling of RNA velocity from single-cell RNA sequencing data

Featured Posters

  • Tarek Alrefae (University of Oxford) - Heterogeneity in Models of Infectious Disease
  • Jake Carson (University of Warwick) - Inference of Infection Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes per host
  • Yu Chen (Imperial College London) - Bayesian rate consistency model to uncovering the hidden structure of contemporary sexual networks in Africa
  • Luca Del Core (University of Nottingham) - Accounting for stochastic gating whilst estimating ion channel kinetics from whole-cell patch-clamp recordings
  • Richard Everitt (University of Warwick) - Ensemble Kalman inversion approximate Bayesian computation
  • Kit Gallagher (University of Oxford) - Using a Kalman filter to infer biological parameters from imperfect time series data
  • Alicia Gill (University of Warwick) - Bayesian Inference of Reproduction Number from Epidemic and Genomic Data using MCMC Methods
  • Andrii Krutsylo (Institute of Computer Science Polish Academy of Science) - The Forward-Forward Algorithm: Biologically Inspired Optimization for Continual Learning
  • Alessia Mapelli (Politecnico di Milano) - Graphs for representation of interacting biological systems and prediction of complex diseases
  • Thomas Morrish (University of Warwick) - An approximate likelihood framework motivated by the irregular movement of animals
  • Michael Plank (University of Canterbury) - A compartment-based model of Covid-19 in New Zealand: exploiting model structure to improve inference methods
  • Ian Roberts (University of Warwick) - Bayesian Inference for the Structured Coalescent
  • Kristian Romano (University of Warwick) Hidden Markov Models for Real Time Telemetric Monitoring of the Circadian Rhythm
  • Elena Sabbioni (Politecnico di Torino) Regularized MANOVA test for zero-inflated semicontinuous high-dimensional data
  • Joseph Shuttleworth (University of Nottingham) - Using many different protocols to characterise discrepancy in mathematical ion channel models
  • Nicholas Steyn (University of Oxford) - Sequential Monte Carlo methods for reproduction number estimation
  • Nenad Suvak (University of Osijek) - Time-changed SIRV model for epidemic of SARS-CoV-2 virus
  • Jia Le Tan (University of Warwick) - Pareto Smoothed Sequential Monte Carlo
  • Joseph Lok Hei Tsui (University of Oxford) - Optimal disease surveillance with graph-based active learning
  • Jo ̃ao Pedro Valeriano Miranda (Instituto de F ́ısica Te ́orica, Universidade Estadual Paulista Ju ́lio de Mesquita Filho) - Recovering the dynamics of unobserved quantities in stochastic processes
  • Sarah Vollert (Queensland University of Technology) - Constructing constraint-informed prior distributions for inference in data-limited scenarios: a case study in ecosystem population models
  • Kate Woolley-Allen (University of Warwick) - Multiplicative transposase cutting bias in ATAC-seq data
  • Mengxin Xi (King’s College London) - Extrapolation methods for Bayesian Inverse Problems
  • Dominic Zhou (University of Warwick) - Adaptive MCMC inference in the Kingman coalescent model – propriety, ergodicity, efficiency