Publications (55)
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya
Elizaveta Semenova, Swapnil Mishra, Samir Bhatt +2
Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the st…
Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation
Samir Bhatt, Ewan Cameron, Seth R Flaxman +3
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to sup…
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra +7
Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An impor…
Leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees
Matthew J Penn, Neil Scheidwasser, Joseph Penn +3
Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogen…
Continuous football player tracking from discrete broadcast data
Matthew J. Penn, Christl A. Donnelly, Samir Bhatt
Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used. T…
Entropic Riemannian Neural Optimal Transport
Alessandro Micheli, Silvia Sapora, Anthea Monod +1
Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian manifolds, where Euclidean geomet…
LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
Yicheng Feng, Hairong Chen, Ziyu Jia +2
Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important for…
Generalised Bayesian distance-based phylogenetics for the genomics era
Matthew J. Penn, Neil Scheidwasser, Mark P. Khurana +3
As whole genomes become widely available, maximum likelihood and Bayesian phylogenetic methods are demonstrating their limits in meeting the escalating computational demands. Conve…
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection
Iwona Hawryluk, Swapnil Mishra, Seth Flaxman +2
Model selection is a fundamental part of the applied Bayesian statistical methodology. Metrics such as the Akaike Information Criterion are commonly used in practice to select mode…
SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Maya Bechler-Speicher, Andrea Zerio, Maor Huri +5
Real-world temporal data often consists of multiple signal types recorded at irregular, asynchronous intervals. For instance, in the medical domain, different types of blood tests…
phylo2vec: a library for vector-based phylogenetic tree manipulation
Neil Scheidwasser, Ayush Nag, Matthew J Penn +6
Phylogenetics is a fundamental component of evolutionary analysis frameworks in biology and linguistics. Recently, the advent of large-scale genomics and the SARS-CoV-2 pandemic ha…
KidSat: satellite imagery to map childhood poverty dataset and benchmark
Makkunda Sharma, Fan Yang, Duy-Nhat Vo +6
Satellite imagery has emerged as an important tool to analyse demographic, health, and development indicators. While various deep learning models have been built for these tasks, e…
Nonparametric Bounds for Evaluating the Clinical Utility of Treatment Rules
Johannes Hruza, Erin Gabriel, Arvid Sjölander +2
Evaluating the value of new clinical treatment rules based on patient characteristics is important but often complicated by hidden confounding factors in observational studies. Sta…
Inference of COVID-19 epidemiological distributions from Brazilian hospital data
Iwona Hawryluk, Thomas A. Mellan, Henrique H. Hoeltgebaum +8
Knowing COVID-19 epidemiological distributions, such as the time from patient admission to death, is directly relevant to effective primary and secondary care planning, and moreove…
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
Mélodie Monod, Alessandro Micheli, Samir Bhatt
We introduce NeuralSurv, the first deep survival model to incorporate Bayesian uncertainty quantification. Our non-parametric, architecture-agnostic framework captures time-varying…
Evaluation of clinical utility in emulated clinical trials
Johannes Hruza, Arvid Sjölander, Erin Gabriel +2
Dynamic treatment regimes have been proposed to personalize treatment decisions by utilizing historical patient data, but they may not always improve on the current standard of car…
Mapping malaria seasonality: a case study from Madagascar
Michele Nguyen, Rosalind E. Howes, Tim C. D. Lucas +17
Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions…
Generative Modeling on Metric Graphs via Neural Optimal Transport
Alessandro Micheli, Yueqi Cao, Anthea Monod +1
We introduce, to our knowledge, the first deep generative modeling framework for probability distributions continuously supported on compact metric graphs. Given source and target…
Inhomogeneous branching trees with symmetric and asymmetric offspring and their genealogies
Frederik M. Andersen, Marc A. Suchard, Carsten Wiuf +1
We define symmetric and asymmetric branching trees, a class of processes particularly suited for modeling genealogies of inhomogeneous populations where individuals may reproduce t…
Phylo2Vec: a vector representation for binary trees
Matthew J Penn, Neil Scheidwasser, Mark P Khurana +3
Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes i…
VAE: a stochastic process prior for Bayesian deep learning with MCMC
Swapnil Mishra, Seth Flaxman, Tresnia Berah +3
Stochastic processes provide a mathematically elegant way model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interest…
Dynamic Graph-Based Forecasts of Bookmakers' Odds in Professional Tennis
Matthew J Penn, Jed Michael, Samir Bhatt
Bookmakers' odds consistently provide one of the most accurate methods for predicting the results of professional tennis matches. However, these odds usually only become available…
Semi-Mechanistic Bayesian Modeling of COVID-19 with Renewal Processes
Samir Bhatt, Neil Ferguson, Seth Flaxman +3
We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis c…
Stochastic Optimal Control of Epidemic Processes in Networks
Lars Lorch, Abir De, Samir Bhatt +3
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes an…
A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models
Nicolas Banholzer, Thomas Mellan, H Juliette T Unwin +3
Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting h…
Unifying incidence and prevalence under a time-varying general branching process
Mikko S. Pakkanen, Xenia Miscouridou, Matthew J. Penn +5
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a ti…
On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective
Swapnil Mishra, Tresnia Berah, Thomas A. Mellan +6
Renewal processes are a popular approach used in modelling infectious disease outbreaks. In a renewal process, previous infections give rise to future infections. However, while th…
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Xenia Miscouridou, Samir Bhatt, George Mohler +2
Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can…
A unified machine learning approach to time series forecasting applied to demand at emergency departments
Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan +7
There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily r…
Modelling the Stochastic Importation Dynamics and Establishment of Novel Pathogenic Strains using a General Branching Processes Framework
Jacob Curran-Sebastian, Frederik Mølkjær Andersen, Samir Bhatt
The importation and subsequent establishment of novel pathogenic strains in a population is subject to a large degree of uncertainty due to the stochastic nature of the disease dyn…
Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring
Yixuan Zhang, Yang Song, Hao Wang +2
Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden. Existing wast…
Graph Mixing Additive Networks
Maya Bechler-Speicher, Andrea Zerio, Maor Huri +5
We introduce GMAN, a flexible, interpretable, and expressive framework that extends Graph Neural Additive Networks (GNANs) to learn from sets of sparse time-series data. GMAN repre…
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
Giovanni Charles, Timothy M. Wolock, Peter Winskill +3
Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recen…
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Elizaveta Semenova, Prakhar Verma, Max Cairney-Leeming +3
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generato…
PriorVAE: Encoding spatial priors with VAEs for small-area estimation
Elizaveta Semenova, Yidan Xu, Adam Howes +4
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical m…
Regularised B-splines projected Gaussian Process priors to estimate time-trends of age-specific COVID-19 deaths related to vaccine roll-out
Mélodie Monod, Alexandra Blenkinsop, Andrea Brizzi +7
The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 a…
Phylogenetics in a warm place: computational aspects of the Tropical Grassmannian
Samir Bhatt, John Sabol, Papri Dey +3
Phylogenetic trees provide a fundamental representation of evolutionary relationships, yet the combinatorial explosion of possible tree topologies renders inference computationally…
iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
Yang Song, Yixuan Zhang, Lingfa Meng +5
Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not expose the latent interactions…
Interdomain Attention: Beyond Token-Level Key-Value Memory
Naoki Kiyohara, Harrison Bo Hua Zhu, Riccardo El Hassanin +4
Transformers and deep state space models (SSMs) sit at opposite ends of a basic design choice: attention routes each query through a growing key-value (KV) cache by content-based m…
Spatial Analysis Made Easy with Linear Regression and Kernels
Philip Milton, Emanuele Giorgi, Samir Bhatt
Kernel methods are an incredibly popular technique for extending linear models to non-linear problems via a mapping to an implicit, high-dimensional feature space. While kernel met…
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes
James A. Scott, Axel Gandy, Swapnil Mishra +4
This article introduces epidemia, an R package for Bayesian, regression-oriented modeling of infectious diseases. The implemented models define a likelihood for all observed data w…
Intrinsic Randomness in Epidemic Modelling Beyond Statistical Uncertainty
Matthew J. Penn, Daniel J. Laydon, Joseph Penn +7
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease r…
Diffusion Models for Inverse Problems in the Exponential Family
Alessandro Micheli, Mélodie Monod, Samir Bhatt
Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian measurement noise, restricting thei…
Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model
Shozen Dan, Yu Chen, Yining Chen +5
Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many contact surveys have been conducted to measure changes in human interactions in the face o…
BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling
Hengguan Huang, Xing Shen, Songtao Wang +5
Human cognition excels at transcending sensory input and forming latent representations that structure our understanding of the world. While Large Language Model (LLM) agents demon…
Riemannian Neural Optimal Transport
Alessandro Micheli, Yueqi Cao, Anthea Monod +1
Computational optimal transport (OT) offers a principled framework for generative modeling. Neural OT methods, which use neural networks to learn an OT map (or potential) from data…
Quantum Algorithms for the Minimum Steiner Tree problem with application to Binary Near-Perfect Phylogenies
Lingfa Meng, David Salvador Novo, Albert H. Werner +1
We present a quantum algorithm in bioinformatics for solving the Binary Near-Perfect Phylogeny Problem (BNPP) with a complexity bound of , where n is the numb…
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh +3
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating…
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features
Jean-Francois Ton, Seth Flaxman, Dino Sejdinovic +1
The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple line…
PlayClass: Automated Play Behaviour Classification in Poultry
Prince Ravi Leow, Neil Scheidwasser, Rebecca Oscarsson +3
Automated monitoring of animal welfare has largely targeted negative indicators, leaving positive welfare behaviours such as play underexplored. To address this gap, we present Pla…
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Seth Flaxman, Swapnil Mishra, Axel Gandy +20
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have imple…
Recurrent Memory for Online Interdomain Gaussian Processes
Wenlong Chen, Naoki Kiyohara, Harrison Bo Hua Zhu +3
We propose a novel online Gaussian process (GP) model that is capable of capturing long-term memory in sequential data in an online learning setting. Our model, Online HiPPO Sparse…
The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic
Christian Morgenstern, Daniel J. Laydon, Charles Whittaker +4
The COVID-19 pandemic has caused over 6.4 million registered deaths to date and has had a profound impact on economic activity. Here, we study the interaction of transmission, mort…
A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA
Chiara Forlani, Samir Bhatt, Michela Cameletti +2
In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from m…
Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies
Kaustubh Chakradeo, Pernille Nielsen, Lise Mette Rahbek Gjerdrum +5
As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers…