Michele Peruzzi
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Informed spatially aware patterns for multiplexed immunofluorescence data Open
Multiplexed immunofluorescence (mIF) imaging has revolutionized the study of cellular interactions within tissue microenvironments, enabling complex pattern analysis critical to understanding disease biology. However, current analytical me…
SHADE: A Multilevel Bayesian Approach to Modeling Directional Spatial Associations in Tissues Open
Motivation Spatial dependencies in tissue microenvironments, particularly asymmetric interactions between cell types, are central to understanding immune dynamics, tumor behavior, and tissue organization. Existing spatial statistical metho…
Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data Open
Scalable spatial GPs for massive datasets can be built via sparse Directed Acyclic Graphs (DAGs) where a small number of directed edges is sufficient to flexibly characterize spatial dependence. The DAG can be used to devise fast algorithm…
Inside-out cross-covariance for spatial multivariate data Open
As the spatial features of multivariate data are increasingly central in researchers' applied problems, there is a growing demand for novel spatially-aware methods that are flexible, easily interpretable, and scalable to large data. We dev…
Radial neighbours for provably accurate scalable approximations of Gaussian processes Open
In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable O(n) computational complexity. In these models, data at each location are typically assume…
Bag of DAGs: Inferring Directional Dependence in Spatiotemporal Processes Open
We propose a class of nonstationary processes to characterize space- and time-varying directional associations in point-referenced data. We are motivated by spatiotemporal modeling of air pollutants in which local wind patterns are key det…
Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes Open
In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable $O(n)$ computational complexity. In these models, data at each location are typically assu…
View article: A novel model to accurately predict continental-scale timing of forest green-up
A novel model to accurately predict continental-scale timing of forest green-up Open
The yearly cycles in vegetation greenness are among the most important drivers of ecosystem processes. Predictive models for the timing of vegetation greenup and senescence are crucial for understanding how biological communities respond t…
Spatial meshing for general Bayesian multivariate models Open
Quantifying spatial and/or temporal associations in multivariate geolocated data of different types is achievable via spatial random effects in a Bayesian hierarchical model, but severe computational bottlenecks arise when spatial dependen…
Bag of DAGs: Inferring Directional Dependence in Spatiotemporal Processes Open
We propose a class of nonstationary processes to characterize space- and time-varying directional associations in point-referenced data. We are motivated by spatiotemporal modeling of air pollutants in which local wind patterns are key det…
Accelerating Economic Growth: The Science beneath the Art Open
Rapid and sustained accelerations in economic growth can have huge implications for poverty alleviation and people's wellbeing, but does the economic profession have the knowledge to engineer them? Conspicuously disappointing outcomes for …
Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data Open
Scalable spatial GPs for massive datasets can be built via sparse Directed Acyclic Graphs (DAGs) where a small number of directed edges is sufficient to flexibly characterize spatial dependence. The DAG can be used to devise fast algorithm…
Spatial Multivariate Trees for Big Data Bayesian Regression Open
High resolution geospatial data are challenging because standard geostatistical models based on Gaussian processes are known to not scale to large data sizes. While progress has been made towards methods that can be computed more efficient…
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains Open
We introduce a class of scalable Bayesian hierarchical models for the analysis of massive geostatistical datasets. The underlying idea combines ideas on high-dimensional geostatistics by partitioning the spatial domain and modeling the reg…
Bayesian Modular and Multiscale Regression Open
We tackle the problem of multiscale regression for predictors that are spatially or temporally indexed, or with a pre-specified multiscale structure, with a Bayesian modular approach. The regression function at the finest scale is expresse…