Research

As a group, our research interests span many areas, from foundational statistical properties + computation to modeling ecological and other types of remotely collected sensor data, applications of AI in Bayesian statistics, as well as applications in astronomy, chemistry, and health.

For a list of Vianey's publications, follow this link: google scholar

In progress:

Sofia Ruiz Suarez: Informed machine learning in ecology, hierarchical classification structures for ecological monitoring
PIs: Vianey + Dak de Kerckhove, in partnership with the Ontario Ministry of Natural Resources.

Arturo Esquivel: Efficient approximate Bayesian computation for chemical reaction networks
Supported by a DSI Doctoral Student Fellowship award. In collaboration with The Matter Lab at U of T.

Yovna Junglee: Bayesian analysis of weighted regression models for spatio-temporal data and its application to PM2.5 mapping using satellite-derived data
Supervised by Meredith Franklin + Vianey.

Marco Gallegos Herrada: Parallel tempering for hidden Markov models
Supervised by Vianey + Jeff Rosenthal.

Rodrigo Barradas Herrera: Bayesian deep generative modeling for flare detection in stellar time series
Supervised by Vianey + Gwen Eadie. In collaboration with Elizaveta Semenova.

Vinky Wang: Machine learning approaches for forecasting environmental dynamics & integration with species distribution models
Supervised by Vianey + Meredith Franklin.

Detecting flares from stellar time series using Gaussian processes + hidden Markov models

Publication: Esquivel et al

Students involved: Arturo Esquivel + Rodrigo Barradas Herrera

Grants: (1) Data Sciences Institute (UofT) Catalyst Grant (Gwen Eadie + Radu Craiu), (2) Canadian Statistical Sciences Institute, Ontario, AI Applications in Statistical Sciences Research

Copula modeling of Serially Correlated Multivariate Data with Hidden Structures

Publication: Zimmerman et al

Collaborators: Robert Zimmerman + Radu Craiu

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