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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, as well as applications in astronomy, chemistry, and health.
For a list of Vianey's publications, follow this link: google scholar
In progress:
In progress:
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.
Detecting flares from stellar time series using Gaussian processes + hidden Markov models
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
Copula modeling of Serially Correlated Multivariate Data with Hidden Structures
Publication: Zimmerman et al
Collaborators: Robert Zimmerman + Radu Craiu
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