UCI Space Time

Handbook of NeuroImaging Data Analysis now published. Contributors from the UCI Space-Time Group are: H Ombao (Editor) and co-authors CM Ting, C Euan, L Hu, Y Wang and A Schroeder.	Space-Time Group at UC Irvine: H. Ombao and PhD students (left to right): D. Ngo, Y. Wang and Z. Yu. Photo courtesy of Paul Kennedy.
Cruz et al (2017) develop a Robust Interrupted Time Series Model for Analyzing Complex Healthcare Intervention Data	Samdin SB has addressed the critical problem of estimating time-evolving effective brain connectivity, by developing a switching vector autoregressive model for caputuring both slow and abrupt changes in causal interactions between different brain regions.
Space-Time Group at UC Santa Cruz: R. Prado (lead), R. Guhaniyogi and graduate students.	Our clustering algorithm cluster channels that display similar spectral profile according to rest or active conditions. This project is led by C. Euan (CIMAT). Statistics collaborators are J. Ortega (Adviser at CIMAT) and H. Ombao (Mentor at UC Irvine). Brain Science collaborators: S. Cramer (UC Irvine) and D. Moorman (U Mass).
Multi-scale connectivity via structured factor analytic models. Local (within region-of-interest) and global (between regions-of-interest).	D. Ngo (PhD student) is developing new methods for conducting exploratory analysis of electroencephalograms (EEG) during rest and active conditions. With H. Ombao (UC Irvine), M. Genton (KAUST), Y. Sun (KAUST).
Rajarshi Guhaniyogi develops a nonparametric manifold regression method with random feature compression for complex high-dimensional data and he applies it to the study of face features.	Lechuan Hu develops a computationally scalable method to study and analyze high dimensional multichannel brain signals and he applies it to study LFPs obtained from a rat in an olfactory study
A structured factor analytic model to study dependence in a default mode network using massive whole brain fMRI data.	It is possible to integrate structural information and fMRI data for studying multi-Subject effective connectivity.
Space-Time Modeling Group at UC Irvine.H. Ombao (lead) and graduate students.	How does stroke alter brain functional connectivity? Z. Yu (PhD Student), H. Ombao and R. Prado develop Bayesian models to identify differences between the stroke and healthy populations. Brain Science collaborator: S. Cramer (UC Irvine).

WELCOME TO THE SPACE-TIME GROUP

We are a statistical group primarily based at UC Irvine and UC Santa Cruz. However, our research network currently includes investigators at other Academic institution worldwide, primarily University of Minnesota, and KAUST (Saudi Arabia).
Our focus is on developing novel statistical methods and models for analyzing massive spatio-temporal data with complex dependence structures.
We collaborate with scientists on study design, modeling & analysis of space-time data arising from various fields such as neuroscience, neurology, psychiatry, sociology and epidemiology.
Through collaborative projects, undergraduate and graduate students are engaged in all phases of inter-disciplinary research including model formulation, implementation and communication of results.

NEWS

MEETING INFORMATION

Winter 2018: Monday 9:30-10:30am, in Room DBH 2222 (UCI).

October 11, 2017

New Videos! Recent advances on big data in brain science.

The Data Science Initiative sponsored a two-day workshop on February 16 and 17, 2017 that highlighted recent advances on big data in brain science.

Check out below the videos featuring Dr. Mark Fiecas (UMN) and Dr. Scott Makeig (UCSD)

Data Science Initiative Videos

September 01, 2017

Modeling Binary Time Series

Congratulations to Xu Gao (UCI). His paper "Modeling Binary Time Series Using Gaussian Processes With Application to Predicting Sleep States" has been accepted for publication in the Journal of Classification. He also developed an R package "HIBITS" which implements the new method. See in the Publications & Software pages for links.

Paper & R package

August 07, 2017

JSM 2017

The following students presented their research at JSM 2017: Maricela Cruz (student paper awardee); Cheng-Han Yu (UCSC, student paper awardee); Xu Gao (UCI, student paper awardee), Dustin Pluta (UCI), and Brian Hart (UMN).

The following faculty also had presentations at JSM 2017: Raquel Prado (lecturer for a course); Mark Fiecas (invited session); Michele Guindani (invited session); Hernando Ombao (invited session); and Zhaoxia Yu (invited session).

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Space-Time Modeling Group

Handbook of NeuroImaging Data Analysis now published. Contributors from the UCI Space-Time Group are: H Ombao (Editor) and co-authors CM Ting, C Euan, L Hu, Y Wang and A Schroeder.

Space-Time Group at UC Irvine: H. Ombao and PhD students (left to right): D. Ngo, Y. Wang and Z. Yu. Photo courtesy of Paul Kennedy.

Cruz et al (2017) develop a Robust Interrupted Time Series Model for Analyzing Complex Healthcare Intervention Data

Samdin SB has addressed the critical problem of estimating time-evolving effective brain connectivity, by developing a switching vector autoregressive model for caputuring both slow and abrupt changes in causal interactions between different brain regions.

Space-Time Group at UC Santa Cruz: R. Prado (lead), R. Guhaniyogi and graduate students.

Multi-scale connectivity via structured factor analytic models. Local (within region-of-interest) and global (between regions-of-interest).

D. Ngo (PhD student) is developing new methods for conducting exploratory analysis of electroencephalograms (EEG) during rest and active conditions. With H. Ombao (UC Irvine), M. Genton (KAUST), Y. Sun (KAUST).

Rajarshi Guhaniyogi develops a nonparametric manifold regression method with random feature compression for complex high-dimensional data and he applies it to the study of face features.

Lechuan Hu develops a computationally scalable method to study and analyze high dimensional multichannel brain signals and he applies it to study LFPs obtained from a rat in an olfactory study

A structured factor analytic model to study dependence in a default mode network using massive whole brain fMRI data.

It is possible to integrate structural information and fMRI data for studying multi-Subject effective connectivity.

Space-Time Modeling Group at UC Irvine.H. Ombao (lead) and graduate students.

How does stroke alter brain functional connectivity? Z. Yu (PhD Student), H. Ombao and R. Prado develop Bayesian models to identify differences between the stroke and healthy populations. Brain Science collaborator: S. Cramer (UC Irvine).

WELCOME TO THE SPACE-TIME GROUP

NEWS

MEETING INFORMATION