Babak Shahbaba

Babak Shahbaba

Associate Professor

Departments of Statistics and Computer Science

University of California, Irvine


Scalable Bayesian Inferences

Nonparametric Bayesian Methods

Statistical Methods in Biological Sciences

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ACTIVITIES


Mentoring And Supervision

Current Group Members

Klaus Telkmann, Postdoc, Statistics

Tian Chen, PhD student, Statistics

Luis De Jesus Martinez Lomeli, PhD student, Systems Biology

Sandra Gattas, MD/PhD Student, Medical Scientist Training Program (co-advising with Norbert Fortin)

Former Group Members

Andrew James Holborook, PhD Student, 2018 (currently a postdoc at UCLA)

Alexander Vandenberg-Rose, Research Scientist, 2017 (currently a data scientist at Obsidian Security)

Cheng Zhang, PhD student (co-advised with Hongkai Zhao), 2017 (currently a postdoc at Fred Hutchinson)

Megan Smith, Postdoc (co-advised with Dan Gillen), 2016 (currently a senior statistician at UCI)

Bo Zhou, PhD student, 2015 (currently a risk analyst at BNY Mellon)

Shiwei Lan, PhD student, 2013 (currently an Assistant Professor at University of Illinois, Urbana-Champaign)


Research Grants

Current

R01MH115697 (Shahbaba), 01/18-12/22                                           

NIH/NIMH

Role: PI

Scalable Bayesian Stochastic Process Models for Neural Data Analysis

The overarching goal of this study is to understand the neural basis of complex behaviors and temporal organization of memories. To this end, we will develop a new powerful and scalable class of statistical models for studying multimodal neural data using Bayesian stochastic processes and computationally efficient algorithms. The potential clinical impact of this study is broad. Our research will address fundamental and unresolved questions about hippocampal function, and these novel approaches may subsequently lead to unprecedented insight into the neural mechanisms underlying memory impairments.


DMS 1622490 (Shahbaba), 08/16-07/19                    

NSF

Role: PI

Theory and practice for exploiting the underlying structure of probability models in big data analysis

The objective of this project is to combine geometric techniques with computational algorithms in order to scale up statistical methods used for big data analysis.


R01 AI107034 (Minin),  05/13-04/18                       

NIH      

Role: Co-Investigator

Bayesian Modeling and Data Integration in Infectious Disease Phylodynamics

The objective of this project is to develop new statistical methodology for analysis of population dynamics of infectious disease agents by integrating gene sequencing and other data collected in infectious disease surveillance programs.


DMS 1763272 (Nie), 07/18-06/23

NSF/Simons Foundation

Role: Senior Personnel

The NSF-Simons Center for Multiscale Cell Fate

The overarching objective of this center is to investigate how cells differentiate into different cell types.


Completed

IIS 1216045 (Welling),  09/12-08/15

NSF

Role: Co-PI

Efficient Bayesian Learning from Stochastic Gradients

This proposal studies a new family of MCMC procedures that requires only very few data-cases per update.


R01 MH091351 (Buss), 12/10-11/15                                        

NIH/ National Institute of Mental Health

Role: Key Personnel

Fetal Programming of the Newborn and Infant Human Brain

The goal of this proposed research is to test specific hypotheses about the effects of in-utero biological stress exposure on human brain morphology and white matter integrity at birth and over the first year of postnatal life. 


R01 HD065825-01 (Entringer), 07/10-06/15                            

NIH-NICHD

Role: Key Personnel

Prenatal Stress Biology, Infant Body Composition and Obesity Risk

The overall objective of this project is to evaluate the impact of maternal biological stress during pregnancy on infant body composition and metabolic function.


R01 HD060628 (Wadhwa, PI), 02/10-01/15                                          

NIH-NICHD

Role: Key Personnel

EMA Assessment of Biobehavioral Processes in Human Pregnancy

The overall objective of this project is to evaluate the impact of maternal psychosocial and biological stress, assessed with state-of the art ambulatory measures, on length of gestation.


R01 ES012243 (Delfino), 04/11-01/16               

NIH-NIEHS

Role: Key Personnel

Transcriptomic, Oxidative Stress, and Inflammatory Responses to Air Pollutants

This study would be among the first using repeated measurements to analyze the relation between chemically characterized air pollutants and genome-wide gene expression patterns in peripheral blood cells from a high-risk population of elderly

individuals.


Selected Invited Talks And Conference Presentations

Dynamic Bayesian models for Neural Data Analysis, 9th International Purdue Symposium on Statistics, June 2018

Decoding of Hippocampal Neural Activity Using Deep Learning Methods, Workshop on Deep Learning, Tokyo, March 2018

Wormhole Hamiltonian Monte Carlo, MCQMC at Stanford, August 2016

Variational Hamiltonian Monte Carlo, ICERM at Brown University, July 2016

Scalable Monte Carlo Methods, UCLA, October 28, 2015

Scalable Monte Carlo Methods, University of Texas at Austin, October 16, 2015

A Dynamic Bayesian Model for Cross-Neuronal Interactions, JSM, Seattle, August 13, 2015

Dependent Matern Process, 3rd Meeting on Statistics, Athens, June 2015

UCI Neurology Grand Rounds, October 2014

A Non-stationary Copula Model for Simultaneously-recorded Neurons, California State University, Fullerton, 2014

Dirichlet Process Mixture of Gaussian Processes for Joint Modeling of Longitudinal and Survival Data, ISBA 2014

A Gaussian Process Model for Estimating Within-Subject Volatility in Longitudinal Models, UCSD, Spring 2014

A Semiparametric Bayesian Model for Detecting Multiway Synchrony Among Neurons, ENAR 2014

Geometric Methods in Markov Chain Monte Carlo, UCSC, Spring 2014

A Gaussian Process Model for Estimating Within-Subject Volatility in Longitudinal Models, UCSD, Spring 2014

Dirichlet Process Mixture of Gaussian Processes for Joint Modeling of Longitudinal and Survival Data, ISBA 2014

Towards Scalable Bayesian Inference, Duke University, 2013

Split Hamiltonian Monte Carlo, JSM, 2013

Hamiltonian Monte Carlo and Its Variations, Department of Mathematics, UCI, 2013

Split Hamiltonian Monte Carlo, University of Washington, 2012

Bayesian Gene Set Analysis, MD Anderson, 2012

Bayesian Nonparametric Variable Selection, JSM, 2012

Bayesian Relevance Determination, California State University, Fullerton, 2012

Bayesian Relevance Determination, WNAR, 2011

Bayesian Gene Set Analysis, SDSU, 2010


Editorial Works And Reviews

Associate editor for JASA/TAS Reviews, 2014, Present

Associate editor for CHANCE, 2011-Present

Member of Scientific Review Committee (SRC) at UCI, 2012-2014

I have severed in several  NSF panels


I have reviewed manuscripts for many journals including:

Journal of the Royal Statistical Society, Journal of the American Statistical Association (JASA), Bayesian Analysis, Biometrics, Statistical Science, Journal of Machine Learning Research (JMLR), Statistics in Medicine, Statistical Analysis and Data Mining, Artificial Intelligence, Journal of Applied Statistics, Biometrical Journal, IEEE Transactions on Neural Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Statistical Applications in Genetics and Molecular Biology, Physics in Medicine and Biology, Pattern Recognition Letter


Affiliations

Centers

Center for Complex Biological Systems

Center for Machine Learning and Intelligent Systems

Institute for Genomics and Bioinformatics

Institute for Clinical and Translational Science


Organizations

American Statistical Association (ASA)

The International Society for Bayesian Analysis (ISBA)

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(949) 824-0623

2224 DBH, UC Irvine, CA 92697

babaks at uci dot edu

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