ACTIVITIES
Mentoring And Supervision
Current Group Members
Wenzhuo Zhou, Postdoctoral Fellow, Statistics, Co-supervising with Annie Qu
Derenik Haghverdian, PhD student, Statistics (NSF GRFP Awardee)
Zahra Moslemi, PhD Student, Statistics
Yueqi Ren, MD/PhD Student, Co-supervising with Craig Startk (NIH F30 Awardee)
Brian Schetzsle, PhD student, Statistics
Former Group Members
Rui Miao, Postdoc, Statistics, 2023 (Co-supervised with Annie Qu); Mathematical Statistician, NIH/NHLBI
Francesco Denti, Postdoc, Statistics, 2022; Assistant Professor, Università Cattolica del Sacro Cuore, Milan
Michelle Ngo, PhD Student, Systems Biology, 2022; Data Scientist at Merck
Luis De Jesus Martinez Lomeli, PhD Student, Systems Biology 2021; Data Scientist at Mythic
Lingge Li, PhD Student, Statistics, 2020 (Co-supervised with Pierre Baldi; ); Data Scientist at Meta
Tian Chen, PhD Student, Statistics, 2019; Data Scientist at Cylance
Forest Agostinelli, PhD Students, CS 2019 (Co-mentored with Pierre Baldi); Assistant Proferssor at University of South Carolina
Andrew Holborook, PhD Student, Statistics, 2018 (Savage Award runner-up); Assistant Professor at UCLA
Cheng Zhang, PhD Student, Mathematics, 2017 (Co-advised with Hongkai Zhao); Assistant Professor at Peking University
Alexander Vandenberg-Rodes, Research Scientist, 2017; Data Scientist at Obsidian Security
Sepehr Akhavan, PhD Student, Statistics, 2016, (Co-supervised with Dan Gillen); Data Scientist at Meta
Bo Zhou, PhD Student, Statistics, 2015; Principal Quantitative Analyst at Capital One)
Shiwei Lan, PhD Student; Statistics, 2013; Assistant Professor at Arizona State University
Research Grants
Current
IIS-2123366 (Lead PI: Shahbaba), 09/21-08/24
NSF
Collaborative Research: HDR DSC: Data Science Training and Practices: Preparing a Diverse Workforce via Academic and Industrial Partnership
Through engaging students selected from a pool of highly diverse populations in STEM areas, this project, California Data Experience Transformation (CADET), will facilitate data science training via curriculum development, hands-on experiences, and close interactions with both academic and non-academic organizations.
R01MH115697 (Shahbaba), 01/18-12/23
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.
See our GitHub page for a brief report of our findings and results.
Completed
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.
DMS1936833 (PI: Shahbaba), 08/19-07/22
NSF
MODULUS: Data-Driven Mechanistic Modeling of Hierarchical Tissues
This project will develop new statistical and mathematical models that describe how cells and molecules within cells self organize to perform biological functions within an organism. More specifically, we will use our models to investigate hematopoiesis, which is a remarkable biological process responsible for creation and maintenance of blood cells, and involves complex interactions among biochemical and physical events across temporal and spatial scales that are still not well-understood. Additionally, this project will provide undergraduate and graduate students with a true interdisciplinary experience with equal mentorship from data and biological scientists.
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.
See our GitHub page for a brief report of our findings and results.
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 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.
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
Workshop on Computational Statistics and Data-Driven Models (Virtual), Brown University, ICERM 2020.
Novel Statistical Methods for Complex Data, Vina del Mar, Chile, March 25 to 29, 2019.
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, 2014-2019
Associate editor for CHANCE, 2011-2019
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, Nature Biotechnology
Affiliations
Centers
Data Science Initiative (Director)
Center for Machine Learning and Intelligent Systems
Center for Multiscale Cell Fate Research
Center for Complex Biological Systems
Institute for Genomics and Bioinformatics
Organizations
Elected Fellow of American Statistical Association (ASA)
The International Society for Bayesian Analysis (ISBA)
(949) 824-0623
2222 ISEB, UC Irvine, CA 92697
babaks at uci dot edu
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