Grants

1U2RTW012123-01, Funded by the Fogarty International Center
(Role: Contact Principal Investigator [PI]; Multiple PIs: Berhane, Bekele, Weke)

Award: 9/22/2021–7/31/2026

“Advancing Public Health Research in Eastern Africa Through Data Science Training (APHREA-DST)”

The unprecedented availability of increasingly complex, voluminous, and multidimensional data and the emergence of data science as a field to harness it provide ideal opportunities to address the multifaceted public health challenges faced by countries in sub-Saharan Africa. However, there is a severe lack of well-trained data scientists and home-grown educational programs to enable context-specific training. This proposal describes a plan to establish sustainable research training programs and to train a new generation of data scientists with skills, knowledge, mentoring, professional skills, and research immersion to position them for rigorous, biomedically grounded, and ethically conscious public health data science practice.

1R01HL153642-01A1, Funded by the National Heart, Lung, and Blood Institute (NHLBI)
(Role: Multi-PI; MPIs: Diaz, Cheung)

Award: 5/5/2021–4/30/2026

“Breaking Up Prolonged Sedentary Behavior to Improve Cardiometabolic Health: An Adaptive Dose-Finding Study”

This adaptive dose-finding study will determine how often and for how long people should break up periods of prolonged sedentary time to transiently improve established cardiovascular risk factors—key foundational information critical to the success of future long-term trials and, ultimately, public health guidelines.

1R21HG012345-01A1, Funded by the National Human Genome Research Institute (NHGRI)
(Role: PI)

Award: 9/8/2021–8/31/2023

“Quantitative Disease Risk Scores for Common Diseases, With Applications to eMERGE”

Labeling clinical data from electronic health records (EHRs) in health systems requires extensive knowledge of human expert, is time-consuming, and leads to inconsistencies in case definitions across different phenotyping algorithms. There is increased recognition that common diseases are not discrete entities but rather reside on a continuum. We propose here to take advantage of rich phenotype data in EHRs and to recommend quantitative disease risk scores based on unsupervised methods that require minimal input from clinicians. We propose applications to phenotypic and genomic data on approximately 100,000 individuals in the eMERGE Network and 500,000 individuals in the U.K. Biobank.

1RF1AG072272-01, Funded by the National Institute on Aging (NIA)
(Roles: MPIs: Ionita-Laza, Wei)

Award: 5/1/2021–4/30/2024

“Multi-Omics Approaches for Gene Discovery in Alzheimer’s Disease”

Alzheimer’s disease (AD) is a complex, heterogeneous disorder, partly influenced by genetics. Identifying new genes for AD is very important as it can lead to a better understanding of the molecular mechanisms underlying AD and can identify novel therapeutic targets. We propose here analytical tools that leverage diverse omics data sets, including genomics, transcriptomics, and epigenomics, to improve gene discovery. We will apply these methods to some of the largest genetic data sets in AD to identify new risk genes for AD.

DMS-2112938, Funded by the National Science Foundation (NSF)
(Roles: MPIs: Qian, McKeague)

Award: 8/1/2021–7/31/2024

“Post-Selection Inference for Survival Outcomes in Precision Medicine”

The problem of detecting informative predictors of a right-censored survival outcome has received much attention over the past decade, especially since advances in biomedical technologies have led to the collection of massive omic-type data sets. This is a project to develop novel and computationally scalable methods of post-selection inference for screening high-dimensional predictors of survival outcomes, and to use those methods to design new classes of treatment selection policies. The validity and utility of the proposed methods will be investigated in biomedical applications.

1R01MH123487-01A1, Funded by the National Institute of Mental Health (NIMH)
(Role: PI)

Award: 7/20/2021–4/30/2026

“Machine Learning Methods for Optimizing Individualized Treatment Strategies for Precision Psychiatry”

Treatments for mental health disorders are largely inadequate, in part because the practice of precision psychiatry faces extensive diagnostic heterogeneity, substantial between-patient variation in biological and clinical disease manifestation, and mismatch between diagnostic categorization and the underlying pathophysiology. This research proposes novel machine learning and statistical inference methods to address emerging challenges in precision psychiatry by discovering powerful and reliable individualized treatment strategies that address heterogeneity; factor in a patient’s clinical, psychosocial, and biological markers; and integrate evidence from multidomain data sources and multiple studies to increase generalizability and reproducibility.

National Institutes of Health (NIH)/NHLBI (R25 HL096260)
(Role: Contact PI)

2009–2024

“BEST-DP: Biostatistics & Epidemiology Summer Training Diversity Program”

Fogarty International Center (U2RTW010125)
(Role: Contact PI; MPIs: Berhane, Kumie, Samet)

2015–2022

“2/2—GEOHealth Hub for Research and Training in Eastern Africa–U.S.”

NIH/National Institute of Environmental Health Sciences (R21ES029668)
(Role: PI)

2019–2022

“Bayesian Exposure-Response Analysis for Immunoassays Data With Measurement Errors”

NIH/NIMH (R01 MH109496)
(Role: PI)

2016–2022

“Novel Methods for Evaluation and Implementation of Behavioral Intervention Technologies for Depression”

NIH/National Institute of Neurological Disorders and Stroke (NINDS) (R01 NS097423)
(Role: PI)

2016–2022

“Functional Data Analytics for Kinematic Assessments of Motor Control”

NIH/NIMH (R01MH095797)
(Role: PI)

2012–2022

“Novel Statistical Methods for DNA Sequencing Data, and Applications to Autism”

SWOG/Hope Foundatio
(Role: PI)

2020–2021

“Analysis and Visualization of Adverse Events and Patient-Reported Outcomes That Reflect Overall Treatment Toxicity Burden,”

NIH/NIA (R01AG062401)
(Role: PI)

2019–2024

“Inferential Methods for Functional Data From Wearable Devices”

NIH/National Center for Advancing Translational Sciences (KL2 TR001874)
(Role: Career Awardee)

2020–2022

“Institutional Career Development Core: Clinical and Translational Science Award”

NIH/National Institute of Biomedical Imaging and Bioengineering (R01 EB024526)
(Role: PI)

2017–2022

“Advanced Modeling Techniques for Brain Imaging Data With PET”

NIH/NIMH (K01MH118477)
(Role: PI)

2018–2022

“Statistical Methods for the Assessment of Social Engagement in Psychosis Using Digital Technologies”

NIH/National Library of Medicine (R01LM013061)
(Role: Contact PI; MPIs: Wang, Weng, Kiryluk)

2019–2024

“Big Data Methods for Comprehensive Similarity-Based Risk Prediction”

NIH/NINDS (R01 NS073671)
(Role: PI)

2011–2022

“Statistical Methods for Early Disease Prediction and Treatment Strategy Estimation Using Biomarker Signature”

NIH/NIMH (R21MH117458)
(Role: PI) 

2018–2021

“Integrative Learning to Combine Evidence for Personalized Treatment Strategies”

NIH/National Institute of General Medical Sciences (R01GM124104)
(Role: Multi-PI; MPIs: Zeng, Wang)

2018­–2022

“Efficient Statistical Learning Methods for Personalized Medicine Using Large-Scale Biomedical Data”

NIH/NHGRI (R01 HG008980)
(Role: PI)

2016–2022

“Develop Quantile Analysis Tools for Sequencing and EQTL Studies”

NSF (DMS-1953527)
(Role: PI)

2020–2023

“Conditional Quantile Random Forest With Biomedical and Biological Applications”