Grants

New Grants

Kiros Berhane 

2U2RTW010125-07 (Renewal) funded by The Fogarty International Center (Role: Contact PI / MPIs: Berhane, Jack, Kumie, Simane)  
Award: 9/28/20215-2/28/2027 

“Geohealth Hub for Research and Training in Eastern Africa – US” 

This renewal application proposes to continue a progressive and tiered training program that will further develop researchers and research teams able to carry out the research agenda of the Eastern Africa GEOHealth Hub and to facilitate the translation of the research findings into impactful actions by key stakeholders. The Hub will train 6 Hub Scholars (building on 11 Hub Scholars from the first funding cycle), degree level trainees (5 PhD and 12 MS/MPH degree candidates), research team trainees across the three LMIC primary partners, and additional trainees from stakeholders as well as three more training partner countries who are all expected to become national leaders in environmental and occupational health research. The Hub will continue to train multidisciplinary research teams, develop curricular materials for academic and stakeholder institutions, and foster evidence translation and implementation with a long-range goal of establishing a sustainable Hub for the region. 

1R25HL161786-01 funded by the National Heart, Lung and Blood Institute (Role: Contact PI/MPI: Christine Mauro) 
Award: 01/15/2022-12/31/2026 

“Summer Institute for Training in Biostatistics and Data Science at Columbia (SIBDS@Columbia)” 

Technological advances are leading to unprecedented abundance of increasingly complex, voluminous, and multi-dimensional data on a wide range of health outcomes and their determinants, including those related to lung, heart, allergy, and infectious diseases—creating a critical need to train biostatisticians and data scientists with multidisciplinary skills to properly analyze such data. Also evident is the need for a diversified workforce of biostatisticians and data scientists well-versed in public health principles to properly tackle the acute and growing challenges of health disparities. To fill this critical gap, the Summer Training Institute in Biostatistics and Data Science at Columbia (SIBDS@Columbia) proposes an innovative curriculum in quantitative skills anchored on data immersion related to research challenges in studies of heart and lung diseases, as well as infectious diseases, to train the next generation of adept biostatisticians and data scientists poised to address these pressing challenges. 

Daniel Malinsky 

1K25ES034064-01 funded by The National Institute of Environmental Health Sciences (Role: PI) 
Award: 08/16/2022-05/31/2027 

“Flexible Causal Inference Methods for Estimating Longitudinal Effects of Air Pollution on Chronic Lung Disease” 

Chronic lung diseases are among the top contributors to mortality in the United States and have previously been linked to air pollution. The proposed research will leverage advances in machine learning to develop novel statistical methods for the analysis of complex existing data on air pollution and lung disease progression over time. By combining his expertise in causal inference and machine learning with newly obtained training in air pollution science and environmental determinants of disease, Dr. Malinsky will advance the state of environmental data science and establish himself as an independent investigator in the intersection of biostatistics, machine learning, epidemiology, and environmental science. 

Yifei Sun 

1R21HL156228-01A1 funded by the National Heart, Lung and Blood Institute (Role: PI) 
Award: 01/03/2022-12/31/2023 

“Dynamic and Personalized Prediction of Complex Cardiovascular Events” 

The objective of this project is to develop novel statistical learning methods for predicting complex time-to-event outcomes such as recurrent events. The proposed methodology will have broad applicability; in particular, we aim to address the gap in predicting cardiovascular disease (CVD) recurrence by analyzing the data from the NHLBI Pooled Cohorts Study. The proposed research has the potential to advance dynamic and personalized risk prediction and to facilitate more effective prevention and treatment strategies for CVD recurrence. 

Wenpin Hou 

4R00HG011468-03 funded by the National Human Geonome Research Institute (Role: PI) 
Award:07/01/22-06/30/25 

“Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape” 

DNA methylation, an epigenetic modification that can modulate gene expression, has been shown to carry inheritable information capable of shaping phenotypes and causing many complex human diseases. In this project, leveraging the emerging technologies of spatial transcriptomics, we will identify disease-specific epigenomic signatures by developing computational and statistical methods to infer the high-resolution tissuespatial DNA methylation landscape that cannot be measured by current experimental technologies. The outcome of this project can lead to the development of new epigenetic targeted therapy and drugs for various diseases that will help the well-being of the broad community. 

Continuing Grants

Iuliana Ionita-Laza & Ying Wei 

NIH/NIA (RF1AG072272) “Multi-omics approaches for gene discovery in Alzheimer's Disease” 2021-2024 (Roles: MPIs Ionita-Laza, Wei) 

Min Qian & Ian McKeague 

NSF (DMS- 2112938) “Post-Selection Inference for Survival Outcomes in Precision Medicine” 2021-2024 (Roles: MPIs Qian, McKeague) 

Ying Kuen (Ken) Cheung 

NIH/NHLBI (R01HL153642) “Breaking up Prolonged Sedentary Behavior to Improve Cardiometabolic Health: An Adaptive Dose-Finding Study” 2021-2026 (Role: Multi-PI, MPIs Diaz, Cheung) 

Kiros Berhane 

NIH/FIC (U2RTW012123) “Advancing Public Health Research in Eastern Africa through Data Science Training” 2021-2026 (APHREA-DST) (Role: Contact PI / MPIs: Berhane, Bekele, Weke)   

Melissa Begg 

NIH/NHLBI (R25 HL096260), “BEST-DP: Biostatistics & Epidemiology Summer Training Diversity Program,” 2009-2024 (Role: Contact PI) 

Qixuan Chen 

NIH/NIEHS (R21ES029668), “Bayesian exposure-response analysis for immunoassays data with measurement errors” 2019-2023 (Role:PI ) 

Ying Kuen (Ken) Cheung 

NIH/NIMH (R01 MH109496), “Novel Methods for Evaluation and Implementation of Behavioral Intervention Technologies for Depression,” 2016-2022 (Role: PI) 

Jeff Goldsmith 

NIH/NINDS (R01 NS097423), “Functional data analytics for kinematic assessments of motor control,” 2016-2023 (Role: PI) 

Iuliana Ionita-Laza 

NIH/NHGRI (R21HG012345) “Quantitative disease risk scores for common diseases, with applications to eMERGE”  2021-2023 (Role: PI) 

NIH/NIMH (R01MH095797), “Novel Statistical methods for DNA Sequencing Data, and applications to Autism,” 2012-2022 (Role: PI) 

Shing Lee 

Funded by SWOG/Hope Foundation “Analysis and visualization of adverse events and patient reported outcomes that reflect overall treatment toxicity Burden” 2020-2022 (Role: PI)  

Ian McKeague 

NIH/NIA (R01AG062401) “Inferential methods for functional data from wearable devices” 2019-2024 (Role:PI) 

Caleb Miles 

NIH/NCATS  (KL2 TR001874), “Institutional Career Development Core: Clinical and Translational Science Award,” 2020-2022 (Role: Career Awardee) 

R. Todd Ogden 

NIH/NIBIB (R01 EB024526), “Advance Modeling Techniques for Brain Imaging Data with PET” 2017-2022 (Role: PI) 

Linda Valeri 

NIH/NIMH (K01MH118477), “Statistical methods for the assessment of social engagement in psychosis using digital technologies” 2018-2023 (Role: PI) 

Shuang Wang 

NIH/NLM (R01LM013061), “Big Data Methods for Comprehensive Similarity based Risk Prediction”, 2019-2024 (Role: Contact PI / MPIs: Wang, Weng, Kiryluk) 

Yuanjia Wang 

NIH/NIMH (R01MH123487) “Machine Learning Methods for Optimizing Individualized Treatment Strategies for Precision Psychiatry” 2021-2026 (Role: PI) 

NIH/NINDS (R01 NS073671), “Statistical Methods for early disease prediction and treatment stragety estimation using biomarker signature,” 2011-2022 (Role: PI)  

NIH/NIMH (R21MH117458) “Integrative Learning to Combine Evidence for Personalized Treatment Strategies” 2018-2021 (Role: PI) 

NIH/NIGM (R01GM124104) “Efficient Statistical Learning Methods for Personalized Medicine Using Large Scale Biomedical”2018-2022 (Role: Multi-PI / MPIs: Zeng, Wang) 

Ying Wei 

NIH/NHGRI (R01 HG008980), “Develop Quantile Analysis Tools for Sequencing and EQTL Studies,” 2016-2022 (Role: PI) 

NSF DMS1953527, “Conditional Quantile Random Forest with Biomedical and Biological Applications,” 2020-2023 (Role: PI)