2024 Grants Update
New Grants since December 2023
Kiros Berhane
1P20AG093975 funded by NATIONAL INSTITUTE OF AGING (Role: MPI, not contact)
Award: 09/2024 – 08/2027
"Climate and Health: Action and Research for Transformational Change (CHART)"
Climate change is and will remain the most significant threat to global public health; the need for robust scientific evidence to guide maximally effective and equitable action is paramount. To address this urgent need and build capacity in climate and health research, we have designed the Climate and Health: Action and Research for Transformational Change (CHART) Center. CHART’s mission and overarching aim is to launch a transformative and integrative framework for climate health research, from molecules to populations, that drives evidence-based impactful solutions.
Qixuan Chen
1R01ES035784 funded by NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES (Role: MPI, contact PI)
Award: 09/2024 – 06/2029
"Improving the analysis and use of contaminated immunoassays: from methods development to implementation"
The proposed research will develop new statistical methods for estimating concentrations of analytes measured in immunoassays, considering measurement error and contamination in the samples, and modeling exposure-outcome associations with adjustment for exposure measure uncertainty. The proposed new methods will be developed based on the indoor allergen measures collected using ELISAs and multiplex assays in the New York City Neighborhood Asthma and Allergy Study and will be used to assess the associations between concentrations of indoor allergens and asthma morbidity among asthmatic children. The methods and tools developed in this proposal will have a wide range of applications and can be used to improve lab analyses, which are crucial for diagnosis, treatment, and intervention decisions in many areas of medicine and public health.
Iuliana Ionita-Laza, Ying Wei
4R01AG072272 funded by NATIONAL INSTITUTE OF AGING (Roles: MPIs)
Award: 05/2021 – 04/2026
"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 datasets including genomics, transcriptomics and epigenomics in order to improve gene discovery. We will apply these methods to some of the largest genetic datasets in AD to identify new risk genes for AD.
Hyunkyu (Cue) Lee
1K99HG013546 funded by NATIONAL HUMAN GENOME RESEARCH INSTITUTE (Role: PI, postdoc)
Award: 09/2024 – 08/2026
"Multi-Trait Analysis in Large-Scale Biobank Datasets Linked to Electronic Health Records"
This research introduces a unique multi-trait model that leverages extensive electronic health records to bolster disease prediction and mapping. Addressing the constraints of current genomic studies enhances personalized medicine through optimized treatment strategies and deeper insights into the genetic relationships among complex human diseases. Furthermore, it promotes health improvement and disease prevention, reinforced by rigorous model validation using extensive data sets and expert analyses.
Zhezhen Jin
DMS2413834 funded by NATIONAL SCIENCE FOUNDATION (Role: PI)
Award: 07/2024 – 06/2027
"Collaborative Research: Nonparametric Learning in High-Dimensional Survival Analysis for causal inference and sequential decision making"
The proposal aims to study a unified framework for nonparametric learning of high dimensional (HD) survival data via data embeddings under various important settings including causal inference and sequential decision making.
Zhonghua Liu
1R01AG086379 funded by NATIONAL INSTITUTE OF AGING (Role: PI)
Award: 06/2024 – 02/2029
"Robust Mendelian Randomization Framework with Multi-Omics Data for Alzheimer's Disease and Related Dementias"
This project aims to develop and apply advanced causal inference methods and computational algorithms to empower the analysis of large-scale genetic and multi-omics data in multi-ethnic and historically underrepresented populations. These methods will help identify causal omics-level biomarkers for non-invasive and accurate early diagnosis of Alzheimer’s disease and related dementias in the preclinical phase, which might provide the best time window for early prevention and intervention.
Caleb Miles
1R01DA059824 funded by NATIONAL INSTITUTE ON DRUG ABUSE (Role: MPI, contact PI)
Award: 08/2024 – 05/2029
"Leveraging harmonized data to improve external validity and efficiency of clinical trials for treating opioid use disorder"
Trials for the treatment of psychiatric and substance use disorders can be difficult, time-consuming, and expensive to conduct, and partially as a consequence, have sample sizes that may be underpowered for: 1) detecting moderately sized average treatment effects (ATEs) that may nonetheless be important for health at the population level, and 2) learning optimal individualized treatment rules (i.e., rules that match treatments to individuals based on demographic and clinical characteristics that optimize outcomes of interest), which are the cornerstone of personalized medicine. Data fusion is a relatively new and increasingly popular domain of data science that combines data from multiple studies to improve statistical power and answer questions that cannot be addressed by a single study alone. Our overall objective is to develop methods that harness follow-up and other post-exposure measurements as potential proxies for outcomes that are systematically missing by study to deliver more precise estimates of causal treatment effects and facilitating the learning of treatment rules that maximize benefits and reduce harms.
Linda Valeri
1R21ES036704 funded by NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES (Role: PI)
Award: 09/2024 – 08/2026
"Machine Learning remedies to unmeasured confounding biases in environmental mixture studies"
This project responds to the research need of more rigorous evaluation of unmeasured confounding in epidemiological analyses of environmental mixtures. We will develop advanced models for causal inference based on Bayesian probabilistic sensitivity analyses and negative control exposures approaches along with computationally efficient and user-friendly R packages to evaluate the impact of unmeasured confounding when causal inferences on multiple continuous exposures are sought. The proposed approaches will enable causal inference on joint exposure effects to inform policy for environmental exposures and child development.
Yuanjia Wang, Todd Ogden
1T32MH135856 funded by NATIONAL INSTITUTE OF MENTAL HEALTH (Roles: MPIs)
Award: 07/2024 – 06/2029
"Research Training Program in Mental Health Biostatistics and Data Science"
Mental health disorders are a major cause of disability that significantly reduces the quality of life. As new technologies emerge for assessing mental health disorders and as research design becomes increasingly complex, the Department of Biostatistics at Columbia University, in partnership with Columbia’s Department of Psychiatry and New York State Psychiatric Institute, has developed an innovative program to train predoctoral scholars in biostatistical methods, machine learning, data science, and interdisciplinary research in order to meet the emerging challenges brought by cutting edge technologies and big data. The program will build on the Department’s strengths and decades of success in rigorous training of biostatisticians to prepare trainees as next-generation leaders in mental health biostatistics and data science (MH-BDS).
Ying Wei, Iuliana Ionita-Laza, Molei Liu
1R01AG087496 funded by NATIONAL INSTITUTE OF AGING (Roles: MPIs)
Award: 06/2024 – 02/2029
"Statistical Framework for Unraveling Age-Dependent Genetic Landscape of Alzheimer's Disease and Related Dementias: Harnessing Large-Scale EHR and DNA-Biobank Integration"
This project aims to build a more comprehensive understanding of the genetic architecture of Alzheimer’s Disease and related dementias, taking into account the complexities of age-related changes and diverse phenotypes. By leveraging large-scale biobanks, electronic medical records, and innovative machine learning techniques, we hope to uncover crucial insights that could lead to better understanding of disease progression, improved prevention strategies and novel therapeutic targets, representing a significant advancement in dementia research.
Continuing Grants
Kiros Berhane and Christine Mauro
NIH/NHLBI: 5R25HL161786 (Roles: MPIs)
2022 - 2026
"Summer Institute for Training in Biostatistics and Data Science at Columbia (SIBDS@Columbia)"
Kiros Berhane
NIH/NIEHS/NIH Ofc of Director/ John E. Fogarty International Center for Advanced Study in the Health Sciences: 5U2RTW012123 (Role: MPI, contact PI)
2021 - 2026
"Advancing Public Health Research in Eastern Africa through Data Science Training (APHREA-DST)"
NIH/NCI/NIEHS/ John E. Fogarty International Center for Advanced Study in the Health Sciences: 5U2RTW010125 (Role: MPI, contact PI)
2015 – 2027
"Geohealth Hub for Research and Training in eastern Africa - U.S."
Ken Cheung
NIH/NHLBI: 5R01HL153642 (Role: MPI, not contact)
2021-2026
"Breaking up Prolonged Sedentary Behavior to Improve Cardiometabolic Health: An Adaptive Dose-Finding Study"
Jeff Goldsmith
NIH/NHLBI: 5R25HL096260 (Role: MPI, contact PI)
2009-2025
"BEST-DP: Biostatistics & Epidemiology Summer Training Diversity Program"
Wenpin Hou
NIH/NHGRI: 5R00HG011468 (Role: PI)
2022-2025
"Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape"
NIH/NIGMS: 5R35GM150887 (Role: PI)
2023-2028
"Methods for inferring and analyzing gene regulatory networks using single-cell multiomics and spatial genomics data"
Iuliana Ionita-Laza
NIH/NIMH: 5R01MH095797
2012 – 2025
"Novel Statistical methods for DNA Sequencing Data, and applications to Autism"
NIH/NHGRI: 1R21HG012345 (Role: PI)
2021 – 2024
"Quantitative disease risk scores for common diseases, with application to eMERGE"
Seonjoo Lee
NIH/NIA: R01AG062578 (Role: PI)
2020-2025
"Statistical method for neural mechanism mediating and moderating cognitive system in Alzheimer's disease and aging research"
NIH/NIMH: R01MH124106 (Role: PI)
2020 – 2025
"A Data Science Framework for Empirically Evaluating and Deriving Reproducible and Transferrable RDoC Constructs in Youth"
Daniel Malinsky
NIH/NIEHS: 5K25ES034064 (Role: PI)
2022 – 2027
"Flexible causal inference methods for estimating longitudinal effects of air pollution on chronic lung disease"
Ian Mckeague
NIH/NIA: 5R01AG062401 (Role: PI)
2019 – 2025
"Inferential methods for functional data from wearable devices"
Min Qian and Ian McKeague
NSF: DMS-2112938
2021 – 2025
"Post-selection inference for survival outcomes in precision medicine"
Linda Valeri
NIH/NIA: 5R01AG077518 (Role: PI)
2023 – 2028
"Bayesian Statistical Learning for Robust and Generalizable Causal Inferences in Alzheimer Disease and Related Disorders Research"
Yuanjia Wang
NIH/NIGMS: 5R01GM124104 (Role: MPI, not contact)
2018 – 2027
"Statistical and Machine Learning Methods to Improve Dynamic Treatment Regimens Estimation Using Real World Data"
NIH/NINDS: 5R01NS073671 (Role: PI)
2011 – 2027
"Statistical Methods for early disease prediction and treatment strategy estimation using biomarker signatures"
NIH/NIMH: 5R01MH123487
2021 – 2026
"Machine Learning Methods for Optimizing Individualized Treatment Strategies for Precision Psychiatry"