Program Directors and Mentors


The Career MODE Program (R25GM143298) is led by a robust and experienced set of researchers and academic leaders with complementary, integrated expertise in omics and data science.

Andrea Baccarelli, MD, PhD

Dr. Andrea Baccarelli is the Leon Hess Professor and Director of the Columbia Mailman School’s Precision Medicine Initiative, part of the University-wide precision medicine initiative. Dr. Baccarelli has both wet-lab and data science expertise on multiple types of omics as pathways linking risk factors to human disease, including DNA methylation, histone modifications, and non-coding RNA/transcriptomics. He has a strong record of innovation and transformative impact. He has published >470 peer-reviewed articles (h-index = 88) and was recently featured in the Web of Science list of the highest cited, most influential investigators of the past decade. In 2020, he was elected to the National Academy of Medicine.

Iuliana Ionita-Laza, PhD

Dr. Iuliana Ionita-Laza, holds a PhD in computer science and has specific expertise in development and application of statistical methods for genomic and multi-omic research. Dr. Ionita-Laza’s research in data science emphasizes development of efficient statistical and computational methods for human genetics and statistical genomics. Dr. Ionita-Laza leads the Genomics@Columbia Program, an initiative to bring together an interdisciplinary group of investigators from multiple departments across Columbia University with research expertise in statistical/computational genomics, other omics, computational biology, and biomedical informatics. 

Gary Miller, PhD

Dr. Gary Miller has extensive expertise in metabolomics and exposomics. He is a worldwide leader in exposome research, an emerging field that uses omics technology to investigate exposure to exogenous chemicals and their metabolites. Dr. Miller’s laboratory studies metabolomics and exposomics in population-based and experimental research. Dr. Miller serves as Vice Dean of Research and Strategy Development in the Mailman School of Public Health.


The Career MODE Program is supported by administrators with project management experience with diverse consituencies.

Fernando Luque

Fernando Luque, Project Coordinator. Fernando earned a BA from the University of Florida and MA from CUNY in Anthropology where he examined yeast evolutionary ecology and domestication. At Columbia University, he previously served the Chair and faculty in the department of Environmental Health Sciences. In 2018, he helped implement a pediatric asthma pilot study post-hurricane Irma and Maria linking clinical professionals and public health students with an environmental justice organization in Puerto Rico. In this role, he's eager to help our trainees and mentors enhance the research education objectives supported by the R25 grant.

Abby Welbourn

Abby Welbourn, Director of Special Projects. Abby has over a decade of experience in academic programming and project and grant management at leading universities. Since 2017, her work at Columbia has led to creating and overseeing the Columbia SHARP (Skills for Health and Research Professionals) Program alongside SHARP Program Director Dr. Andrea Baccarelli.



Career MODE Mentors have established research portfolios in -omics and data science, bringing together a wealth of experience from across the nation at different institutions.

Name Specific expertise Institution General expertise 
Aguet, Francois Functional impact of human genetic variation and cell type-specific regulation of gene expression Broad Institute  Omics
Alvarez, Jessica State-of-the-art metabolomics with aspects of nutrition research  Emory University Omics
Baccarelli, Andrea Epigenomics as target of environmental exposures Columbia University Omics + data science
Bastarache, Lisa Research in bioinformatics, pharmacogenomics, systems biology, and translational informatics Vanderbilt University Data science
Berhane, Kiros Data science in longitudinal studies and multilevel modeling Columbia University Data science
Binder, Alexandra Analysis of high-dimensional, omic data to understand molecular mechanisms shaping cancer University of Hawai'i Data science
Breton, Carrie Genetic and epigenetic mechanisms of disease in early life University of Southern California Omics + data science
Brunst, Kelly Epigenomics and mitochondriomics/mitochondrial function in childrens environmental health University of Cincinnati Omics
Cabrera, Robert Genetic engineering, functional genomics, live-cell imaging, high-throughput screening on birth defects Baylor University Omics
Cardenas, Andres Epigenetics and epigenomics University of California, Berkeley Omics + data science
Chatzi, Lida Nutrition and obesogenic exposures during pregnancy on long-term maternal and child health University of Southern California Omics
Chen, Mengjie Statistical methods to address the challenges of high-throughput technologies University of Chicago Data science
Chiuzan, Codruta Machine learning Columbia University Data science
Chung, Wendy Genomic and precision medicine for obesity, type 2 diabetes, congenital heart disease Columbia University Omics + data science
Colicino, Elena Data sciences approaches to genomics and epigenomics Mount Sinai Data science
Conneely, Karen Statistical methods for genetic & epigenetic association studies Emory University Data science
Conti, David Hierarchical modeling and Bayes model averaging as a framework for multiple genetic polymorphisms University of Southern California Data science
Coull, Brent Semiparametric regression modeling, machine learning Harvard University Data science
Cox, Nancy Large-scale integration of genomic with other omics data Vanderbilt University Data science
David, Maude Microbiota and their genomic characteristics related to health Oregon State University Omics
De Oliveira Otto, Marcia Nutritional and Cardiometabolic Disease Epidemiology in high-risk populations The University of Texas Health Science Center at Houston. Omics + data science
Dolinoy, Dana Role of nutritional and environmental factors on the epigenome University of Michigan Omics
Dominici, Francesca Data science, Bayesian methods, and causal inference Harvard University Data science
Dudoit, Sandrine Statistical methods and software for analysis of biomedical and genomic data University of California, Berkeley Data science
Feng, Jean Interpretability and reliability of machine learning methods for biomedical applications University of California, San Francisco Data science
Fukuyama, Julia Development of statistical and computational methods to understand biological data Indiana University Data science
Garmire, Lana Integrative omics/clinic data analysis University of Michigan Data science
Genkinger, Jeanine Impact of molecular pathways and related biomarkers on cancer risk and progression Columbia University Omics
Goldsmith, Jeff Functional data analysis by developing methods for understanding patterns in large, complex datasets  Columbia University Data science
Greally, John Cellular and transcriptional regulatory changes in aging Albert Einstein  Omics
Harari, Homero Exposomics Mount Sinai Omics
Hoyo, Cathrine Epigenomics in common chronic diseases North Carolina State  Data science
Huerta-Sanchez, Emilia Theoretical, computational, and statistical models Brown University Data science
Ideraabdullah, Folami Mechanisms of epigenome modulation and genetic differences that contribute to variability University of North Carolina Omics
Im, Hae Kyung Quantitative and computational methods on genomics University of Chicago Data science
Ionita-Laza, Iuliana Development of statistical and computational methods for high-dimensional genetic and functional genomics data Columbia University Data science
Jackson, Chandra Metabolome and exposome NIEHS Omics
Jones, Dean Metabolomics and exposomics Emory University Omics
Lappalainen, Tuuli Functional genetic variation in human populations and its contribution to traits and diseases New York Genome Center Omics + data science
Lemos, Bernardo Epigenetics and epigenomics Harvard University Omics
Lin, Xihong Statistical and computational methods to analyze big data from genome, exposome, and phenome Harvard University Data science
Liu, Chunyu Genome sequencing research in population studies Boston University Data science
Lovinsky, Stephanie Exposome and lung function Columbia University Omics
Manichaikul, Ani Statistical genetics and genetic epidemiology University of Virginia Data science
Marsit, Carmen DNA methylation and miRNA as key epigenetic mechanisms Emory University Omics + data science
Mathe, Ewy Multi-omic analysis of genomics, epigenomics, nucleotide variants, and metabolic patterns Ohio State University Data science
Maunakea, Alika High-throughput, genome-wide technologies that survey DNA methylation and histone modifications University of Hawai'i Omics
Miller, Gary Exposomics and metabolomics Columbia University Omics
Mukherjee, Bhramar Statistical methods for analysis of electronic health records and studies of gene-environment interaction University of Michigan Data science
Navas-Acien, Ana Genomic and epigenomic variants, and effective interventions for reducing involuntary exposures Columbia University Omics
Niedziewicki, Megan Exposome, metabolomics, and health effects  Mount Sinai Omics + data science
Palacios, Julia Development of statistical methods to understand complex stochastic phenomena Stanford University Data science
Patel, Chirag Computational & bioinformatics for high-throughput data Harvard University  Data science
Pearson, Brandon Neurotoxicology, epigenomics, cell biology, stress, and diverse model organisms Columbia University Omics
Perzanowski, Matthew* Exposome and lung function Columbia University Omics
Pollitt, Krystal Mass spectrometry techniques (ICP-MS, LC-MS, GC-MS) and application in epidemiological studies Yale University Omics+ data science
Puga, Alvaro Gene-environment interactions during embryogenesis  University of Cincinnati Omics
Raj, Towfique Computational approaches to understand genetic factors driving neurodegenerative diseases  Mount Sinai Omics + data science
Re, Diane Genomics and epigenomics of human chronic diseases Columbia University Omics
Roede, James Epigenomics and neurotoxicity University of Colorado Omics
Rosa, Maria Exposome and lung function Mount Sinai Omics
Rose, Sherri Non-parametric machine learning for causal inference and prediction University of California, San Francisco Data science
Salas, Lucas Epigenetic mechanisms and cancer outcomes Dartmouth University Omics + data science
Schooling, Mary Evolutionary biology, cohort studies, endocrine disruptors, and Mendelian randomization City University of New York Data science
Sharpton, Thomas Microbiota and their genomic characteristics (i.e., the microbiome) related to health Oregon State University Omics
Shen, Yufeng Genomic and computational approaches for human biology and diseases Columbia University Omics + data science
Shields, Alexandra Clinical integration of genomic technologies MGH and Harvard University Omics
Shrubsole, Martha Role of microbiome on gastrointestinal cancers Vanderbilt University Omics
Sofer, Tamar Statistical and computing methods Harvard University Data science
(Lasky)-Su, Jessica Data science, population sciences, and biostatistics Harvard University Data science
Tatonetti, Nicholas Translational bioinformatics, machine learning, observational data mining, genetic networks and network analysis Columbia University Data science
Walker, Cheryl Gene-environment interactions and their role in cancer, fibroids, and non-alcoholic fatty liver disease Baylor University Omics
Walker, Douglas Metabolomics and exposomics Mount Sinai Omics
Wang, Kai* Genomics and bioinformatics methods to improve diagnosis, treatment, and prognosis of rare diseases University of Pennsylvania Data science
Wang, Shuang Methods for gene-gene interaction in linkage analysis  Columbia University Data science
Ward-Cavinness, Cavin High-dimensional omics data to study molecular mechanisms linking environmental exposures and adverse health  Environmental Protection Agency Data science
Zhang, Hao (Helen) Transdisciplinary research in principles of data science University of Arizona Data science