Program Directors and Mentors
Directors
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.
Diane Berengere Ré, PhD
Dr. Ré is a neuroscientist focused on investigating environmental risk factors and mechanisms for neurodegenerative diseases. The overarching goal of Dr. Ré's research is to make an impact in therapy and prevention of amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases through innovative and rigorously designed mechanistic and biomarker studies. Since she started her faculty appointment, she has developed a novel line of animal- and human model-based experimental research linking environmental exposures and in particular metals and pesticides to the late-onset paralytic disorder ALS. Dr. Ré's work is at the forefront of complex and poorly understood gene-environment (GxE) interactions in the etiology of ALS and, more recently, Parkinson's disease.
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.
Jeanette Stingone, PhD
Dr. Jeanette Stingone, is an environmental epidemiologist with a focus on perinatal and pediatric health. She conducts research that couples data science techniques with epidemiologic methods to investigate how prenatal and early-life environmental exposures affect health and development throughout childhood and beyond. Currently, she is investigating how machine learning approaches can be used to uncover the combinations of multiple environmental exposures that contribute to disease and disability in children including birth defects, adverse neurodevelopment and early puberty. Dr. Stingone also has a strong interest in the use of collective science initiatives to advance public health research, and works to develop methods and approaches for data harmonization across diverse studies of environmental health.
Linda Valeri, PhD
Dr. Linda Valeri, received her PhD in Biostatistics from Harvard University in 2013, where her dissertation focused on statistical methods for causal mediation analysis. Dr. Valeri is an expert in causal inference with a focus on statistical methods for causal mediation analysis, measurement error, and missing data. She is interested in translating statistical methods in public health to improve our understanding of mental health, environmental determinants of health, and health disparities.
Coordinator
The Career MODE Program is supported by administrators with project management experience with diverse consituencies.
Fernando Luque
Fernando Luque received his Bachelors from the University of Florida and his Masters from CUNY in Anthropology, where he studied the molecular ecology of S. cerevisiae. Prior to joining Columbia, Fernando worked with a fine art firm specializing in archaeometry and historical academic research for the authentication, conservation, and restoration of artifacts. At Columbia University, he previously served the Chair and faculty in the department of Environmental Health Sciences. In 2018, he helped spearhead a collaborative pediatric asthma study in Puerto Rico after Hurricane Maria, involving clinicians, public health students, and an environmental justice organization to address a critical public health challenge. In his current role, Fernando oversees the research development of early-stage investigators through the comprehensive training, networking, and mentorship supported by this NIH/NIGMS funded R25 grant.
Past Directors
Thank you to our former Directors and coordinator, Dr. Andrea Baccarelli, Dr. Gary Miller, and Abby Welbourn. Dr. Baccarelli moved on to serve as the Dean of the Faculty at the Harvard T.H. Chan School of Public Health. Dr. Miller is leading the Columbia Center for Innovative Exposomics and the NEXUS Network for Exposomics in the US. Abby Welbourn is the Director of Professional Research Programming and Strategic Initiatives at Harvard T.H. Chan School of Public Health.
Past and Current Mentors
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. Below is a brief but incomplete list of scientists that previously served or agreed to participate as Omics or Data mentors for this program.
| 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 | Harvard 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 | Metabolomic and multi-omics; translational research | NCATS/ NIH | 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 | Oregon State 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 |