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.
Past Directors
Thank you to our former Directors and coordinator, Dr. Andrea Baccarelli, Dr. Gary Miller, Abby Welbourn, and Fernando Luque. 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 |
| Balliu, Brunilda | Statistical/computational methods for high-dimensional repeated & longitudinal data to uncover genetic, molecular, and environmental drivers of complex traits. | UCLA | 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 |
| Burris, Heather | Perinatal epidemiology of social & environmental determinants and interventions to reduce inequities (ECHO, GeoBirth, NICU postpartum care). | UPenn | 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 | UT Health 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 | UC Berkeley | Data science |
| Duarte, Julio | Genomics of heart failure and preemptive pharmacogenetic testing, with emphasis on underserved populations. | University of Florida | Omics |
| Duncan, Dustin | Social epidemiology, spatial epidemiology, environmental epidemiology, health equity | Columbia University | Omics |
| Feng, Jean | Interpretability and reliability of machine learning methods for biomedical applications | UCSF | Data science |
| Fryer, John D. | Molecular mechanisms of Alzheimer’s genetic risk and microglial/immune pathways; links with sepsis—mouse & in-vitro models. | Mayo Clinic, Phoenix | Omics |
| 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 |
| Hernandez, Diana | Social and environmental determinants of health | Columbia University | Omics |
| Hou, Wenpin | Statistical/ML methods for single-cell & spatial genomics; gene-regulatory network modeling and GPT applications to genomics. | Columbia | Data Science |
| 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 | UNC | 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 |
| Joshi, Trupti | Translational bioinformatics and multi-omics integration using ML/HPC for precision medicine, agriculture, and genomic epidemiology. | University of Missouri | Data Science |
| Kiryluk, Krzysztof | Genetics of kidney disease and precision medicine for glomerular disorders using large cohorts and informatics. | Columbia | Omics |
| Knowles, David | Machine learning for genomics to model transcriptomic variation, splicing, and gene–environment effects in large datasets. | Columbia | Data Science |
| Landry, Markita | Nanomaterial probes for neuromodulator imaging and nanoparticle platforms for targeted biomolecule delivery (incl. plant gene editing). | Berkeley | 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 |
| Mangul, Serghei | Reproducible, open computational genomics for translational research with emphasis on software usability and data sharing. | University of Southern California | 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 |
| Mitrofanova, Antonina | Algorithms to decode genomic/transcriptomic/epigenomic drivers of cancer progression and therapy response for patient-specific care. | Rutgers | Data Science |
| 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 |
| Patel, Chirag | Computational methods integrating EMR, exposome, and genomic data to map phenomes and gene–environment effects. | Harvard | 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 |
| Raphael, Ben | Graph/ML/optimization algorithms for cancer genomics—evolution, pathway/network analysis, and structural variation (TCGA/ICGC). | Princeton | Data Science |
| Re, Diane | Genomics and epigenomics of human chronic diseases | Columbia University | Omics |
| Reinhardt, R. Lee | Mucosal T-cell/ILC regulation of type-2 inflammation in infection, allergy, and lung immunity, including microgravity effects. | University of Colorado | Omics |
| Roede, James | Epigenomics and neurotoxicity | University of Colorado | Omics |
| Rosa, Maria Jose | Environmental epidemiology of children’s respiratory health focusing on early-life metals/air pollution and mitochondrial mechanisms. | Mt. Sinai, Icahn School of Medicine | Omics |
| Rose, Sherri | Non-parametric machine learning for causal inference and prediction | UCSF | 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 | CUNY | Data science |
| Sen, Pei | Data-driven infectious-disease modeling for surveillance, forecasting, and control using mobility, climate, and health records. | Columbia | 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 |
| Shen, Yike | Computational precision environmental health integrating exposures and multi-omics for biomarker discovery. | University of Texas at Arlington | Data Science |
| Shields, Alexandra | Clinical integration of genomic technologies | MGH & Harvard University | Omics |
| Shrubsole, Martha J. | Role of microbiome on gastrointestinal cancers | Vanderbilt University | Omics |
| Sofer, Tamar | [no specific expertise yet — but known for statistical/computational methods] | Harvard | Data Science |
| (Lasky)-Su, Jessica | Data science, population sciences, and biostatistics | Harvard University | Data science |
| Sussel, Lori | Transcriptional networks governing pancreatic islet development and β-cell identity/function in diabetes. | University of Colorado | Omics |
| Tatonetti, Nicholas | Translational bioinformatics, machine learning, observational data mining, genetic networks and network analysis | Columbia University | Data science |
| Uhlemann, Anne-Catrin | Clinical/molecular epidemiology of bacterial infections, especially staphylococcal and transplant-related infections. | Columbia | Omics |
| Velmeshev, Dmitry | Single-cell & spatial genomics to map human brain development and neurodevelopmental disorders. | Duke | Omics |
| 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 | UPenn | Data science |
| Wang, Shuang | Methods for gene-gene interaction in linkage analysis | Columbia University | Data science |
| Ward-Caviness, Cavin | Computational genomics and statistical/population genetics for high-dimensional omics & environmental health. | University of North Carolina | Data Science |
| Zhang, Hao (Helen) | Transdisciplinary research in principles of data science | University of Arizona | Data science |