BIO
My research program focuses on the molecular- and pharmaco-epidemiology of breast cancer incidence and survival. I have developed a large and interdisciplinary network of local, national, and international collaborators with whom I conduct population-based studies to answer pressing questions with relevance to both breast cancer survivors and to women at risk for breast cancer. Since joining ¶¶Òõ̽̽ in 2013, I have received research grants as principal investigator (PI) from the National Institutes of Health, Susan G. Komen for the Cure, The Mary Kay Foundation, and The Saint Baldrick’s Foundation. I was ¶¶Òõ̽̽ PI and co-investigator on R01 CA166825, which explored the genetic epidemiology of tamoxifen treatment failure. I was selected in 2017 as a Project Director for the Vermont Center on Behavior and Health COBRE to study the impact of behavioral risk factors for breast cancer in women at high risk. I am currently ¶¶Òõ̽̽ PI and coinvestigator on R01 LM013049, in which my research team is developing Monte Carlo and Bayesian methods for quantification and correction of systematic bias in epidemiologic studies. To date, I have authored over 60 research papers and letters, many of which appear in top-tier oncology or epidemiology journals, garnering an hindex of 22 and 1,791 citations (per Google Scholar). My research is often highlighted at national and international meetings, including platform talks at the San Antonio Breast Cancer Symposium, the Society for Epidemiologic Research, and the International Congress on Pharmacoepidemiology. The quality and impact of my work have been recognized by the NIH Loan Repayment Program, by my department (James E. Demueles Award for Dedication to Research Excellence), and by the Larner College of Medicine (Dean’s Rising Star New Investigator Award). In addition to these research activities, I am actively engaged in teaching, mentoring, and service.
Area(s) of expertise
Molecular epidemiology, Pharmacoepidemiology, Drug safety, Breast cancer, Oncology, Epidemiologic Methods, causal inference
Bio
My research program focuses on the molecular- and pharmaco-epidemiology of breast cancer incidence and survival. I have developed a large and interdisciplinary network of local, national, and international collaborators with whom I conduct population-based studies to answer pressing questions with relevance to both breast cancer survivors and to women at risk for breast cancer. Since joining ¶¶Òõ̽̽ in 2013, I have received research grants as principal investigator (PI) from the National Institutes of Health, Susan G. Komen for the Cure, The Mary Kay Foundation, and The Saint Baldrick’s Foundation. I was ¶¶Òõ̽̽ PI and co-investigator on R01 CA166825, which explored the genetic epidemiology of tamoxifen treatment failure. I was selected in 2017 as a Project Director for the Vermont Center on Behavior and Health COBRE to study the impact of behavioral risk factors for breast cancer in women at high risk. I am currently ¶¶Òõ̽̽ PI and coinvestigator on R01 LM013049, in which my research team is developing Monte Carlo and Bayesian methods for quantification and correction of systematic bias in epidemiologic studies. To date, I have authored over 60 research papers and letters, many of which appear in top-tier oncology or epidemiology journals, garnering an hindex of 22 and 1,791 citations (per Google Scholar). My research is often highlighted at national and international meetings, including platform talks at the San Antonio Breast Cancer Symposium, the Society for Epidemiologic Research, and the International Congress on Pharmacoepidemiology. The quality and impact of my work have been recognized by the NIH Loan Repayment Program, by my department (James E. Demueles Award for Dedication to Research Excellence), and by the Larner College of Medicine (Dean’s Rising Star New Investigator Award). In addition to these research activities, I am actively engaged in teaching, mentoring, and service.
Areas of Expertise
Molecular epidemiology, Pharmacoepidemiology, Drug safety, Breast cancer, Oncology, Epidemiologic Methods, causal inference