QBS Faculty Mentors
QBS faculty are recognized leaders in their fields of research, with the additional benefit of being skilled teachers and mentors.
Dr. Whitfield's work focuses on is Precision Medicine in systemic sclerosis (SSc). His laboratory is identifying gene expression biomarkers that subset SSc patients, predict clinical endpoints, and assess response to therapy. The lab is focused on understanding the pathophysiology of the disease, analyzing molecular data from SSc clinical trials, perform network analyses on SSc genomic data, and using this information for drug repositioning efforts.
The Ackerman laboratory conducts interdisciplinary research at the interface of biomedical and engineering sciences: developing high throughput tools to evaluate the antibody response in disease states ranging from infection to cancer in order to aid in therapeutic antibody and vaccine design and development. We aim to understand the protective mechanism of antibodies using approaches grounded in fundamental engineering principles utilizing protein evolution, molecular biology, and mathematical modeling.
Through the collection and management of data from family studies, Dr. Amos seeks to understand human health and dfisease through the development and application of statistical methods for identifying genetic risk factors and is interested in the design of clinical studies to identify predictors of cancer development and progression.
Dr. Andrew is a molecular epidemiologist with research interests in genetic and environmental factor interactions and their impact on cancer risk or prognosis. Recent projects include a large-scale investigation of SNPs and exposure factors that influence bladder cancer, and a multi-level analysis of lung tumor molecular markers in relation to exposure factors.
The goal of the Computational Structural Biology Laboratory (CSBL) is to develop integrated computational-experimental approaches to the structural and functional understanding of and control over the molecular machinery of the cell.
Dr. Barry is the Project Director for the Vitamin D/Calcium Polyp Prevention Study, a multi-centered randomized controlled trial of Vitamin D and/or Calcium for the prevention of colorectal adenomas. Her research focuses on cancer chemoprevention and the mechanism of action of chemopreventative agents.
The Cheng lab focuses on applying computational methods to understand the molecular biology, immunology, and pharmacology of cancer. We take an integrative approach by combining high-throughput genomic data with clinical and phenotypic information in our analyses.
Dr. Christensen's research is focused on combining advances in molecular biology, genomics and bioinformatics with the powerful techniques of modern epidemiology and statistics to characterize epigenetic states in human health and disease.
Dr. Demidenko has broad interests in theoretical and applied statistics, applied mathematics, and biomathematics. He has published papers on mixed models, sample size and power calculations, asymptotic hypothesis tests comparison, optimization in statistics, image reconstruction, inverse problems, financial mathematics, partial differential equations, statistical analysis of image and shapes, and tumor response to treatment.
The objective of Dr. Diamond's research is to use bioengineering and simulation to understand how the brain works. Specific interests include biomedical imaging, functional neuroimaging, physiological modeling, neurovascular coupling and magnetic nanoparticle imaging.
Nearly all eukaryotic cells possess circadian oscillators that govern growth and metabolism as a function of time of day. Through collection and modeling of 'omics and data the Dunlap lab seeks to understand how the clock works and how it controls the life of a cell.
Dr. Frost's research focuses on the development of novel bioinformatics and biostatistical methods for high-dimensional data analysis. Applied research areas include gene set testing, gene-gene and gene-environment interactions, biomedical ontologies and cancer genomics. Statistical topics of interest include penalized regression, principal component analysis, random matrix theory and optimization.
Dr. Gerber's lab develops high-throughput mass spectrometry technology and bioinformatics methods for the analysis of proteins and their post-translational modifications in complex biological processes such as cell division and tumorigenesis.
Dr. Gilbert-Diamond's research lab focuses on gene-environment interactions related to child growth and health including in utero exposures to toxic metals and vitamin D as well as early life exposures to electronic media and unhealthy diets.
Olga has substantial experience in cancer studies of epidemiological and genetic risk factors and in modeling lung cancer natural history, screening and population lung cancer trends. Using a carcinogenesis modeling approach, her group was able to derive risk profile-specific estimates of lifetime lung cancer risk in individuals with different smoking histories. Through modeling they have estimated potential lung cancer mortality reduction due to CT screening, in populations stratified according to their lung cancer risk profile. Yet another area of her interest is genetics of scleroderma.
The focus of Dr. Grigoryan's research is to understand the principles underlying natural protein structure and function towards the design of novel proteins for targeted applications. Specific problems of interest include designing proteins to specifically disrupt/potentiate cellular protein-protein interactions, designing protein self-assembly as well as co-assembly of proteins with nanomaterials, and the development of novel computational tools to enable more quantitative/accurate design of detailed molecular properties.
Dr. Gui's is developing cutting-edge biostatistical methods for the analysis of high-dimensional omics data. Recent work has focused on the detection of gene-gene interactions in genome-wide association data.
Saeed Hassanpour is developing computational methods and tools for extracting and organizing biomedical knowledge from unstructured data and text. The lab's data mining interests cover a wide range of data from clinical notes, patient medical history, radiology and pathology reports, medical imaging repositories, biomedical literature, the Web, and social media contents. The lab's knowledge extraction frameworks aim at distilling meaning from heterogeneous, complex and massive amounts of biomedical data and text, improving the understanding of medical conditions and health care, and having a practical impact on clinical care.
The Hill lab focuses on determining the identity of pathogens infecting the lung, bloodstream, or urine using the molecules present on a patient's breath, or directly sniffing the fluid, respectively. The researchers use a variety of tools in combination, including: advanced mass spectrometry, microbiology, molecular biology, advanced statistics, and also, basic electronics.
Dr. Hoen's research focus is on the development of the microbiome in infants and children, and the associations between environmental and dietary exposures, the microbiome, and risk for infectious and other diseases.
The Karagas lab research focuses on understanding the pathogenesis of human disease through population-based approaches, with prevention as the ultimate objective.
Dr. Li is interested in developing (statistical) modeling tools in the field of molecular epidemiology to analyze human microbiome and epigenetic changes in order to identify microbes and DNA methylation that mediate disease-leading causal pathways in children's health research. He is also interested in joint modeling of quality of life and survival in palliative care research. The models tools he uses include lasso-type regularization, SCAD regularization, mediation analysis, structural equation modeling, GEE, mixed models, Cox model, etc.
Dr. MacKenzie uses statistics to help medical researchers from a vast spectrum of disciplines and specialties. Over a 180 peer-reviewed publications have resulted from his collaborations. He has expertise in survival analysis.
The Miller Lab focuses on mechanisms of drug resistance and the implementation of molecular therapeutics for breast cancer. We integrate data from cellular and mouse models of breast cancer and early-phase clinical trials to understand how cancer cells respond and adapt to drugs, and ways to abrogate drug resistance.
James's research interests have centered on social network analysis, causal inference, multivariate-hierarchical modeling, and the design and analysis of medical device clinical trials. He has developed novel statistical methods, often involving novel use of Bayesian statistics, to solve important methodological and applied problems in health policy and health services research, including the evaluation of treatments and quality of care in multiple areas of medicine.
Dr. Onega's major interests are in cancer control and geoinformatics, with a focus on understanding health care access, delivery, and effectiveness across health care systems and populations. Her team integrates methods from geosciences, informatics, and epidemiology to address questions ranging from technology diffusion to cancer screening, population health, and risk assessment.
Dr. Passarelli is a cancer epidemiologist focusing on genetic and environmental risk factors for common cancers, including the molecular epidemiology of colorectal cancer and adenomas.
Dr. Rees is a physician epidemiologist and the director of the New Hampshire State Cancer Registry. Her research interests include disease surveillance; epidemiology of cancer; multiple malignancies; epidemiology of infectious diseases; environmental health; randomized control trial methodology.
Dr. Romano's research lab explores the influence of maternal exposure to environmental endocrine disrupting chemicals (EDCs) during pregnancy on early life growth, childhood development, and pregnancy complications, with a focus on EDCs commonly found in consumer products in the United States, including phthalates, perfluoroalkyl substances (PFAS), parabens, and flame retardants.
Dr. Sanchez's laboratory studies the pathways that protect genomic integrity and in particular the study of the Chk1 protein kinase, a target of inhibitors currently in clinical trials for cancer. DNA damage has been found to be an early event in pre-malignant lesions and can be caused by deregulation of cancer-driving genes called oncogenes. This finding led the Sanchez laboratory to develop and use genetically engineered mouse models to investigate the role that Chk1 pathways play in the early stages of cancer development. Genomic information can allow investigators to devise precision therapies that target molecular lesions specific to a patient's cancer. Dr. Sanchez's laboratory also works on applying the concept of synthetic lethality by combining chemical screens with isogenic platforms for the identification of drugs and drug targets for the treatment of cancer.
Emily's methodologic research interest is in the analysis of intensively collected data such as those obtained through mobile technology-based assessments, particularly in the area of mental health. She also works on longitudinal and correlated data modeling and structural equation modeling.
The goal of the Environmental Diseases Genomics Laboratory is to understand the role of environmental agents such as dioxin in determining susceptibility to disease. A central focus of the lab is the use of genomics to understand obesity and the fetal basis of adult diseases (FeBAD).
Dr. Tosteson's research addresses clinical and health policy issues in cancer and musculoskeletal diseases through decision-analytic modeling and economic evaluation. Her methodological interests include decision-analytic modeling, comparative effectiveness research, and statistical methods for diagnostic technology assessment.
The Tosteson lab conducts statistical research in the areas of noncompliance in surgical clinical trials, covariate measurement error for nonlinear regression models and statistical methods for image based research.
Dr. Zhaxybayeva's research interests are to understand how microbes change over time by mining data sets containing thousands of genomes and terabases of environmental DNA (metagenomes) in order to find new ways to characterize microbial communities, and track down genomic signatures of microbial adaptations.