Allison C. Goff, Ph.D., is a scientist and data professional with expertise at the intersection of genetics, neuroscience, and data science. She earned her doctorate through the Georgetown University/National Institutes of Health (NIH) partnership program, where her research focused on transcriptional signatures of steroid hormone responses and microRNA networks underlying reproductive mood disorders. During this time, she developed and implemented reproducible analytic pipelines in R and Python for large-scale sequencing data, incorporating dimensionality reduction, clustering, and network analysis to identify disease-relevant molecular signatures. Her work has been published in leading journals including Molecular Psychiatry and Translational Psychiatry.
Dr. Goff’s professional expertise extends across statistical modeling, machine learning, and data visualization, with a particular emphasis on building transparent workflows for complex, high-dimensional datasets. She has mentored undergraduate, graduate, and medical trainees in computational methods, and currently teaches graduate-level courses in data analytics and visualization at the Massachusetts General Hospital Institute of Health Professions.
Currently, Dr. Goff leads computer vision and AI projects that apply machine learning and statistical modeling to behavioral and biomedical data. Her roles involve guiding technical development and analytical methods, determining appropriate statistical frameworks, and overseeing the design of data visualizations and output reporting.
Through her combined experience in genomics, bioinformatics, and machine learning, Dr. Goff brings a unique perspective to modern data science challenges. Her work is dedicated to bridging biological complexity with computational innovation to accelerate discovery and improve human health outcomes.
- BS, Neuroscience, Univ. of Wisconsin - Madison
- MA, Bioethics, New York University
- PhD, Genetics, Georgetown University
Data Science & Analytics: Statistical modeling, regression analysis, clustering, dimensionality reduction, network analysis, and machine learning for high-dimensional data.
Programming & Tools: R, Python, Unix/Linux, Jupyter, HPC environments.
Bioinformatics & Genomics: RNA-seq, microRNA-seq, next-generation sequencing analysis, pathway analysis, data integration with external genomic databases.
Data Visualization & Communication: Development of reproducible analytic pipelines, creation of data-driven visualizations, and documentation for transparent research.
Teaching & Mentorship: Instructor in healthcare data analytics and bioinformatics; experience mentoring students and junior researchers in computational methods and data analysis.