Meet Our Doctors

Dr. Rabbee earned her BA degree in Computer Science and Economics from Wellesley College. After working as a software engineer for several years, she completed her MA degree in Statistics at the University of California at Berkeley. Dr. Rabbee then pursued management consulting and founded her own independent statistical consulting company. Dr. Rabbee next completed a PhD in Biostatistics at Harvard School of Public Health and an NSF/VIGRE post-doctoral fellowship at the University of California at Berkeley.

During her post-doctoral fellowship, Dr. Rabbee co-invented the highly-accurate RLMM classification algorithm for Affymetrix SNP arrays. After this invention and teaching duties at UC Berkeley, she went on to spend more than 20 years discovering and developing new methods for drug and molecular diagnostics research and development in the industry. Of note, Dr. Rabbee and colleagues received a patent for identifying a biomarker signature predicting therapeutic efficacy for a subgroup of patients in Ovarian cancer at Genentech, Inc., as well as two patents for contributing to building a successfully commercialized high-dimensional classifier for indeterminate cytopathology FNA samples in thyroid cancer to identify benign vs malignant cases at Veracyte, Inc. Dr. Rabbee has worked on large DSMB support at LabCorp for nine trials in a joint drug development agreement between multiple companies and CROs.

Dr. Rabbee continued to grow professionally by taking on new challenges and responsibilities by building leadership and strategic thinking abilities in the industry. She built the first Statistical Methodologies team in Neurology at Eisai, Inc. where her team built a pipeline for processing and deep learning from brain MRI scans of Alzheimer’s patients. Most recently, she built the department of Biometrics, including Biostatistics, Statistical Programming and Data Management at Alladapt Immunotherapeutics, Inc.

Dr. Rabbee is engaged in the larger statistical community in teaching, publishing and professional service. She has taught machine learning and other statistical courses to advanced undergraduate and master’s students at UC Berkeley as a Lecturer until 2022. She has solo authored “Biomarker Analysis in Clinical Trials Using R” with Chapman and Hall and is currently working on her second book, “Machine Learning in Clinical Trials Using R” with the same publisher. She is an associate editor for the Journal for Biopharmaceutical Statistics and a reviewer for the journal Therapeutic Innovation and Regulatory Science.

At the NIH Clinical Center, Dr. Rabbee specializes in Machine Learning and Data Science applications for scientific and medical research and oversees BCES collaborations within Clinical Center as well as outside of the Clinical Center.