Jill Rubinstein, MD, Ph.D.
Computational Oncology studies that integrate clinical, genomic, and imaging data to explore the dynamics of tumor heterogeneity and evolution in response to treatment.
Tumors are continually evolving collections of cells, characterized by a dynamic interplay among heterogeneous sub-clonal populations that expand and contract under innate and imposed selective pressures. My research couples deep learning imaging techniques with high-resolution molecular assays and matched clinical information to analyze tumors through a framework of evolution. We study the impact of treatment on the dynamics of the tumor ecosystem to elucidate resistance mechanisms and identify potential targets for intervention.
Education and experience
Education
Yale College
B.A., Architecture
1994-1999
Stockholm University
M.S., Computer and Systems Sciences
2002-2004
Yale University
M.S., Computational Biology & Bioinformatics
Ph.D., Computational Biology & Bioinformatics
M.D.
2004-2012
Experience
Yale-New Haven Hospital
General Surgery Resident
2012-2018
Memorial Sloan Kettering Cancer Center
Complex Surgical Oncology Fellow
2018-2020
Hartford Healthcare
Surgical Oncologist
2020-present
University of Connecticut
Assistant Professor of Surgery
2021-present
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Featured ArticleMay 04, 2022Putting machine learning to work for cancer patients
A research team at JAX is boosting the speed and accuracy of tumor sample image analysis, for multiple cancer types, offering the potential for more targeted treatment direction without additional testing.