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Aaron Bernstein

First Name
Aaron
Last Name
Bernstein
Photo
Aaron Bernstein
Category
Research Interest

Machine learning-driven algorithms, Breast cancer 

Degrees

B.S., Pennsylvania State University
MPhil, Epidemiology, University of Cambridge

Student's Research

Aaron Bernstein is passionate about oncology research. Throughout his undergraduate study at The Pennsylvania State University, he made a point of exploring the full spectrum of biomedical research, from basic gene regulation work to clinical studies of chemotherapeutic toxicity. While Aaron began in wet-bench molecular biology research, intending to work as close to the fundamental mechanisms of cancer as possible, he ultimately found that he preferred the more computational approaches of bioinformatics and biostatistics.

Aaron earned an MPhil in Epidemiology at University of Cambridge.  Under the supervision of Dr. Paul Pharoah and Dr. Serena Nik-Zainal, he completed a bioinformatics thesis on the association between germline risk-conferring variants and somatic mutational signatures in breast cancer. As both an NIH OxCam Scholar and a Gates Cambridge Scholar, Aaron will pursue PhD research on the development of machine learning-driven algorithms for identification and evaluation of breast cancer histology slides, under the supervision of Dr. Pharoah, as well as Dr. Montserrat Garcia-Closas and Dr. Jonas Almeida at the NCI.

Aaron’s ultimate goal is to become a physician-scientist with a specialty in oncology and a research focus on the application of machine-learning to patient genetics and histology. A PhD through the NIH OxCam program will aid Aaron in forming collaborations with international leaders in these topics, promoting the integration of his research directly into the clinic and helping inform patient treatment globally.

Institute Center Mentor
Mentors

Dr. Montserrat Garcia-Closas (NCI),
Dr. Jonas Almeida (NCI) and 
Prof. Paul Pharoah (Cambridge)

Homepage Description
As an NIH OxCam and Gates Cambridge Scholar, Aaron will pursue PhD research on the development of machine learning-driven algorithms for identification and evaluation of breast cancer histology slides.
Entry Year
Thesis Pending
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