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Alaina Shreves

First Name
Alaina
Last Name
Shreves
Photo
Alaina 2
Research Interest

Chronic disease epidemiology, Machine learning, Wearable sensors

Scholar Type
Degrees

B.S. Neuroscience & Public Health,
The College of William and Mary, 2018
M.S. Epidemiology, Harvard T.H. Chan School of Public Health, 2022

Student's Research

Alaina graduated from the College of William and Mary with a BS in Neuroscience and Public Health. As an undergraduate, she participated in the Amgen Scholars Program at the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH), where she studied the life course epidemiology of disorders identified through the newborn heel prick test.

She then worked as a postbaccalaureate fellow at the National Cancer Institute (NCI) under the supervision of Dr. Robert Hoover, gaining experience in study management through the development of the Connect for Cancer Prevention cohort.

Alaina earned an MS in Epidemiology and Biostatistics from the Harvard T.H. Chan School of Public Health. As a graduate research assistant with Dr. Lorelei Mucci, she investigated quality-of-life outcomes and sleep among prostate cancer survivors in the Health Professionals Follow-up Study.

She is now pursuing a DPhil at the University of Oxford and the National Cancer Institute. Her research focuses on physical activity and chronic disease prevention, leveraging wearable data from large population cohorts. She is co-supervised by Professor Aiden Doherty (Oxford Population Health), Professor Ruth Travis (Oxford Cancer Epidemiology Unit), Dr. Adam Lewandowski (Oxford Population Health), and Dr. Charles Matthews (NCI Metabolic Epidemiology Branch).

Institute Center Mentor
Mentors

Dr. Charles Matthews (NCI), Prof. Aiden Doherty (Oxford), and Prof. Ruth Travis (Oxford)

Homepage Description
As an NIH Oxford Scholar, Alaina will investigate what types of physical activity are associated with lower cancer incidence. She will use machine learning methods to quantify accelerometer-measured activity for cancer risk models with U.K. Biobank data. She will also implement two-sample Mendelian Randomization methods to explore potential causal effects between physical activity and cancer.
Entry Year
Thesis Pending
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