I am a machine learning research scientist at the NIMH where I develop statistical and machine learning methods. I completed my PhD in the Department of Biostatistics at Harvard where I was an NIH Graduate Fellowship recipient (NRSA: F31). I was advised by Dr. Giovanni Parmigiani and conducted statistics and machine learning research with a focus on the development of transfer learning methodologies for training predictive models when multiple training datasets are available (i.e., domain generalization and multi-source domain adaptation). I also collaborated closely with Dr. Rahul Mazumder at MIT. My graduate methods research involved tools from mixed integer and convex optimization. At the NIH I have worked on functional data analysis and causal inference methods. My subject area interests include neuroscience and chemical dependence. Please feel free to reach out to me at gloewinger@gmail.com.
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PhD in Biostatistics, 2022
Harvard University
AM in Biostatistics, 2019
Harvard University
BA in Neuroscience, 2012
Pitzer College
I grew up in Washington, DC and studied neuroscience at Pitzer College (of the Claremont Colleges). After graduating, I was fortunate to receive a Watson Fellowship to conduct research for one year in Peru, Brazil, Thailand and Vietnam on alternative treatments for chemical dependence. I subsequently spent a year in Nepal on a research Fulbright Fellowship on HIV risk and chemical dependence.
After returning from abroad, I received an NIH postbac research fellowship in the laboratory of Dr. David Lovinger. At the NIH I witnessed how biomedical sciences increasingly rely on and benefit from statistical methods research. This experience motivated me to pursue doctoral training in biostatistics.
My graduate research focused on developing machine learning algorithms that borrow information across different datasets to improve model generalizability. In addition to my advisor, Dr. Giovanni Parmigiani, I collaborated with Dr. Rahul Mazumder at MIT and Dr. Rajarshi Mukherjee at Harvard.
At the NIMH I develop machine learning and statistical methods. I also actively engage in collaborative statistical work with clinicians, neuroscientists, and mental health researchers.
In my free time, I train Brazilian Jiu Jitsu and other forms of grappling. I am also interested in chess, Vipassana meditation and studying languages.