My name is Angie Shen, and I am a Senior at Duke University majoring in Statistics. I’m passionate about Statistics because I am fascinated by how it derives insights from quantitative analysis and integrates mathematical theory, computing and narrative. Through different classes, research and special projects, I have been able to find my interest in two specific areas: applications in medicine and Bayesian Machine Learning methods. These topics will be the focus of my graduate school research, as I plan to apply for PhD programs in Biostatistics and Statistics.

I was first exposed to applying statistical analysis to problems in health care during my summer internship at a mental health non-profit, Center for Health and Wellbeing, in Belfast, UK. I surveyed 85 of the caregiver clientele on their mental health needs and analyzed the relationship between hours of care provided, mental health symptoms and treatment outcome. I started to appreciate how quantitative analysis can reveal important insights about patient needs and optimal treatment methods, and as a result, improves the effectiveness and efficiency of health care provision.

During Sophomore year, I started working with Biostatistician Dr. Ben Goldstein and Statistician Dr. Beka Steorts on precision medicine: improving health care outcomes by optimally utilizing patient information to provide personalized health care. We are working with clinicians from the Duke Hospital to develop a dynamic risk prediction model for patient deterioration with Electronic Health Records (EHR) data. This research project has made me realize that statisticians can make a tremendous difference in health care outcomes, which I used to think is solely the expertise of doctors. Quantitative analysis of clinical data can unveil individual health dynamics and prevent acute patient deterioration, which significantly improves the effectiveness and efficiency of health care delivery. I presented our work at JSM 2016 in Chicoago, Women in Machine Learning (WiML 2016) in Barcelona, and INFORMS Health Care Conference in Rotterdam, Netherlands. Going to conferences and talking to researchers have helped me grasp my research question in depth and inspired me to learn and consider a variety of different methods that I could use to improve my analysis.

Besides applications in health, I am also interested in Bayesian statistical methods and Machine Learning. I have taken a few graduate level classes on Bayesian methods and Machine Learning. I am currently working on my senior thesis with Dr. Alan Gelfand on exploring anisotropic covariance functions for point referenced data under Bayesian framework. I also did an independent study with Dr. Beka Steorts on Bayesian nonparametric models, specifically developing code for Dirichlet Process and Chinese Restaurant Process. At NIPS conference I attended many talks and workshops on cutting-edge machine learning research. I have been able to apply machine learning algorithms to real world data though projects at DataFest and computing classes. I hope to further work on these areas in graduate school.

Outside of research, I enjoy reading, writing short stories and dancing (Hiphop, Waacking and House). I have a Minor in Literature at Duke, and I’m particularly interested in feminist theory, postcolonial theory and East Asian literature and film. My published writings are collected in this post.

All inquiries are welcome at angieshen6 AT gmail.com. Thank you for reading.