Angel Huang is working with the Department of Child and Family Mental Health and Developmental Neurology to develop and evaluate machine learning models to improve the early diagnosis of autism using electronic health records and insurance claims data. She is also working on a project with the Duke Collaborative to Advance Clinical Health Equity (CACHE) to investigate therapeutic inertia and its contributing factors in patients with diabetes. She holds a PhD in neuroscience from the University of North Carolina at Chapel Hill and a MPA in public health from NYU. She is passionate about applying artificial intelligence and machine learning to facilitate our understanding of medical and healthcare problems and ultimately improve patient outcomes.  


Elliot Hill is working with the Biostatistics and Bioinformatics Department to develop AI systems that help clinicians monitor and diagnose neurodevelopmental disorders like ADHD and autism in adolescents. His projects involve developing medical concept embeddings using self-supervised learning, identifying patient subtypes using unsupervised learning, and building deep learning models for time-series prediction. He holds a BS in ecology and evolutionary biology and a MS in computational science from Tulane University. In his free time, he likes to rock climb and hike with his wife and dog.

Mengying Yan

Mengying Yan is a 4th-year PhD student in Biostatistics at Duke University. She received her MS in statistics from George Washington University and BS in mathematics and statistics from the University of Minnesota — Twin Cities. Ms. Yan is interested in using EHR data for clinical prediction models. Her research focuses on observability issues in EHR and how it impacts algorithmic bias.


Aditya Parekh is working with the Departments of Biostatistics & Bioinformatics and Cell Biology to develop scalable machine learning systems and software infrastructure to allow for the efficient analysis of large biological datasets. In addition, he works on several problems of interests to computational and cell biologists, including single-cell RNA-sequencing analysis and computational approaches for protein engineering. His goal is to make newly developed methods in computational biology more accessible, robust, and useful for bioinformaticians and biologists alike. He joined Duke after completing his MS at Carnegie Mellon in computational biology. Prior to that, he earned his BS in Biochemistry and Mathematics at Arizona State University, and looks forward to making the transition from sun devil to blue devil at Duke.

Aditya Parekh

Scott Sun

Scott Sun is a first-year PhD student in Biostatistics at Duke University. He received his Master of Biostatistics from Duke University and Bachelor of Science in Mathematics and Data Science from Rose-Hulman Institute of Technology. Scott is interested in missing data and class imbalance issues that are commonly observed in real-world data. His current research focuses on leveraging deep learning techniques to appropriately and effectively tackle these challenges and optimize the overall model performance.


Christine Shen, a third-year PhD student in the Statistical Science department, holds an MS in statistics from Duke and a BBA in Actuarial Science from the Chinese University of Hong Kong. Her research passion lies in developing inference tools, particularly through the use of Bayesian methods. With evolving interests, she looks forward to joining the AI Health community and applying statistical tools to tackle real-world problems.


Meet the AI Health Alumni Fellows