Data Management & Statistics Core
The Data Management & Statistics Core of the Stanford Alzheimer’s Disease Research Center (ADRC), is responsible for managing large volumes of clinical, neuropsychological, genetic, imaging, tissue biomarker, and associated research data. The Core provides anonymous data to the National Alzheimer Coordinating Center and to qualified researchers at Stanford and other universities.
Core faculty work closely with other ADRC Cores in support of Center goals. They offer biostatistical consultation, support “big data” research using ADRC data, promote research on biostatistical methods tailored to ADRC data, provide statistical consultation to ADRC participating investigators, and offer biostatistical training for junior investigators.
Lu Tian, ScD
Associate Professor of Biomedical Data Science
Data Management & Statistics Core Leader
Lu Tian is an Associate Professor of the Department of Biomedical Data Science at Stanford University. He received his Sc.D. in Biostatistics from Harvard University. Dr. Tian has rich experience in conducting statistical methodological research, planning large epidemiological studies, running data management for randomized clinical trials and conducting applied data analysis. His current research interest is in developing statistical methods in personalized medicine, causal inference, survival analysis and high throughput data analysis.
Zihuai He, PhD
Assistant Professor of Neurology and Medicine (BMIR)
Data Management & Statistics Core Associate Leader
Dr. He received his PhD from the University of Michigan in 2016. Following a postdoctoral training in biostatistics at Columbia University, he joined Stanford University as an assistant professor of neurology and of medicine in 2018. His research is concentrated in the area of statistical genetics and integrative analysis of omics data, with the aim of developing novel statistical and computational methodologies for the identification and interpretation of complex biological pathways involved in human diseases, particularly neurological disorders. His methodology interest includes high-dimensional data analysis, correlated (longitudinal, familial) data analysis and machine learning algorithms.
Serena Yeung, PhD
Assistant Professor of Biomedical Data Science
Dr. Serena Yeung is an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. Her research focus is on developing artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. She has extensive expertise in deep learning and computer vision, and has developed computer vision algorithms for analyzing diverse types of visual data ranging from video capture of human behavior, to medical images and cell microscopy images.
Dr. Yeung leads the Medical AI and Computer Vision Lab at Stanford. She is affiliated with the Stanford Artificial Intelligence Laboratory, the Clinical Excellence Research Center, the Center for Artificial Intelligence in Medicine & Imaging, the Center for Human-Centered Artificial Intelligence, and Bio-X. She also serves on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence.
Janet Hwang, MS
Janet Hwang received her bachelor's degree from the mathematics department of Tamkang University in Taiwan and her MS degree in computer science from the University of Houston in Texas. Prior to coming to Stanford in 2017, she worked for startup companies in San Jose, helping to design and implement database management systems and Web 2.0 technologies.
Amy Lin, MPH
Database System Analyst
Amy Lin received her BA in Biological Sciences from Cornell University and MPH in Epidemiology and Biostatistics from the University of Southern California. Following clinical research positions at USC and UCSF, she joined Stanford University as a Data Analyst in 2018.