Chang (April) Shu
Dr. Chang Shu, also known as April Shu, is an Assistant Professor in the Center for Genetic Epidemiology and the Department of Population and Public Health Sciences at Keck School of Medicine of USC. April specializes in psychiatric genetics and epidemiology. Her work focuses on autism genetics, substance use epigenetics, and single-cell transcriptomics. She utilizes advanced statistical and machine learning tools to uncover novel genetic factors in mental and behavioral disorders.
April earned her Ph.D. in Mental Health focusing on psychiatric genetics and epigenetics in 2018 from Johns Hopkins Bloomberg School of Public Health under the mentorship of Drs. Brion Maher and Dani Fallin. During her time there, she also trained in statistical genetics and machine learning with Dr. Hongkai Ji and completed a concurrent master's degree in Biostatistics. Her postdoctoral training was conducted at Yale University with Dr. Ke Xu, followed by a position as an Associate Research Scientist in labs led by Drs. Wendy Chung and Yufeng Shen. She holds an additional master's degree in Epidemiology from Harvard School of Public Health, mentored by Drs. Edward Giovannucci and Benjamin Le Cook. April completed her undergraduate degree in Chemistry and Biochemistry at Tsinghua University in Beijing.
April's interdisciplinary training positions her uniquely at the intersection of genetics, epidemiology, and biostatistics, offering a comprehensive lens to explore complex mental health issues.
Master's Student in Biostatistics
Qi a second-year graduate student majoring in Biostatistics at USC. Her research focus is on utilizing machine learning models to investigate the severity and subtypes of autism. Recently, she completed a project titled "Modifiable Risk Factors for Coronary Heart Disease", where she conducted statistical analysis using Stata to explore the associations between exposures and diseases in cohort studies. Qi completed her undergraduate degree in Statistics at the University of Waterloo, during which she developed models to examine the relationships between systolic blood pressure and various variables. She also conducted research on variants of ARCH/GARCH models, contributing to the analysis and prediction of financial volatility.
Undergraduate Student in Computer Science
Preethi is a senior at Columbia University majoring in Computer Science on the Intelligent Systems track. She is currently working on perform and evaluate missing data imputation methods on parent-reported data and standardized surveys for children with autism. She simulates several data missingness patterns to investigate the imputation performance of various machine learning models including random forest, autoencoder, and more. Preethi is also co-mentored by Dr. Yufeng Shen.