Jingshen Wang

Professor Jingshen Wang emphasizes the importance of robust statistical methods in advancing public health research. As an Assistant Professor in Biostatistics at the University of California, Berkeley School of Public Health, she brings a wealth of expertise in handling complex data sets. Her research primarily focuses on causal inference, missing data, and longitudinal studies, areas crucial for deriving meaningful insights from observational data. Dr. Wang is dedicated to developing innovative statistical methodologies that enhance the quality and reliability of health research. Her work is instrumental in addressing the challenges posed by incomplete or imperfect data, ensuring that conclusions drawn from such data are both valid and actionable. Her contributions to the field have been recognized through prestigious awards, including the NSF Career Award, which underscores her commitment to advancing statistical science. In addition to her research, Dr. Wang is passionate about mentoring the next generation of biostatisticians. Her doctoral candidate, Waverly Wei, received the Student Paper Award, a testament to Dr. Wang's dedication to fostering academic excellence and innovation among her students. Through her teaching and mentorship, she inspires students to explore new frontiers in biostatistics and public health. Dr. Wang's work not only advances theoretical understanding but also has practical implications for public health policy and practice. By improving methods for analyzing observational data, she contributes to more informed decision-making processes in healthcare. Her efforts ensure that statistical analyses are not just technically sound but also relevant to real-world health challenges. Her commitment to excellence in research and education makes her a valuable asset to the University of California, Berkeley, and the broader public health community. Dr. Wang continues to push the boundaries of biostatistics, striving to make a lasting impact on public health through her innovative approaches to data analysis.