SDS Welcomes Three New Faculty: Su Chen, Amanda Glazer, and Yuling Yao
Please join the Department of Statistics and Data Sciences in welcoming Su Chen, Amanda Glazer, and Yuling Yao to our faculty! Read more about each of these individuals’ extensive experience and interests below.
Su Chen, Associate Professor of Instruction, Statistics and Data Sciences and Director of SDS Experiential Learning
Su Chen has extensive experience developing and teaching courses that highlight experiential learning in statistics and data sciences. She is passionate about curriculum design, pedagogical innovation, and helping students achieve academic success. In addition to teaching, she is also involved in collaborative research in which she applies data science and machine learning techniques to fields such as educational psychology, nutrition and wellness, and the social sciences more broadly. Previously, Chen was an Assistant Teaching Professor in the Center for Transforming Data to Knowledge (D2K Lab) and former Director of the Data Science Minor Program at Rice University. She earned her Ph.D. in statistics at the University of Texas at Austin, advised by Professor Stephen Walker. Her doctoral research lies in the intersection of methodology, theory, and computation of Bayesian statistics and their applications to high dimensional data analysis.
Amanda Glazer, Assistant Professor, Statistics and Data Sciences
Amanda Glazer’s research focuses on developing causal inference and nonparametric methods, along with associated software and tools that address real scientific problems. While she has a wide range of interests, she is particularly drawn to problems that affect society, such as issues of discrimination and social justice. She also conducts research in sports analytics, having previously worked as a baseball operations analyst for the San Francisco Giants for four years. She is currently working on developing a new special topics course on sports analytics that we are excited to open to our undergraduate SDS Majors next Spring. Glazer earned her Ph.D. in statistics from the University of California, Berkeley and her B.A. in statistics and mathematics from Harvard.
Yuling Yao, Assistant Professor, Statistics and Data Sciences
Yuling Yao’s research interests lie in Bayesian computation, Bayesian modeling, machine learning, and causal inference. His previous work included applications modeling such phenomena as lead fallout in Paris, arsenic diffusion in South Asia, COVID-19 mortality in Bangladesh and galaxy clustering in the universe. He designs statistical and machine-learning methods, with a focus on model evaluation, aggregation, causal inference and prediction under misspecification. Some ongoing progresses are on cross-validation, stacking and hierarchical stacking and covariate imbalance. He develops algorithms and theories for fully Bayesian and approximate computations, including importance sampling, simulated tempering and annealing and multimodal MCMC sampling. His recent interest is in simulation-based and score-based methods for scientific computing, as well as the general framework of distribution aggregation flow. Yao was previously a Flatiron Research Fellow at the Flatiron Institute. He earned his Ph.D. in statistics from Columbia University.
