![Joseph Chang](https://campuspress.yale.edu/fdstest/files/2022/12/CHANG-h-300x300.jpeg)
Joseph Chang is the James A. Attwood Professor of Statistics and Data Science and chairman of the Yale Statistics and Data Science Department. Before joining the Yale faculty in 1989, Chang did graduate studies in Mathematics, Operations Research, and Statistics at Stanford. He has held visiting positions in the University of California at Santa Cruz, the National Security Agency, and Pfizer Corporation Central Research. His research interests have included a number of areas in probability, statistics, and applications, including random walks, statistical process control, Bayesian inference, evolutionary tree reconstruction, statistical genetics, and autism research. One of the applications of his work is in illuminating understandings of evolutionary relationships among different species, clarifying capabilities and limitations of phylogenetic methods to reconstruct evolutionary histories using genetic sequences and other data. Chang has also used mathematical models to study shared ancestry among humans, quantifying how closely related we are to each other and showing how we likely share identical ancestors within the recent several thousand years. Chang collaborates with the Yale Child Study Center on research that provides insights into development among autistic children. Chang’s teaching has received recognition at Yale through the Lex Hixon Prize for Teaching Excellence in the Social Sciences and the William Clyde DeVane Medal.
What do you do with data science?
My research interests have included a number of areas in probability, statistics, and applications, including random walks, statistical process control, Bayesian inference, evolutionary tree reconstruction, statistical genetics, and autism research. One of the applications has been in illuminating understandings of evolutionary relationships among different species, clarifying capabilities and limitations of phylogenetic methods to reconstruct evolutionary histories using genetic sequences and other data. I have also used mathematical models to study shared ancestry among humans, quantifying how closely related we are to each other and showing how we likely share identical ancestors within the recent several thousand years. Recently, I worked on mathematical models to address problems related to the SARS-CoV-2 pandemic, developed in real time to support Yale’s response. I also have long standing collaborations with the Yale Child Study Center on autism research that involves a variety of types of data including parental reports, eye tracking, and fMRI.