Statistical Modeling and Data Analysis

Karene Chu serves as the Assistant Director of Education and Research Scientist at MIT's Institute for Data, Systems, and Society, where she has made significant contributions to digital learning initiatives. After receiving her Ph.D. in mathematics from the University of Toronto in 2012, she completed postdoctoral fellowships at both the University of Toronto/Fields Institute and MIT, specializing in knot theory and quantum invariants. In 2015, she transitioned to become a digital learning lab fellow at MIT, where she has since played a pivotal role in developing and managing the MicroMasters Program in Statistics and Data Science. Her educational impact includes co-authoring the MITx Calculus Series, which became a Top 10 edX course in 2016, and leading the development of a five-course series on differential equations. She is also a key instructor in MIT's Machine Learning with Python course alongside Regina Barzilay and Tommi Jaakkola. Her teaching excellence was first recognized at the University of Toronto, where she received a teaching award for her work in single and multi-variable calculus and linear algebra. As part of MIT's edX group, she collaborated with colleagues to earn the inaugural MITx Prize for Teaching and Learning in MOOCs, demonstrating her commitment to advancing digital education and making complex mathematical concepts accessible to learners worldwide.

Born in Switzerland in 1983, Caroline Uhler has risen to become a leading figure in statistical machine learning and computational biology. Currently serving as the Andrew (1956) and Erna Viterbi Professor of Engineering at MIT and Director of the Eric and Wendy Schmidt Center at the Broad Institute, her journey began with multiple degrees from the University of Zurich - a BSc in Biology, MSc in Mathematics, and MEd in High School Mathematics Education. Her path changed when she discovered algebraic statistics through Professor Bernd Sturmfels, leading her to pursue a Ph.D. in Statistics at UC Berkeley, which she completed in 2011. After postdoctoral positions at the University of Minnesota and ETH Zurich, followed by three years as an assistant professor at IST Austria, she joined MIT's faculty in 2015. Her groundbreaking research combines machine learning, statistics, and genomics, with particular focus on causal inference, representation learning, and gene regulation. Her exceptional contributions have earned her numerous prestigious honors, including the NIH New Innovator Award, Simons Investigator Award, NSF CAREER Award, Sloan Research Fellowship, and election to the International Statistical Institute. She is also a SIAM Fellow and IMS Fellow, while maintaining her passion for teaching and mentoring students at MIT. Most recently, her work has expanded to include developing machine learning methods for drug repurposing, including applications for COVID-19 treatment.

Stefanie Sabrina Jegelka's journey from a small German town to becoming a leading figure in machine learning began with winning a ThinkQuest competition for an educational website about butterflies. After earning her diploma in bioinformatics from the University of Tübingen in 2007, she completed her Ph.D. jointly at ETH Zurich and the Max Planck Institute for Intelligent Systems in 2012. Following postdoctoral research at UC Berkeley, she joined MIT's faculty in 2015, where she rose to become an X-Consortium Career Development Associate Professor. In 2024, she assumed the position of Alexander von Humboldt Professor at the Technical University of Munich while maintaining her association with MIT.