Dr. Geoffrey Schiebinger, University of British Columbia
Abstract: New measurement technologies like single-cell RNA sequencing are bringing 'big data' to biology. One of the most exciting prospects associated with this new trove of data is the possibility of studying temporal processes, such as differentiation and development. In this talk, we introduce the basic elements of a mathematical theory to answer questions like How does a stem cell transform into a muscle cell, a skin cell, or a neuron? How can we reprogram a skin cell into a neuron? We model a developing population of cells with a curve in the space of probability distributions on a high-dimensional gene expression space. We design algorithms to recover these curves from samples at various time-points and we collaborate closely with experimentalists to test these ideas on real data.
Bio: Geoffrey Schiebinger received his PhD in Statistics from UC Berkeley, where he was supervised by Ben Recht and also worked with Martin Wainwright, Bin Yu and Aditya Guntuboyina. He did his postdoctoral studies with Eric Lander, Aviv Regev and Philippe Rigollet at the Broad Institute and the MIT Center for Statistics and Data Science. He is now an Assistant Professor of Mathematics and an Associate Member of Biomedical Engineering at the University of British Columbia. He has won numerous awards including the 2021 Maud Menten New Principal Investigator Prize in Genetics, a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and Best Contribution to the 2017 conference on Statistical Challenges in Single Cell Analysis.