The Herbert and Florence Irving Institute for Cancer Dynamics will continue its seminar series on the topic of mathematical sciences underpinning cancer research during the 2022-2023 academic year. The monthly seminars take place on the third Wednesday of the month, 2:00-3:00 PM EST. The presentations are open to the Columbia community (in person and online) and to researchers outside Columbia (via Zoom).
On Wednesday, October 19th (2:00-3:00 PM, EST), IICD welcomes Jingyi Jessica Li, PhD, Associate Professor in Statistics, UCLA. Seminar hosted by Simon Tavaré, FRS.
Title: A Unified Framework for Realistic in Silico Data Generation and Statistical Model Inference in Single-Cell and Spatial Omics
Abstract: In the single-cell and spatial omics field, computational challenges include method benchmarking, data interpretation, and in silico data generation. To address these challenges, we propose an all-in-one statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real datasets. Furthermore, using a unified probabilistic model for single-cell and spatial omics data, scDesgin3 can infer biologically meaningful parameters, assess the quality of cell clusters and trajectories, and generate in silico negative and positive controls for benchmarking computational tools.
Bio: Jingyi Jessica Li is an Associate Professor in the Department of Statistics (primary) and the Departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at UCLA and a Harvard Radcliffe Fellow in the year 2022-23. Before joining UCLA in 2013, Jessica obtained Ph.D. from UC Berkeley, where she worked with Profs. Peter J. Bickel and Haiyan Huang, and B.S. (summa cum laude) from Tsinghua University, China. At UCLA, Jessica leads the group “Junction of Statistics and Biology,” which comprises students from interdisciplinary backgrounds. On the statistical methodology side, her research interests include association measures, asymmetric classification, p-value-free false discovery rate control, and high-dimensional variable selection. On the biomedical application side, her research interests include bulk and single-cell omics, comparative genomics, and information flow in the central dogma. Jessica is the recipient of the Alfred P. Sloan Research Fellowship (2018), the Johnson & Johnson WiSTEM2D Math Scholar Award (2018), the NSF CAREER Award (2019), the MIT Technology Review 35 Innovators Under 35 China (2020), and the Radcliffe Fellowship (2022).