ELLIS Life / NCT Data Science Seminar: Jean-Philippe Vert

Machine Learning for Single Cell Omics

Jean-Philippe Vert, MINES ParisTech & Google Brain, Paris
November 17, 11:00 AM (CET)

Watch the video here


In this talk I will describe several machine learning-based methods to analyze single cell omics data, which provide a rich characterization of individual cells within a heterogeneous population. I will focus on: 1) methods to transform raw single-cell transcriptomic data into low-dimensional vector representation of cells that capture biological variability and similarity among cells, 2) methods to infer gene regulatory networks from single-cell transcriptomic data, and 3) methods to integrate multimodal single-cell data, in particular, to “translate” an observed modality such as the transcriptome of a cell into another unobserved modality, such as chromatin accessibility.


Jean-Philippe Vert is a research scientist at Google Brain in Paris and adjunct research professor at PSL Mines ParisTech’s Centre for Computational Biology. Prior to joining Google in 2018, he worked as a postdoc in computational biology at Kyoto University (2001-2002), research professor and founding director of the Centre for Computational Biology at Mines ParisTech (2003-2018), team leader at the Curie Institute in Paris on computational biology of cancer (2008-2018), Miller visiting professor at UC Berkeley (2015-2016), and research professor at the department of mathematics of Ecole normale superieure in Paris (2016-2018). He graduated from Ecole Polytechnique (1995), Corps des Mines (1998), and holds a PhD in mathematics from Paris 6 University (2001). His research interest concerns the development of statistical and machine learning methods, particularly to model complex, high-dimensional and structured data, with an application focus on computational biology, genomics and precision medicine. His recent contributions include new methods to embed structured data such as strings, graphs or permutations to vector spaces, regularization techniques to learn from limited amounts of data, and computationally efficient techniques for pattern detection and feature selection. He is also working on several medical applications in cancer research, including quantifying and modeling cancer heterogeneity, predicting response to therapy, and modeling the genome and epigenome of cancer cells at the single-cell level.