Date and LocationFeb 22, 2019 - 10:00am to 11:00am
The spatial organization of the genome represents an important epigenetic regulator of gene expression, and alterations thereof are associated with various diseases. A recent break-through in genomics makes it possible to perform perturbation experiments at a very large scale. This motivates the development of a causal inference framework that is based on observational and interventional data. We characterize the causal relationships that are identifiable and present the first provably consistent algorithm for learning a causal network from such data. I will then link gene expression with the 3D genome organization. In particular, we will discuss approaches for integrating different data modalities and analyze alterations in the spatial organization of the genome via autoencoders. We end by a theoretical analysis of autoencoders linking overparameterization to memorization. Collectively, this talk will highlight the symbiosis between genomics and AI and show how biology can lead to new theorems, which in turn can guide biological experiments.
Caroline Uhler joined the MIT faculty in 2015 and is currently the Henry L. and Grace Doherty associate professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. She holds an MSc in mathematics, a BSc in biology, and an MEd from the University of Zurich. She obtained her PhD in statistics from the University of California, Berkeley in 2011. She then spent a semester as a research fellow at the Simons Institute at UC Berkeley, short postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. She is a Sloan Research Fellow and an elected member of the International Statistical Institute, and she received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Her research focuses on statistics and machine learning, in particular on graphical models and causal inference, and applications to genomics.
This is a CBE Special Seminar.