| Abstract |
Genomic data are available at unprecedented scales due to developments in high throughput sequencing and imaging technologies. In parallel, innovations in statistics and machine learning boast successful algorithms for a wide array of engineering applications. However, bridging these two worlds—the world of real, messy biological data, and that of algorithms and computation—remains challenging. In this talk, I will review recent attempts to extract interpretable representations in genomics and imaging studies. I will discuss pitfalls and formulate open questions with an emphasis on statistical challenges arising from both data collection and data analysis, with the final goal of building a foundation for and testing the limits of computational morphogenomics – the attempt to determine shape, form and trajectory from genomic information. |
| About the speaker |
Dr. Bianca Dumitrascu is currently a Departmental Early Career Fellow in the Accelerate Programme for Scientific Discovery at the Department of Computer Science and Technology in the University of Cambridge and earned a PhD in Quantitative and Computational Biology at Princeton University. Previously, Dr. Dumitrascu was a Member at the School of Mathematics at the Institute for Advanced Study in Princeton, USA and was a post-doctoral researcher in the Department of Statistics at Duke University. |
