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Machine learning for genetics and health: key challenges and an ongoing search for solutions.

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If you have a question about this talk, please contact Lorena Qendro.

Abstract: Genomic and medical data are available at unprecedented scales due to developments in high throughput sequencing, imaging technologies, as well as better practices in aggregating electronic health care records. 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—is challenging. In this talk, I will survey current work in biomedical research areas with a focus on genetics, imaging, and clinical data. I will discuss my own ongoing work to address statistical challenges arising from both data collection and data analysis, with an emphasis on pitfalls and open questions.

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Bio: I recently joined the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge as a Departmental Early Career Fellow in the Accelerate Programme for Scientific Discovery.

Previously, I was a Member in the School of Mathematics at the Institute for Advanced Study and I attended the semester long program in deep learning at the Statistical and Applied Mathematical Sciences Institute, where I was graciously hosted by David Dunson in the Duke Statistical Science Department. I received my PhD in Computational Biology at Princeton University, under the mentorship of Barbara E. Engelhardt. My PhD research focused on the effect of experimental design in single cell gene expression studies and on method development for structured, high-dimensional medical and genomic data. I did my undergraduate studies in Mathematics at MIT .

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