Machine Learning from Systems to Biology

7 Janeiro 2019, 14:30 • Catia Luisa Santana Calisto Pesquita

Title: Machine Learning from Systems to Biology


Presenter: Alcides Fonseca

Short Bio:

Alcides Fonseca is an Assistant Professor at FCUL and a researcher at LASIGE. Alcides holds a PhD from the University of Coimbra, where he taught as an Assistant and as an Invited Professor at ISCTEM, Mozambique. His research crosses the areas of Programming Languages, Parallel Systems and Machine Learning. One example of such research was the CMU|Portugal AEminium project, from which a new concurrent-by-default programming language was developed, targeting multicore and GPU processors.

http://alcidesfonseca.com


Abstract:

The talk will cover two types of work. The first half of the talk will cover how Machine Learning was applied to automate complex decisions that authors of parallel programs have to make in order to have faster programs. Two examples will be provided: CPU vs GPU decision and choosing the best dynamic granularity mechanism.

The second half will cover how to apply the same classification algorithms to a completely different scenario: predicting the pathogenicity of genetic variants. Despite using the same algorithms, the challenges are completely different: what is the ideal dataset to use, how to integrate this pipeline into commercial clinical diagnosis, how to convince doctors to trust these results.