Another ML professor job talk on Thursday. There might be some time for students or postdocs interested in meeting our visitor, so let Linda know if you are interested.

---------- Forwarded message ---------- From: Pierre McKenzie mckenzie@iro.umontreal.ca Date: 2017-02-14 14:23 GMT-05:00 Subject: Colloque DIRO, jeudi 16 février, Ted Meeds (Vrije U, Amsterdam) To: seminaires@iro.umontreal.ca Cc: veronique.sage@umontreal.ca

*Modeling science with machine learning: from inference in simulation-based models to understanding cancer.*

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* Ted Meeds *

Vrije Universiteit Amsterdam

*Jeudi 16 février, 15:30-16:30*, *Salle 6214*, Pavillon André-Aisenstadt

Université de Montréal, 2920 Chemin de la Tour

Café avant 15:00-15:30

* Cette conférence sera donnée en anglais.*

*Résumé:* In this talk I will explore the role that machine learning models, algorithms, and inference procedures play in several areas of science. Likelihood-free inference, or approximate Bayesian computation (ABC) is a framework for performing Bayesian inference in a wide variety of simulation-based sciences, from population genetics to computational psychology. I will first describe recent algorithmic improvements in ABC inference which are due in part from viewing inference from a machine learning perspective. I will then describe ongoing work using deep neural networks to model heterogeneous pan-cancer data and challenging tumour segmentation tasks whose long term goal is targeted immunotherapy treatment. Finally I will touch upon recent work on compressing neural networks using simple machine learning techniques, resulting in sparse and quantized parameters which, when applied to scientific domains, provides parsimonious models that are simple enough to be understood by experts.