Objective: Cultivating research collaboration, joint grants and connecting the UM researchers to the right organisations. Supports Owkin expansion of our presence in North America and facilitates collaborations with PIs at UM. A great introduction to what Owkin does to UM.

Introduction Owkin & Scientific Overview of the Sessions — Patrick Sin-Chan, Partnerships Manager – Owkin
Session 1: Methodology and Data Science
Learning From Others Without Sacrificing Privacy: Application of Federated Machine Learning to Mobile Health Data
Presenter: Ambuj Tewari, Associate Professor, Statistics
Privacy Preserving Federated Learning Platform: from Design to Deployment in Real World Use Cases
Presenter: Camille Marini
Accelerating Machine Learning with Multi-Armed Bandit
Barzan Mozafari, Associate Professor, Computer Science and Engineering
Siloed Federated Learning for Multi-Centric Histopathology Datasets
Presenter: Mathieu Andreux
20 mins Panel Discussion (MIDAS Moderator- Kayvan Najarian, Professor, Computational Medicine and Bioinformatics)
Session 2: Biotech/medical
Covid-19 Severity Analysis with CT Scans and Machine Learning
Presenter: Simon Jégou
Linking Single-cell Molecular States with Phenotypes Using Machine Learning
Presenter: Josh Welch, Assistant Professor, Computational Medicine and Bioinformatics
HE2RNA: a Deep Learning Model to Predict RNA-Seq Expression of Tumors from Whole Slide Images
Presenter: Alberto Romagnoni
Using Large-scale Pharmacogenomic Databases to Predict Drug Effectiveness
Presenter: Johann Gagnon-Bartsch, Assistant Professor, Statistics
20 mins Panel discussion (Owkin Moderator: Patrick Sin-Chan)