Janosch Ortmann

Background

About Janosch Ortmann

I analyse decision making processes under uncertainty, using machine learning, probability theory and optimisation. My current research includes applications in precision medicine, humanitarian logistics, the design of transport networks and real options. A few projects are summarised below: Precision cancer medicine To improve patient outcomes and health spending, I am developing models to understand how a patient's genetic code relates to the success of specific cancer treatments. This work is done in collaboration with the labs of Benjamin Haibe-Kains and Anna Goldenberg, using mouse models and Gaussian Process Regression to predict tumour response to therapy. Improved Processing of Neo-natal MRIs MRI images of newborn babies represent a challenge for medical professionals, as their brains are much less developed into distinct areas and image quality is usually lower due to babies moving more in the MRI machine. In collaboration with the Canadian Neonatal Brain Platform, who have a large collection of neonatal brain images, I am developing new machine learning methods to process images and mathematical/statistical tools to analyse outputs. Enhanced Decision-Making in Humanitarian Logistics Humanitarian aid is often delivered under conditions of extreme uncertainty and complexity. I am developing new methods at the interface of machine learning (unsupervised clustering) and operations research (stochastic optimisation) to provide decision makers with better and more structured information. In my free time I lead the data work for Data for Good in Montréal, a not for profit organisation providing free data consulting to charities.