Mathematical modelling in the machine learning era

Online workshop, December 9

In recent years machine learning and artificial intelligence have been on the rise and progress has been made on problems that previously have been tackled with more traditional mathematical and statistical modelling. Examples of this includes image segmentation, automatic control and medical diagnostics. During this workshop we will discuss the pros and cons of machine learning versus mathematical/statistical models. We will also look at how problem-specific features might affect the choice of method and lastly discuss how the two approaches can be combined within a united modelling framework.​

Screenshot of presentations on YouTube

All presentations on YouTube >>







​9:00–9:10 ​Bernt Wennberg (Chalmers and University of Gothenburg): Welcome & Introduction
​9:10–9:30  ​Philip Gerlee (Chalmers and University of Gothenburg): Mathematical modelling in the machine learning era
​9:30–10:15 ​Robert Feldt (Chalmers): Data-driven Interpretability – a hybrid between massive/ML and small/mathematical models?
​10:15–10:30  ​Break
​10:30–11:15 ​Hitesh Mistry (University of Manchester): Small Models for Big Data
​11:15–12:00 ​Aila Särkkä (Chalmers and University of Gothenburg): Spatial point process models for sweat glands observed with noise
​12:00–13:00 ​Lunch
​13:00–13:45 ​Sebastién Benzekry (INRIA Bordeaux-Sud Ouest): Machine learning and mechanistic modeling for prediction of metastatic relapse in breast cancer
​13:45–14:30 Sandy Anderson (Moffitt Cancer Center): Man Vs Machine in Cancer Therapy
​14:30–15:00 ​Concluding discussion


Participation is free of charge. To register for the workshop please send an email to Philip Gerlee.

The workshop is funded by the SSF-grant ”Hierarchical mixed effects modelling”​​

Sidansvarig Publicerad: ti 05 jan 2021.