ODSL - The ORIGINS Data Science Laboratory

The ODSL provides a new pillar for novel analysis methods, algorithms and computational tools to fully exploit high-dimensional, complex data sets. The ODSL specialises in advanced techniques for pattern recognition in noisy data and for the identification and extraction of weak signals. Modern techniques in machine learning will be employed as well as augmented reality and visualisation techniques in the extraction of scientific results.

ODSL Block Courses - Sept 2021

We are organising our next set of Block courses from September 6th - 16th 2021 under the title Practical Inference for Researchers in the Physical Sciences.

This session consists of two one-week courses:

1. Monte Carlo inference methods

Introduction to Bayesian inference with physical models. Parameter uncertainties, degeneracies and knowledge updates. Model comparison and criticism. Modern Monte Carlo algorithms for Bayesian inference in practice and probabilistic computation packages: Importance Sampling, Markov Chain Monte Carlo, Nested Sampling.

2. Bayesian workflow

Bayesian thinking, going from a science question to a generative statistical model, defining sensible priors, verification through simulations, diagnosing problems in models and computation, robust decision making, experiment design.
 

The courses will be held online and organised by Johannes Buchner and Francesca Capel. We plan to offer credits to both TUM and LMU students. 

For more information and to register please visit: https://indico.ph.tum.de/event/6875/

Projects

Visit out projects page for an overview of our ongoing research projects.

 

Journal Club

We run a journal club to discuss data science topics every Friday at 2 pm. To join our mailing list and receive notifications, please send an empty email to odsl-subscribe(at)lists.lrz.de or visit this website: https://lists.lrz.de/mailman/listinfo/odsl.

If you have ideas for topics to discuss, feel free to propose them in the following google doc: http://bit.ly/odsljc20.

Archive

Please visit our archive, to see older posts from our website.