Teaching
Lecture and lab courses in Heidelberg
- University of Heidelberg lecture course (Bachelor/Master): The Physics of interstellar travel (MVSpec, SS 2024)
- Introduction to probabilistic inference (2023)
- University of Heidelberg lecture course (Bachelor/Master): The Physics of interstellar travel (MVSpec, SS 2022)
- University of Heidelberg lecture course (Bachelor/Master): The Physics of interstellar travel (MVSpec, SS 2021)
- University of Heidelberg lecture course (Bachelor/Master): The Physics of interstellar travel (MVSpec, SS 2020)
- Die Physik der interstellaren Raumfahrt (PSem, SS 2019)
(The Physics of interstellar travel - Bachelor seminar) (link to 2017 course) (link to 2018 course) - University of Heidelberg
Physics MSc course: Computational statistics and data analysis (MVComp2, 2015)
- Einführung in die Astronomie und Astrophysik III (PSem, WS 2014/2015)
(Introduction to Astronomy & Astrophysics III - Mandatory seminar) - University of Heidelberg
Physics BSc Practical course: Statistical Methods (UKSta, 2013)
(Details of 2012 course) (Details of 2011 course) (Details of 2009 course) - University of Heidelberg Physics and Astronomy Oberseminar : Applications of machine learning in astronomy (2009)
- IMPRS and MPIA mini lecture course: Introduction to machine learning and pattern recognition (2008)
- University of Heidelberg lecture course: Machine learning, pattern recognition and statistical data modelling (2007)
- University of Heidelberg lecture course: From brown dwarfs to giant planets (2005)
Other teaching
- ESAC data analysis & statistics workshop 2017, ESAC, Madrid, Spain, November 2017
(astrometry, Gaia, GDR1, inference from astrometry) - Computational statistics and astrostatistics, IUCAA, Pune, India, January 2017
- 38th International School for Young Astronomers, IPM, Tehran, Iran, August-September 2016
(Introduction to Bayesian statistics; Gaia mission and first data release) - Good practices in astrostatistics, IUCAA, Pune, India, January 2016
- Astrostats 2013 summer school, Alicante, Spain, June 2013
- Tutor for Experimental Physics 1 (mechanics, thermodymanics), WS2012/2013, WS2022/2023
- Tutor for Experimental Physics 2 (electromagnetism), SS2014
- Tutor for Experimental Physics 3 (optics, quantum physics), WS2013/14, WS2016/17
- Mathematics supervisions for the Natural Sciences Tripos, University of Cambridge, 1993-1996
Discussions, notes, tutorials
- Why we must use class priors and adjust the confusion matrix in classification
- What is inference?
- Why science is not really about falsification
- A miscarriage of justice following poor interpretation of statistics: The Sally Clark case
- Discovery of the Higgs boson? What p-values mean
- A note on combining estimates of probabilities (with a discussion of the meaning of conditional independence)
- A summary of Gaussian Processes
- Using Gaussian Processes to
infer 3D Galactic dust extinction
This method was subsequently implemented by my PhD students and applied in various studies, starting with this one. - Estimating distances from parallaxes: a tutorial
- Estimating the distance to and sizes/shape of a star cluster from astrometry
- Tutorial on inference from parallax and proper motions
- Model comparison with the cross validation likelihood: see section 11.6.1 of Practical Bayesian Inference
- Bayesian timeseries analysis and stochastic processes
- A probabilistic model to identify exoplanetary companions from multi-epoch astrometry
- Articles with a significant didactic component
- The Sun diver
- Data sampling vs. posterior sampling: Appendix to Close encounters of the stellar kind
- Bayesian time series analysis: Bayesian time series analysis of terrestrial impact cratering and A Bayesian method for the analysis of deterministic and stochastic time series
- Stochastic processes: A Bayesian method for the analysis of deterministic and stochastic time series
- Bayesian parameter estimation: Bayesian inference of stellar parameters and interstellar extinction using parallaxes and multiband photometry
- The evidence for and against astronomical impacts on climate change and mass extinctions: A review
- Probabilistic object classification and model-based priors:
Quasar and galaxy classification in Gaia
Data Release 2 and
Finding rare objects and building pure samples: Probabilistic quasar classification from low resolution Gaia spectra - A recurrent neural network for modelling dynamical systems