Teaching

Lecture and lab courses in Heidelberg

• University of Heidelberg lecture course (Bachelor/Master): The Physics of interstellar travel (MVSem, SS 2026)
• 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