I'm a data scientist at Google, where I'm part of our core analysis organisation in Search Ads. My work focuses on product launch analysis, large-scale experiments, causal inference, anomaly detection, strategy, team development, and analysis software like CausalImpact (video).
Before joining Google, I was a postdoc at ETH Zurich, Switzerland, where I worked on variational Bayes and high-dimensional time series (thesis), which is the focus of this site.
Variance reduction in bipartite experiments through correlation clustering
J. Pouget-Abadie, K. Aydin, W. Schudy, K.H. Brodersen, Vahab Mirrokni (2019)
Neural Information Processing Systems (NeurIPS)
Reward-guided learning with and without causal attribution
G. Jocham*, K.H. Brodersen*, A.O. Constantinescu, M.C. Kahn, A. Ianni, M.E. Walton, M.F. Rushworth, T.E. Behrens (2016)
Inferring causal impact using Bayesian structural time-series models
K.H. Brodersen, F. Gallusser, J. Koehler, N. Remy, S. Scott (2015)
Annals of Applied Statistics
Variational Bayesian mixed-effects inference for classification studies
K.H. Brodersen, J. Daunizeau, C. Mathys, J.R. Chumbley, J.M. Buhmann, K.E. Stephan (2013)
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