Michael U. Gutmann

I am a Senior Lecturer (Assoc Prof) in Machine Learning at the School of Informatics of the University of Edinburgh, affiliated with the Institute for Adaptive & Neural Computation.

My research is in machine learning for science, with a focus on developing methods for (Bayesian) inference and design.

Recent papers

• BioRxiv: Risk-averse optimization of genetic circuits under uncertainty [link]

• TMLR: Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families [link]

• eLife: Designing Optimal Behavioral Experiments Using Machine Learning [link]

• TMLR: Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling [link]

• JMLR: Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data [link]