Moritz
Münchmeyer

Assistant Professor Of Physics at University of Wisconsin-Madison

Moritz Münchmeyer

Welcome to my website! I’m an Assistant Professor of Physics at the University of Wisconsin-Madison. My speciality is Theoretical and Computational Cosmology. In my research I combine theoretical investigation, data from different cosmological surveys, and methods from artificial intelligence to deepen our understanding of the universe. More about my research at UW-Madison is here.

Before moving to Madison, I was a Senior Postdoctoral Fellow at Perimeter Institute for Theoretical Physics and a Postdoctoral Affiliate at the Vector Institute for Artificial Intelligence. I also co-founded the machine learning company Wolution, which provides image and video analysis to clients in research and industry.

This website is not kept up-to-date, please refer to my Research Group Website instead.

Research interests

In my research I combine some of the most exciting cosmological observables, such as the Cosmic Microwave Background and galaxy surveys, with techniques from data science and in particular machine learning. Using analytical and computational methods, I find new ways to probe fundamental physics to ultimately answer questions such as what happened at the big bang.

Fundamental physics with cosmology.

In cosmology, we can look back through time, up to the big bang, by combining very sophisticated experiments and deep theoretical analysis. To me this is one of the most fascinating endeavors that humans have ever attempted and made progress in. My research connects fundamental theory and experiment by finding new ways in which data can be analyzed to obtain new insights or discoveries. I am studying the properties of the matter and radiation which fill the universe and can teach us about the most fundamental theories of physics, such as string theory.

Machine learning in Cosmology.

Machine learning has revolutionized many aspects of technology, for example image and speech recognition, but is not yet very important in the precision science of cosmology. Machine learning tools are often too uncontrolled or poorly understood to deal with the percent level accuracy required in cosmology, and they have been developed for totally different types of data. In my research, I try to bridge this gap by developing new machine learning tools and techniques, specifically made to solve challenges in cosmology.

You can learn more about my research here, browse recent projects here, or have a look into my publications.