Mark Heimann

Mark Heimann

I am a PhD candidate in the computer science department at the University of Michigan, where I consider myself fortunate to be a member of the GEMS Lab and advised by Danai Koutra.

Specifically, my current research interests involve representation learning in networks, where I am developing methods to learn vector embeddings of nodes that can be used for multi-network problems such as network alignment. I am interested in the connection between node representation learning methods based on deep learning-inspired language modeling techniques, matrix factorization, and low-rank matrix approximation. I also have broader interests in develping scalable methods for various data mining tasks involving network data.

I have an eclectic set of other interests ranging from competitive (high-level) chess and (novice-level) powerlifting to music and amateur baking. Sometimes I manage to combine them with my work; for instance, I am very interested in creating artificial intelligence technology for music analysis and performance, as well as the design and use of electronic effects especially for augmenting acoustic instruments. Most of the time, though, they exist in the space left for them by life as a PhD student, but I'm always happy to talk about them.

Research Projects

Publications

*Equal contribution