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. I spent the summer of 2018 at Oak Ridge National Laboratory, working with Dr. Ramakrishnan Kannan on nonlinear dimensionality reduction. We collaborated with researchers at the Center for Nanophase Materials Sciences to develop new algorithms for hyperspectral unmixing.

My current research focuses on 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 have broader interests in methods and applications for representation learning, matrix factorization, and nonlinear dimensionality reduction.

Research Projects

Publications

*Equal contribution