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 2019 at the Information Sciences Institute in California working with Emilio Ferrara using node embeddings to detect cyberbullying in social media at the level of individual users as well as media sessions. Previously, I spent Winter 2019 interning remotely with Ryan Rossi at Adobe Research working on entity resolution, and in the summer of 2018 I was at Oak Ridge National Laboratory, working with 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 work on node embedding with the goal of preserving the similarity between nodes with similar structural roles in a network or networks. I propose methods that use these node embeddings for data mining tasks that are defined over multiple networks, such as network alignment and classification. I am interested in methodological connections and applications to natural language processing and the social and natural sciences.

Publications

*Equal contribution