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 am spending 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 am developing methods for node embedding that preserve the similarity between nodes with similar structural roles in a network or networks. Such node embeddings are comparable even across entirely different networks, for example when performing multi-network tasks such as network alignment or classification. I have broader interests in methods and applications for representation learning, matrix factorization, and nonlinear dimensionality reduction.
- Mark Heimann, Tara Safavi, and Danai Koutra. Distribution of Node Embeddings as Multiresolution Features for Graphs. IEEE International Conference on Data Mining (ICDM), 2019. Best Student Paper
- Di Jin, Mark Heimann, Ryan Rossi, and Danai Koutra. node2bits: Compact Time- and Attribute-aware
Node Representations for User Stitching. European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2019.
- Di Jin*, Mark Heimann*, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, and Danai Koutra. Smart Roles: Inferring Professional Roles in Email Networks. Conference on Knowledge Discovery and Data Mining (KDD), 2019.
- Mark Heimann, Haoming Shen, Tara Safavi, and Danai Koutra. REGAL: Representation Learning-based Graph Alignment. International Conference on Information and Knowledge Management (CIKM), 2018. [code]
- Mark Heimann*, Wei Lee*, Shengjie Pan, Kuan-Yu Chen, and Danai Koutra. HashAlign: Hash-Based Alignment of Multiple Graphs. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018.
- Yujun Yan, Mark Heimann, Di Jin, and Danai Koutra. Fast Flow-based Random Walk with Restart in a Multi-query Setting. SIAM International Conference on Data Mining (SDM), 2018.
- Mark Heimann and Danai Koutra. On Generalizing Neural Node Embedding Methods to Multi-Network Problems. KDD Workshop on Mining and Learning with Graphs (MLG), 2017.