Mark Heimann

Researcher, Lawrence Livermore National Laboratory

Joined

September 2021

Names

Mark Heimann (Preferred)

Emails

****@umich.edu (Confirmed)

****@llnl.gov (Confirmed)

Personal Links

Homepage

Google Scholar

DBLP

Career & Education History

Researcher

Lawrence Livermore National Laboratory (llnl.gov)

2022 – Present

Postdoc

Lawrence Livermore National Laboratory (llnl.gov)

2020 – 2022

PhD student

University of Michigan (umich.edu)

2015 – 2020

Advisors, Relations & Conflicts

PhD Advisor

Danai Koutra

2015 – 2020

Expertise

Network embedding

Present

Graph neural networks

Present

Publications

Improving Graph Neural Networks with Heterophily-based Filtration and Filtration Learning
Samuel Leventhal, Valerio Pascucci, Mark Heimann
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Estimating Epistemic Uncertainty of Graph Neural Networks using Stochastic Centering
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
- NeurIPS 2023 GLFrontiers Workshop Poster
- Readers: Everyone
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
- ICLR 2024 poster
- Readers: Everyone
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T Schaub, Danai Koutra
- LoG 2023 Oral
- Readers: Everyone
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification
Rakshith Subramanyam, Mark Heimann, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan
- Published: 01 Jan 2023, Last Modified: 17 May 2023
- WACV 2023
- Readers: Everyone
Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks
Samuel Leventhal, Attila Gyulassy, Valerio Pascucci, Mark Heimann
- Published: 22 Nov 2022, Last Modified: 05 May 2023
- NeurIPS 2022 GLFrontiers Workshop
- Readers: Everyone
Node Proximity Is All You Need: A Unified Framework for Proximity-Preserving and Structural Node and Graph Embedding
Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
- 13 Jun 2022
- OpenReview Archive Direct Upload
- Readers: Everyone
Analyzing Data-Centric Properties for Graph Contrastive Learning
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
- Published: 31 Oct 2022, Last Modified: 11 Jan 2023
- NeurIPS 2022 Accept
- Readers: Everyone
Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints
Konstantia Georgouli, Helgi I. Ingólfsson, Fikret Aydin, Mark Heimann, Felice C. Lightstone, Peer-Timo Bremer, Harsh Bhatia
- 2022 (modified: 17 Nov 2022)
- CoRR 2022
- Readers: Everyone
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
Jing Zhu, Danai Koutra, Mark Heimann
- 2022 (modified: 04 Jan 2023)
- CIKM 2022
- Readers: Everyone

View all 38 publications

Co-Authors

View all 46 co-authors