David F. Gleich
Physics and Astronomy · Purdue University West Lafayette
Publications
220
Citations
5,776
Est. group size
~2
Recurring co-author estimate
Active years
24
Publishing since 2003
David F. Gleich works on the mathematics and computing methods behind analyzing large networks and graphs, including tools like graph matrices, clustering, and higher-order structures called hypergraphs (where relationships can involve more than two items at once). His work also extends into designing specialized computer hardware and optimization algorithms to process these large-scale graph datasets more efficiently.
Publication output was steady at around 14-16 papers per year from 2017-2020, then declined to a lower and more variable pace of roughly 4-10 papers per year from 2021 onward.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications
Proceedings of the National Academy of Sciences · 2026
- A Cheeger inequality for size-specific conductance
Discrete Mathematics · 2026
- Decoupling Variance and Scale-Invariant Updates in Adaptive Gradient Descent for Unified Vector and Matrix Optimization
Open MIND · 2026
- Decoupling Variance and Scale-Invariant Updates in Adaptive Gradient Descent for Unified Vector and Matrix Optimization
arXiv (Cornell University) · 2026
- UpDown: A Supercomputer Co-Designed for Scalable Graph Processing
IEEE Transactions on Parallel and Distributed Systems · 2026
- UpDown: Efficient Manycore based on Many Threading and Scalable Memory Parallelism
2026
- A spatial hypergraph model to smoothly interpolate between pairwise graphs and hypergraphs to study higher-order structures
PLOS complex systems. · 2025
- A Spatial Hypergraph Model Where Epidemic Spread Demonstrates Clear Higher-Order Effects
Studies in computational intelligence · 2025
- Scaling Triangle Counting and K-Truss on the UpDown Architecture
2025
- KVMSR+UDWeave: Extreme-Scaling with Fine-grained Parallelism on the UpDown Graph Supercomputer
2025
- An Empirical Study of Conjugate Gradient Preconditioners for Solving Symmetric Positive Definite Systems of Linear Equations
arXiv (Cornell University) · 2025
- How Fast Can Graph Computations Go on Fine-grained Parallel Architectures
arXiv (Cornell University) · 2025
- Powers of Magnetic Graph Matrix: Fourier Spectrum, Walk Compression, and Applications
arXiv (Cornell University) · 2025
- Densest Subhypergraph: Negative Supermodular Functions and Strongly Localized Methods
2024
- Better than best low-rank approximation with the singular value decomposition
arXiv (Cornell University) · 2024
- arXiv (Cornell University)×42
- Zenodo (CERN European Organization for Nuclear Research)×4
- SIAM Journal on Scientific Computing×3
- Society for Industrial and Applied Mathematics eBooks×3
- Studies in computational intelligence×3
- Bruno Ribeiro
Physics and Astronomy · Purdue University West Lafayette
- Xiaodan Lou
Physics and Astronomy · Indiana University
- Tanya Berger‐Wolf
Physics and Astronomy · The Ohio State University
- Subhadeep Paul
Physics and Astronomy · The Ohio State University
- Filipi N. Silva
Physics and Astronomy · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile