David F. Gleich

Professor of Computer Science (and Mathematics, by courtesy) · Purdue University

My research is on novel models and fast large-scale algorithms for data-driven computing. This includes looking at data from scientific data analysis, bioinformatics, and network analysis. I am committed to making software available based on this research and more generally.

Full publication list
Curriculum vitae

Awards & research funding

Some people like seeing this award and research funding information.

  • SIAM Fellow (2025)
  • Purdue University Faculty Scholar
  • SIAM Outstanding Publication Prize (2018)
  • Sloan Research Fellowship (2016)
  • NSF CAREER Award (2011)
  • John von Neumann post-doctoral fellowship at Sandia National Labs (2009)

Research funding from NSF, DOE, DARPA, IARPA, and NASA.

OFFICE LWSN 1207 · Lawson Building, Purdue ← find it
EMAIL first name @ this domain
ELSEWHERE scholar · github · youtube · mastodon · bluesky
RECENT
CURRENTLY ADVISING
📚✨PhD Disha Shur · random samples of nonlinear graphs
📚✨PhD Marc Tunnell · preconditioners for SPD systems
📚✨PhD Zitao Song · matrix methods for AI training
📚✨PhD Zhiyao Xu · EMT linear solvers
📚✨HS Ayush Kulkarni · tensor Kronecker H-eigenvectors
COMPLETED
🎉🥳2026PhD🎓 Yufan Huang · dense structures, shortest paths, SDPs
🎉🥳2025PhD🎓 Omar Eldaghar · local structure, epidemics
🎉🥳2025PhD🎓 Charles Colley · tensors, sampling, architecture
🎉🥳2022PhD🎓 Meng Liu · nonlinear diffusions, NN analysis
⭐2022MS Disha Shur · personalized PageRank embeddings
⭐2021UG Abhi Sinha · on-the-fly image graphs
⭐2020UG Cameron Ruggles · parallel correlation clustering
🎉🥳2019PhD🎓 Nate Veldt · graph clustering optimization
🎉🥳2019PhD🎓 Nicole Eikmeier · spectra of realistic networks
🎉🥳2019PhD🎓 Huda Nassar · network alignment
⭐2019UG Madhurima Mahajan · tensor methods in DFT
⭐2019UG Joshua Yeung · higher-order word embeddings
🎉🥳2018PhD🎓 Tao Wu · higher-order random walks
⭐2018UG Caitlin Kennedy · spectral embeddings with motifs
⭐2017EXT Austin Benson · higher-order networks
🏆2017HS Arjun Ramani · structured random graphs
🎉🥳2016PhD🎓 Kyle Kloster · graph diffusions, matrix functions
🎉🥳2016PhD🎓 Yangyang Hou · low-rank clustering
⭐2016MS Varun Vasudevan · rank-1 approximations
⭐2016UG Bryan Rainey · ranking methods
⭐2016HS Forrest Brown · non-backtracking pseudo-spectra
🎉🥳2015PhD🎓 Yao Zhu · parallel sparse solvers
⭐2015EXT Joyce Whang · overlapping clustering
⭐2012UG Austin Benson · tall-and-skinny matrix factorizations
RESEARCH INTERESTS

I have broad interests beyond just matrices, networks, and data. I like to think in terms of models, algorithms, and data. So I had an LLM categorize all my papers along these axes. Models are how we set up and formalize a problem. Algorithms are how we solve it. Data are what we use them to study.

One dot per paper, per theme. Click a dot to light up that paper's full footprint.
Click a dot
Click any dot to see the paper's full tag set and its other appearances.
TEACHING