David F. Gleich
Professor and University Faculty Scholar
Computer Science
Mathematics (Courtesy)
Purdue University
My research is on high performance and large scale computations with a focus on enabling previously infeasible analysis of data from biology, social networks, and scientific simulations. I find stating and studying these problems as large scale matrix computations productive. I received an NSF CAREER award to discover how to scale matrix methods to the enormous sizes of modern data with only modest computational resources. Prior to joining Purdue, I was the John von Neumann post-doctoral fellow at Sandia National Labs.
News highlights from the last 5-ish years.
- 2023-12-01 We have a paper published in Nature Machine Intelligence on topological analysis of neural networks, read about it in IEEE Spectrum or Purdue News
- 2021-01-22 A nice summary of some recent ideas in higher-order analysis from SIAM News Higher-order Network Analysis Takes Off, Fueled by Old Ideas and New Data
- 2020-09-21 Behind-the-scenes videos: Our correlation clustering work Using cliques to draw graphs
- 2018-07-10 I'm very honored that SIAM selected my paper, PageRank Beyond the Web for a 2018 SIAM Outstanding Paper Prize
Recent Interests
- Higher-order methods for analyzing data
- Algorithmic Anti-differentiation
- Network diffusions including PageRank, heat kernels
- Spectral graph theory
- High performance computers
- Large scale data computations
- Community detection and clustering
- Matrix-based network computations
- About a decade ago, I wrote an article for ACM's XRDS magazine on some of the issues that
- arise in my research. It's a nice overview of issues I'm looking at
- and problems I'm studying.
- David F. Gleich, Expanders, tropical semi-rings, and nuclear norms: oh my! XRDS: Crossroads, The ACM Magazine for Students - Scientific Computing, Spring 2013, Volume 19, Issue 3, Pages 32-36, http://dx.doi.org/10.1145/2425676.2425688
dgleich--at--purdue.edu
Everyone gets a lot of email, myself included. If I haven't replied in a week, please do resend it. (But wait a week!) If it's urgent, give me a call! Or shoot me a tweet! Or a thread (no links to threads yet!). Or a toot.
Classes
- Fall 2024 CS515 - Matrix Computations
Prior classes
- Fall 2023 CS515 - Matrix Computations
- Spring 2023, CS520 - Computational Methods in Optimization
- Fall 2022 CS515 - Matrix Computations
- Fall 2021, CS515 - Matrix Computations The online, virtual edition!
- Spring 2021 CS514 - Numerical analysis The online, virtual edition!
- Fall 2020, CS515 - Matrix Computations The online, virtual edition!
- Spring 2020, CS520 - Computational Methods in Optimization
- Fall 2019, CS515 - Matrix Computations
- Spring 2019, CS520 - Computational Methods in Optimization
- Fall 2018, CS515 - Matrix Computations
- Fall 2017, CS515 - Matrix Computations
- Spring 2017, CS520 - Computational Methods in Optimization
- Fall 2016, CS314 - Numerical Methods
- Spring 2016, CS 514 - Numerical analysis
- Fall 2015, CS515 - Matrix Computations
- Fall 2015, CS 515 - Matrix Computations
- Spring 2014, CS314 - Numerical Methods
- Spring 2014, CS520 - Computational Methods in Optimization
- Spring 2013, CS520 - Computational Methods in Optimization
- Fall 2012, CS515 - Matrix Computations
- Spring 2012, Computational Methods in Optimization
- Fall 2011, Network and Matrix Computations
Recent student co-authors
Here's a list of students I've worked with recently where we have a paper or submission together, so the list includes more than students I directly advise. Please ask to be included if you don't see yourself listed!
Past student co-authors
(Probably many links are out of date! Check Internet Archive.)