# David F. Gleich

### 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.

## 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
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

## Email

 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!

## 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!