The Mystery Behind Large Graphs

Our guest in this episode is David Tench, a Grace Hopper postdoctoral fellow at Lawrence Berkeley National Labs, who specializes in scalable graph algorithms and compression techniques to tackle massive datasets.

In this episode, we will learn how his techniques enable real-time analysis of large datasets, such as particle tracking in physics experiments or social network analysis, by reducing storage requirements while preserving critical structural properties.

David also challenges the common belief that giant graphs are sparse by pointing to a potential bias: Maybe because of the challenges that exist in analyzing large dense graphs, we only see datasets of sparse graphs? The truth is out there…

David encourages you to reach out to him if you have a large scale graph application that you don't currently have the capacity to deal with using your current methods and your current hardware. He promises to "look for the hammer that might help you with your nail".

Guest

David Tench: I am the 2023 Grace Hopper postdoctoral researcher at Lawrence Berkeley Labs advised by Aydin Buluç. Before that, I was an NSF Computing Innovation Fellow at Rutgers University advised by Martin Farach-Colton and Michael Bender. I completed my PhD at UMass Amherst in the College of Information and Computer Science, where I was advised by Andrew McGregor. I design algorithms and build systems for large-scale computation, in particular graph sketching algorithms for graphs that are massive, dense, and change over time. I apply these algorithms and systems to real-world problems in areas like databases, bioinformatics, network measurement, and machine learning.