Hi! I'm David Tench, a computer science researcher interested in processing massive datasets.
I design space-efficient, memory-hierarchy-aware algorithms and build systems to address massive-scale problems in areas like bioinformatics, databases, network measurement, and machine learning. I am particularly interested in designing and implementing practical graph streaming and sketching algorithms.
Recent News
Here's what I've been up to lately.
I was recently awarded the 2023 Grace Hopper postdoctoral fellowship at Lawrence Berkeley Labs , funding a two-year postdoctoral position to build systems powered by randomized and memory-hierarchy-aware algorithms to solve massive-scale scientific problems.
Watch me present GraphZeppelin at SIGMOD 2022.
Check out my paper on GraphZeppelin, a graph processing system which uses linear sketching techniques to process massive, changing graphs. Appeared at SIGMOD 2022. Check out our code here . I presented an early version of this work as an invited talk at APOCS 2022.
About Me
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.
Contact
Email me at dtench [at] pm [dot] me.