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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.

Grace Hopper postdoc fellowship

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.


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.


Email me at dtench [at] pm [dot] me.