I currently work as an AI engineer at ThirdAI, where we are building an
in-house sparse machine learning engine from scratch. At ThirdAI, I am
lucky to be able to combine my passions for building algorithms and
for implementing high performance systems.
While at ThirdAI, and earlier in Rice University's Sketching and Hashing
Lab, I have investigated randomized algorithms, group testing, and
locality sensitive hashing. This work has resulting in efficient data
structures that use these techniques to speed up high dimensional
near neighbor search
and set-of-vector search
Broadly, I am interested in building algorithms that solve real-world
problems in a wide variety of domains, including machine learning,
near neighbor search, streaming, and AI alignment.
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries.
Joshua Engels, Benjamin Coleman, Vihan Lakshman, and Anshumali Shrivastava
Practical Near Neighbor Search via Group Testing.
Joshua Engels*, Benjamin Coleman*, and Anshumali Shrivastava
NeurIPS 2021: Spotlight talk - top 3%
* indicates equal contribution
Missed one! How ballot layout and visual task strategy can interact to produce voting errors.
Joshua Engels, Xianni Wang, Michael D. Byrne
International Conference on Cognitive Modeling 2020.
Bananagrams is NP-complete
Have you ever wondered if Bananagrams is NP-complete? No? Well I think it is, and I’ll prove it!...
What is Set-of-Vector Search?
In the traditional near neighbor problem, we are given a dataset \(D\) of \(N\) \(d\)-dimensional vectors. Our goal...
Simulating Voter Errors with ACT-R
Badly designed ballots may not seem like a pressing problem, but they have already swung multiple elections. For...