Integrating Tomorrow's Government: An Exploratory Case Study of Resistance to Data Consolidation. - John Woosley; Carlos Thomas; Andy Davis (AMCIS, 2004)

Edgecomputing: extending enterprise applications to the edge of the internet. - Andy Davis; Jay Parikh; William E. Weihl (WWW (Alternate Track Papers & Posters), 2004)

The Fun and Future of CTF. - Andy Davis; Tim Leek; Michael Zhivich; Kyle Gwinnup; William Leonard (3GSE, 2014)

TensorFlow: A System for Large-Scale Machine Learning. - Martín Abadi; Paul Barham 001; Jianmin Chen; Zhifeng Chen; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Geoffrey Irving; Michael Isard; Manjunath Kudlur; Josh Levenberg; et al. (OSDI, 2016)

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. - Martín Abadi; Ashish Agarwal; Paul Barham 001; Eugene Brevdo; Zhifeng Chen; Craig Citro; Gregory S. Corrado; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Ian J. Goodfellow; et al. (CoRR, 2016)

TensorFlow: A system for large-scale machine learning. - Martín Abadi; Paul Barham 001; Jianmin Chen; Zhifeng Chen; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Geoffrey Irving; Michael Isard; Manjunath Kudlur; Josh Levenberg; et al. (CoRR, 2016)

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. - Noam Shazeer; Azalia Mirhoseini; Krzysztof Maziarz; Andy Davis; Quoc V. Le; Geoffrey E. Hinton; Jeff Dean (ICLR (Poster), 2017)

AutoCTF: Creating Diverse Pwnables via Automated Bug Injection. - Patrick Hulin; Andy Davis; Rahul Sridhar; Andrew Fasano; Cody Gallagher; Aaron Sedlacek; Tim Leek; Brendan Dolan-Gavitt (WOOT, 2017)

Dynamic control flow in large-scale machine learning. - Yuan Yu; Martín Abadi; Paul Barham 001; Eugene Brevdo; Mike Burrows; Andy Davis; Jeff Dean; Sanjay Ghemawat; Tim Harley; Peter Hawkins; Michael Isard; Manjunath Kudlur; et al. (EuroSys, 2018)

MLIR: A Compiler Infrastructure for the End of Moore's Law. - Chris Lattner; Jacques A. Pienaar; Mehdi Amini; Uday Bondhugula; River Riddle; Albert Cohen 001; Tatiana Shpeisman; Andy Davis; Nicolas Vasilache; Oleksandr Zinenko (CoRR, 2020)

MLIR: Scaling Compiler Infrastructure for Domain Specific Computation. - Chris Lattner; Mehdi Amini; Uday Bondhugula; Albert Cohen 001; Andy Davis; Jacques A. Pienaar; River Riddle; Tatiana Shpeisman; Nicolas Vasilache; Oleksandr Zinenko (CGO, 2021)

Cyber security in New Space. - Mark Manulis; Christopher P. Bridges; R. Harrison; Venkkatesh Sekar; Andy Davis (Int. J. Inf. Sec., 2021)

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s. - Felix Chern; Blake Hechtman; Andy Davis; Ruiqi Guo; David Majnemer; Sanjiv Kumar (NeurIPS, 2022)

Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models. - Shibo Wang; Jinliang Wei; Amit Sabne; Andy Davis; Berkin Ilbeyi; Blake Hechtman; Dehao Chen; Karthik Srinivasa Murthy; Marcello Maggioni; Qiao Zhang; Sameer Kumar; Tongfei Guo; et al. (ASPLOS (1), 2023)

A Study of Multi-Factor and Risk-Based Authentication Availability. - Anthony Gavazzi; Ryan Williams; Engin Kirda; Long Lu; Andre King; Andy Davis; Tim Leek (USENIX Security Symposium, 2023)

On Training a Neural Network to Explain Binaries. - Alexander Interrante-Grant; Andy Davis; Heather Preslier; Tim Leek (CoRR, 2024)

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration. - Ibrahim Ahmed 012; Clemens Schaefer; Gil Tabak; Denis Vnukov; Zenong Zhang; Felix Chern; Anatoliy Yevtushenko; Andy Davis (CoRR, 2025)