Publications

Augmented balancing weights as linear regression. David Bruns-Smith, Oliver Dukes, Avi Feller, and Betsy Ogburn. Forthcoming at the Journal of the Royal Statistical Society (Series B).

Most recent version

Using Supervised Learning to Estimate Inequality in the Size and Persistence of Income Shocks. David Bruns-Smith, Avi Feller, and Emi Nakamura. FAccT 2023.

Outcome assumptions and duality theory for balancing weights. David Bruns-Smith and Avi Feller. AISTATS 2022.

Model-free and model-based policy evaluation when causality is uncertain. David Bruns-Smith. ICML 2021.

Working Papers

Robust fitted-q-evaluation and iteration under sequentially exogenous unobserved confounders. David Bruns-Smith and Angela Zhou. 2023. Preprint.

Disentangling Age, Time and Cohort Effects in Income Inequality: A Machine Learning Approach. David Bruns-Smith, Emi Nakamura, Jon Steinsson.

Two Stage Machine Learning for Instrumental Variable Regression. David Bruns-Smith.

Previous Publications

Accelerating Genomic Data Analytics with Composable Hardware Acceleration Framework.
Tae Jun Ham, David Bruns-Smith, Brendan Sweeney, Yejin Lee, Seong Hoon Seo, 
U. Gyeong Song, Young H. Oh, Krste Asanovic, Jae W. Lee, and Lisa Wu Wills.
IEEE Micro (2021).

Genesis: a hardware acceleration framework for genomic data analysis. 
Tae Jun Ham, David Bruns-Smith, Brendan Sweeney, Yejin Lee, Seong Hoon Seo, 
U. Gyeong Song, Young H. Oh, Krste Asanovic, Jae W. Lee, and Lisa Wu Wills. 
International Symposium on Computer Architecture (ISCA), 2020.
IEEE Micro Top Picks.

Enhancing network visibility and security through tensor analysis.
Muthu Baskaran, Thomas Henretty, James Ezick, Richard Lethin, and David Bruns-Smith. 
Future Generation Computer Systems, 2019.

Fpga accelerated indel realignment in the cloud.
Lisa Wu, David Bruns-Smith, Frank Nothaft, Qijing Huang, Sagar Karandikar, 
Johnny Le, Andrew Lin, Howard Mao, Brendan Sweeney, Krste Asanovic, and David Patterson.
IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019.

Memory-efficient parallel tensor decompositions.
Muthu Baskaran, Tom Henretty, Benoit Pradelle, M. Harper Langston, David Bruns-Smith, 
James Ezick, and Richard Lethin.
IEEE High Performance Extreme Computing Conference (HPEC), 2017.
Best Paper Finalist

A quantitative and qualitative analysis of tensor decompositions on spatiotemporal data.
Tom Henretty, Muthu Baskaran, James Ezick, David Bruns-Smith, and Tyler A Simon.
IEEE High Performance Extreme Computing Conference (HPEC), 2017.

Cyber security through multidimensional data decompositions.
David Bruns-Smith, Muthu M. Baskaran, James Ezick, Tom Henretty, and Richard Lethin.
Cybersecurity Symposium (CYBERSEC), 2016.