I'm a fourth year graduate student in computer science at Johns Hopkins University, where I'm currently working with Prof. Suchi Saria on improving the reliability of machine learning models. For more on this see our recent tutorial from the 2019 ACM Conference on Fairness, Accountability and Transparency.
Previously, I received B.S. degrees in computer science and mathematics from Vanderbilt University, where I worked on ensuring privacy through de-identification in electronic medical records with Professor Brad Malin.
The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance.
Adarsh Subbaswamy, Bryant Chen, and Suchi Saria.
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport.
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria.
Artificial Intelligence and Statistics, AISTATS 2019.
(Previously an oral presentation at NeurIPS 2018 Causal Learning Workshop)
Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms.
Adarsh Subbaswamy and Suchi Saria.
Uncertainty in Artificial Intelligence, UAI 2018.
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions.
Hossein Soleimani*, Adarsh Subbaswamy*, and Suchi Saria. (*Equal Contribution)
Uncertainty in Artificial Intelligence, UAI 2017.
Non-intrusive occupancy monitoring using smart meters.
Dong Chen, Sean Barker, Adarsh Subbaswamy, David Irwin, and Prashant Shenoy.
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. 2013.
Baltimore, Maryland, USA
asubbaswamy (at) jhu (dot) edu