I am a PhD student in the Human-Computer Interaction Institute (HCII) within the School of Computer Science at Carnegie Mellon University. I am fortunate to be advised by Steven Wu and Ken Holstein.
My work examines the safety and validity of data-driven algorithms deployed in high stakes decision-making settings. I develop algorithmic methods and evaluation tools to help practitoners assess the feasability of model deployments given real-world complexities. I adopt an interdisciplinary perspective, leveraging methods from HCI, machine learning, and statistics. My work is generously supported by an NSF Graduate Research Fellowship.
Previously, I did my Master’s in Computer Science at Cambridge. I also studied Computer Science and Psychology at the University of Missouri, where I co-founded TigerAware, a mobile-based research platform.
News & Travel
|Apr 2023||Two papers accepted at FAccT 23’. See you in Chicago!|
|Nov 2022||I will be presenting work on counterfactual prediction under outcome measurement error at the NeurIPS Causal ML for Impact workshop on Friday Dec 2nd.|
|Apr 2022||I will be presenging Under-reliance or misalignment? at the CHI Workshop on Trust and Reliance in Human-AI Teams. You can see a video about this work here.|
- Counterfactual Prediction Under Outcome Measurement Error Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2023 [PDF]
- Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2023 [PDF]
- TigerAware: An Innovative Mobile Survey and Sensor Data Collection and Analytics System IEEE DSC, 2018
- ADA - Automatic Detection of Alcohol Usage for Mobile Ambulatory Assessment IEEE SmartComp, 2016
Workshops & Preprints
- Towards a Learner-Centered Explainable AI ACM CHI 2022 Workshop on Human-Centered Explainable AI (HCXAI), 2022 [PDF]
- Counterfactual Decision Support Under Treatment-Conditional Outcome Measurement Error NeurIPS 2022 Workshop on Causality for Real-world Impact, 2022
- Ground(less) Truth: The Problem with Proxy Outcomes in Human-AI Decision-Making NeurIPS 2022 Workshop on Human-Centered AI (HCAI), 2022
- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction MICCAI Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge, 2019 [PDF]