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 reliability and validity of data-driven algorithms deployed in high-stakes decision-making settings. I develop algorithmic methods and evaluation tools to help practitioners assess the feasibility of model deployments given real-world complexities. I adopt an interdisciplinary approach, leveraging methods from HCI, machine learning, and causal inference. My work is generously supported by an NSF Graduate Research Fellowship and the Center for Advancing Safety of Machine Intelligence (CASMI).
Previously, I completed 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
Feb 2024 | I will give an invited talk “Human-Algorithm Decision-Making Under Imperfect Proxy Labels” at the 2024 Lecture Series on Network Inequality at CSH Vienna. |
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Dec 2023 | I will present ongoing work at the NeurIPS Regulatable ML Workshop on Saturday December 16th. |
Jun 2023 | Our work Counterfactual Prediction Under Outcome Measurement Error won a Best Paper Award at FAccT 23’. |
Apr 2023 | Two papers accepted at FAccT 23’. |
Conference Papers
- Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge Proceedings of The ACM Collective Intelligence Conference, 2023
- Federated Continual Learning for Socially Aware Robotics Proceedings of the 32nd IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 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
- Policy Comparison Under Unmeasured Confounding NeurIPS 2023 Workshop on Regulatable ML (RegML), 2023
- Ground(less) Truth: The Problem with Proxy Outcomes in Human-AI Decision-Making NeurIPS 2022 Workshop on Human-Centered AI (HCAI), 2022
- Counterfactual Decision Support Under Treatment-Conditional Outcome Measurement Error NeurIPS 2022 Workshop on Causality for Real-world Impact, 2022
- Towards a Learner-Centered Explainable AI ACM CHI 2022 Workshop on Human-Centered Explainable AI (HCXAI), 2022 [PDF]
- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction MICCAI Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge, 2019 [PDF]