climate informatics / human-centered AI / science studies
I study how machine learning enters climate science: not only as a technical instrument, but as a sociotechnical practice shaped by data work, expert judgment, institutional infrastructures, and the questions scientists are able to ask.
I am a fourth-year Ph.D. candidate in the Department of Computer Science at the University of Toronto, supervised by Professor Steve Easterbrook.
- climate model interpretation
- mixed-methods research
- explainable AI
- scientific workflows
- responsible data practices
My dissertation brings together quantitative climate and machine learning analyses with qualitative studies of climate scientists' lived experiences. Through this mixed-methods approach, I examine where prevailing ML framings align with, strain against, or overlook scientists' situated needs in practice.
Before Toronto, I completed Bachelor’s degrees in Computer Science and Mathematical Statistics with honors at Wake Forest University, followed by an MSc in financial technology at the Hong Kong University of Science and Technology.
news
| Jan 16, 2025 | My first-authored paper, CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Prediction of Cancer Treatment-Induced Cardiotoxicity, was accepted to CHI 2025. |
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| Apr 12, 2024 | Received Schwartz Reisman Institute for Technology and Society Fellowship |
| Mar 30, 2024 | Our paper “Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework” was accepted to FAccT 2024 |
| Mar 21, 2024 | Our paper “Bridging the Usability Gap: A Research Agenda for Enhancing Climate Information and Communication” was accepted to Sustaining Scalable Sustainability CHI 2024 workshop. |
| Dec 15, 2023 | Presented our work “Regional Studies of Multimodel Ensemble of Climate Projections for Enhanced Interpretability and Performance using Machine Learning Approaches” at AGU 2023. |