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Trevor Harris

I am an Assistant Professor of Statistics at the University of Connecticut. I work on uncertainty quantification, statistical learning, and methods for spatial and functional data, with applications in climate science and epidemiology.

Much of my research asks how we can assess the uncertainty in a model's predictions and use those predictions to learn about a scientific system. Recent work studies conformal prediction for functions and spatial events, the evaluation of climate models, and forecasting from irregular monitoring networks.

Before joining UConn in 2024, I was an Assistant Professor at Texas A&M University. I received my PhD in Statistics from the University of Illinois Urbana-Champaign.

Research

Uncertainty quantification

I develop conformal methods for predictions that take the form of functions, fields, trajectories, or collections of spatial events. My recent work uses flows to sample from conformal prediction regions and construct predictive distributions, making these regions easier to represent and use.

Climate model evaluation and integration

I study how climate simulations differ from observations and how the two can be combined to improve projections. This includes distributional comparisons using optimal transport, methods for separating timing and amplitude errors, and conformal ensembles for quantifying projection uncertainty.

Spatial dependence and environmental risk

I also work on dependence and extremes in environmental data, including teleconnections and compound climate risk. In epidemiological collaborations, I develop graph models for West Nile virus forecasting and study the effects of mosquito-control interventions.

Conformal climate projections for temperature and precipitation — Temperature and precipitation projections from conformal ensembles and conventional ensemble methods.

Map of estimated extremal teleconnection structure — Estimated tail dependence across the globe, used to study connections between extreme events.

Selected papers

Flow-Based Conformal Predictive Distributions (2026). NeurIPS, accepted. Code
Decision-Focused Calibration with Adaptive Conformal Predictive Distributions (2026). ICLR, under review.
Manifold Constrained Conformal Prediction for Spatial Events (2026).
Locally Adaptive Conformal Inference for Operator Models (2025). Code
Statistics for Climate Science: Differentiation and Integration (2026). Annual Review of Statistics and Its Application.
Quantifying Uncertainty in Climate Projections with Conformal Ensembles (2024). Code
Validating Climate Models with Spherical Convolutional Wasserstein Distance (2024). Code
Forecasting West Nile Virus with Deep Graph Encoders (2025).

See the full publication list for all papers and their current status.