Projects

Variational inference for robust Gaussian process regression

We developed a scalable approximate inference algorithm for robust Gaussian process regression with contaminated normal noise. Initial experiments with simulated and actual data are promising.

Daniel Iong, Yang Chen

Variational inference for robust Gaussian process regression

Explainable ML for space weather forecasting

We trained gradient boosted trees to forecast the SYM-H index several hours ahead using solar wind data and used SHAP values to explain predictions.

Daniel Iong, Yang Chen, Gabor Toth, Shasha Zou, Tuija I. Pulkkinen, Jiaen Ren, Enrico Camporeale, Tamas I. Gombosi

Explainable ML for space weather forecasting

Modeling heterogeneous causal mechanisms in epidemiology with observational data

We introduced the notion of ‘mechanistic heterogeneity’ in Mendelian Randomization, developed a latent mixture method to model it, and used it to estimate heterogeneous causal effects between HDL cholesterol and coronary heart disease.

Daniel Iong, Qingyuan Zhao, Yang Chen

Modeling heterogeneous causal mechanisms in epidemiology with observational data