Harvard’s Statistics PhD propels methodologists who craft robust inference for AI, genomics, and climate science. Students design causal forests, tighten concentration inequalities, and release open-source probabilistic programming libraries, shaping analytics in academia and industry.
Non-parametric Bayesian model for multimodal sensor fusion
Conformal prediction intervals for large language models
Differential-privacy methods for spatial epidemiology data
Causal discovery algorithms benchmarking toolkit
Deep generative models with uncertainty calibration
High-dimensional asymptotics of sparse PCA
Open-source Julia package for scalable MCMC
Thesis on fairness metrics in sequential decision processes
Visualization suite for communicating statistical uncertainty
Workshop on reproducible research using Quarto and renv
Build the mathematics powering trustworthy data science.
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