Rubin–Neyman Causal framework for Probability Measures
A mathematical view of causal inference through lens of functionals.
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This page contains archived lecture notes and research ideas in Statistics, Machine Learning, and Generative AI. All opinions are my own and do not represent any affiliated organizations.
A mathematical view of causal inference through lens of functionals.
How synthetic negative controls give finite-sample FDR control for variable selection.
How reweighting connects importance sampling, causal adjustment, clinical trial comparison, class-imbalance remedies, coreset selection, and deep generative ...
How survival analysis evolved from censored-time likelihoods to generative AI survival models.
Transforming probability distributions through geometry, entropy, and variational optimality
Defining Bayesian uncertainty without the prior and likelihood
Distribution-free uncertainty quantification in the age of Deep Learning
The most important statistical-physics principle in the age of Generative AI