Research
My work bridges statistics and machine learning, with a focus on developing principled methods that make data a more powerful storyteller. I am particularly interested in problems where classical statistical thinking can meaningfully improve modern AI systems.
Mixture Reduction
Approximating large or growing mixture models with simpler ones, with applications to federated learning and 3D Gaussian Splatting compaction.
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Empirical Likelihood in Modern AI
Modernizing classical empirical likelihood for federated learning and noisy label settings, shifting FL from aggregation to intelligent guidance.
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Tabular Foundation Models
Evaluating and extending prior-fitted networks (PFNs) for in-context statistical learning — from supervised prediction to clustering.
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