Alexandre Belloni works across optimization, econometrics and high-dimensional statistics. His Chicago Booth profile describes research connecting statistical inference with decisions under limited information.[ref]
With Victor Chernozhukov and Lie Wang, he formulated the Square-root Lasso as a convex conic optimization problem. This connects a statistically pivotal tuning rule with algorithms for convex optimization, giving a computable sparse regression estimator without a preliminary estimate of noise scale.[ref]
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming by Alexandre Belloni and others. Abstract and section 1, optimization formulation
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