Sam Davanloo Tajbakhsh

Sam Davanloo Tajbakhsh works on mathematical optimization methods, particularly stochastic (randomized) algorithms for solving large-scale optimization problems that arise in statistics and machine learning. His work includes developing and analyzing algorithms for optimization on curved spaces (Riemannian manifolds), problems with noisy or approximate information (inexact oracles), and statistical models with structured sparsity, such as time series models. Prospective students would likely engage with theoretical analysis of algorithm convergence rates alongside applications in statistical learning.

Publication output began around 2020 and has continued at a modest, fairly steady pace of one to a few papers per year through 2024.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026