Hamed Karimi wins ECML PKDD Test-of-Time Award

Amazon senior applied scientist Hamed Karimi and coauthors Julie Nutini and Mark Schmidt won the 2026 ECML PKDD Test-of-Time Award for their 2016 paper on linear convergence of gradient and proximal-gradient methods under the Polyak–Łojasiewicz condition. The paper showed that the largely overlooked 1963 inequality can prove fast linear convergence for standard training methods even when many solutions work equally well, mapped how previously scattered weaker-condition results relate, and extended the analysis to nonsmooth landscapes and algorithms including coordinate, sign gradient, and stochastic gradient descent. It has been cited over 2,000 times and is used in graduate textbooks.