Nicola Bastianello

KIST Medical College, University of Padua

Papers

Total Citations

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About

Nicola Bastianello is a researcher specializing in online optimization, signal processing, and machine learning, with a particular focus on developing efficient algorithms for time-varying and dynamic optimization problems. His work addresses the fundamental challenge of solving optimization problems in real time as data streams continuously evolve, a critical need in modern signal processing and machine learning applications. Bastianello's most notable contributions center on prediction-correction methods — algorithmic frameworks that anticipate changes in optimization landscapes and efficiently adapt solutions as conditions shift. His 2023 paper on extrapolation-based prediction-correction methods tackles online convex optimization problems driven by streaming data, advancing both primal and dual algorithmic perspectives. Building on earlier foundational work, his 2019 paper introduced prediction-correction splitting techniques for nonsmooth time-varying problems, elegantly handling objective functions composed of strongly convex and nonsmooth components — a practically important and mathematically challenging setting. Together, these works have accumulated nearly 20 citations, reflecting growing interest from the optimization and signal processing communities. His research equips practitioners with principled, theoretically grounded tools for tackling dynamic, real-world optimization scenarios, making his contributions particularly relevant to researchers working at the intersection of online learning, control systems, and large-scale data processing.