Best Paper Award at MASCOTS 2024 for 'd-MALIBOO: a Bayesian Optimization framework for dealing with Discrete Variables'.
Listed in the World’s Top 2% Scientists (October 2023, November 2022) based on standardized citation metrics.
Google Education Award, May 2022.
TETRAMAX Best Project Award, October 2021, for the ANDREAS project on power and cost management of deep learning workloads.
Best Paper Award at ICA3PP 2016 for work on Hadoop application performance modeling.
Received Top CompSci University Azure Adoption grants in October 2016, September 2017, and July 2018.
Served as General Co-Chair or PC Chair for multiple international workshops and conferences, including Fast Continuum (2025, 2023), SCADL (2022–2020), IFIP WG 7.3 Performance (2021, 2020), and QUDOS (2020, 2019).
Background
Research focuses on the design, prototyping, and evaluation of resource management algorithms for large-scale distributed systems supporting AI, big data, and web applications.
Aims to develop optimization algorithms for maximizing Quality of Service and managing resources in fog/edge/cloud infrastructures and HPC systems.
Recent work includes AI applications (coordinated the AI-SPRINT project), reinforcement learning, and performance analysis and optimization of large-scale systems and software.
Contributed to major projects such as MODAClouds, DICE, EUBRA-BIGSEA, ATMOSPHERE, and GAME-IT (focused on energy efficiency in virtualized infrastructures).
PhD research addressed cost-oriented design and capacity planning of distributed IT architectures.