activity
20222026
most citedOSCAR-P and aMLLibrary: Profiling and Predicting the Performance of FaaS-based Applications in Computing Continua

5 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications

Alberto Tagliaferro, Bruno Guindani, Livia Lestingi +1

Early-stage specifications of safety-critical systems are typically expressed in natural language, making it difficult to derive formal properties suitable for verification and nee…

cs.SE2026

Automated Detection and Mitigation of Dependability Failures in Healthcare Scenarios through Digital Twins

Bruno Guindani, Matteo Camilli, Livia Lestingi +1

Medical Cyber-Physical Systems (CPSs) integrating Patients, Devices, and healthcare personnel (Physicians) form safety-critical PDP triads whose dependability is challenged by syst…

cs.PF2024★ 5 cited

OSCAR-P and aMLLibrary: Profiling and Predicting the Performance of FaaS-based Applications in Computing Continua

Roberto Sala, Bruno Guindani, Enrico Galimberti +6

This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibra…

cs.DC2024

Efficient Parameter Tuning for a Structure-Based Virtual Screening HPC Application

Bruno Guindani, Davide Gadioli, Roberto Rocco +2

Virtual screening applications are highly parameterized to optimize the balance between quality and execution performance. While output quality is critical, the entire screening pr…

stat.CO2022

BayesMix: Bayesian Mixture Models in C++

Mario Beraha, Bruno Guindani, Matteo Gianella +1

We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform infe…