Gianvito Urgese

Assistant Professor

Politecnico di Torino

Italy

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Gianvito Urgese is an Assistant Professor at Politecnico di Torino, Italy. His research areas include neuromorphic hardware applications for constraint satisfaction problems, quantum annealing optimizations, and life cycle management in Industry 4.0. He has contributed to advancements in AIoT applications, additive manufacturing processes, and bioinformatics tools, demonstrating a strong focus on innovative engineering and computational solutions.

Articles (10)

Year:

2025

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2025

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2022

Year:

2022

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2021

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2021

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2021

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2020

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2020

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2020

Collaborators (7)

Xavier Bofill De Ros

Aarhus University

DENMARK

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Michael Hackenberg

Associate Professor (Profesor Titular)

Universidad de Granada

SPAIN

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Isidore Rigoutsos

Professor and Director

Thomas Jefferson University

UNITED STATES

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Jan van Deventer

Assistant professor

Luleå University of Technology

SWEDEN

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Jerker Delsing

Lulea University of Technology

SWEDEN

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Thomas Desvignes

Assistant Professor

University of Alabama, Tuscaloosa

UNITED STATES

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Eric Londin

Assistant Professr

Thomas Jefferson University

UNITED STATES

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Gianvito Urgese

Research Interests

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About

Articles (10)

Efficient Solution Validation of Constraint Satisfaction Problems on Neuromorphic Hardware: The Case of Sudoku Puzzles

Path Integral Quantum Annealing Optimizations Validated on 0-1 Multidimensional Knapsack Problem

A SOA-Based Engineering Process Model for the Life Cycle Management of System-of-Systems in Industry 4.0

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

PageRank Implemented with the MPI Paradigm Running on a Many-Core Neuromorphic Platform

Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions

Flexible On-Line Reconfiguration of Multi-Core Neuromorphic Platforms

Unification of miRNA and isomiR research: The mirGFF3 format and the mirtop API

BioSeqZip: a collapser of NGS redundant reads for the optimization of sequence analysis

DEEPrior: a deep learning tool for the prioritization of gene fusions

Collaborators (7)

Xavier Bofill De Ros

Michael Hackenberg

Isidore Rigoutsos

Jan van Deventer

Jerker Delsing

Thomas Desvignes

Eric Londin

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