Damiano Verda

Head

Italy

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About

Damiano Verda is the Head at Rulex in Italy. His research encompasses various areas including machine learning applications in healthcare, financial modeling, and explainable AI. Recent articles highlight his work on biomarkers for multiple sclerosis, asset allocation strategies, and enhancing explainability in AI models. His contributions aim to advance the application of data-driven techniques across diverse domains.

Articles (15)

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2025

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2022

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2022

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2022

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2022

Collaborators (7)

Pier Giuseppe Giribone

Adjunct Professor

University of Genoa

ITALY

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Rosanna Asselta

Full Professor in Medical Genetics

Humanitas University

ITALY

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Alessio Gerussi

Lecturer

University of Milano-Bicocca

ITALY

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dr. Luca Longo

lecturer

Technological University Dublin

IRELAND

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Ana LLEO

Professor of Internal Medicine

Humanitas University

ITALY

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Davide Paolo Bernasconi

University of Milano-Bicocca

ITALY

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Lucas Rizzo

Assistant Lecturer

Technological University Dublin

IRELAND

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Damiano Verda

Research Interests

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Articles (15)

Enhancing the explainability of the default probability model using the logic learning machine: A comparison between native “white boxes” machine learning techniques

Optimal asset allocation using visual programming techniques: A quantitative analysis based on an ESG portfolio

Splicing-based biomarkers define a robust multigene classifier for relapsing-remitting multiple sclerosis

Alternative Support Threshold Computation for Market Basket Analysis

Explainable evaluation of generative adversarial networks for wearables data augmentation

A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AI

Deep Learning helps discriminating Autoimmune Hepatitis and Primary Biliary Cholangitis

Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control

Using Stratified Sampling to Improve LIME Image Explanations

Optimizing Water Distribution through Explainable AI and Rule-Based Control

Validation of a predictive model for obstructive sleep apnea in people with Down syndrome

LLM-PBC: Logic Learning Machine-Based Explainable Rules Accurately Stratify the Genetic Risk of Primary Biliary Cholangitis

Machine learning in primary biliary cholangitis: A novel approach for risk stratification

Artificial intelligence for precision medicine in autoimmune liver disease

The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers

Collaborators (7)

Pier Giuseppe Giribone

Rosanna Asselta

Alessio Gerussi

dr. Luca Longo

Ana LLEO

Davide Paolo Bernasconi

Lucas Rizzo

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