1. Combining Explainable Artificial Intelligence (Xai) With Blockchain Towards Trustworthy Data-Driven PoliciesLatest

   Published in 2025 under IEEE

   The paper proposes a data-driven policy-making approach that combines Explainable AI with blockchain to both clarify AI outputs and ensure their trustworthiness.

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2. UI/UX Sustainable Design: Best Practices for Applications CO2 Emissions Reduction

   Published in 2024 under IEEE

   This paper presents best practices for designing environmentally sustainable UX/UI in web applications, demonstrating through evaluation that such practices reduce energy consumption and carbon emissions.

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3. A Visual Programming Environment for Describing Complex Big Data Functions

   Published in 2023 under ACM

   This study introduces Pipeline Modeler, a Visual Programming Environment that translates graphical workflows into executable operations for big data scenarios, making complex pipeline management more accessible while supporting scalable deployment.

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4. AI4Gov: Trusted AI for Transparent Public Governance Fostering Democratic Values

   Published in 2023 under IEEE

   This paper proposes a holistic ecosystem that integrates Trustworthy and Explainable AI, Big Data, IoT, and blockchain to ensure transparency, accountability, and fairness in AI-driven public governance and policy-making.

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5. MathBlock: Performing Complex Mathematical Operations on Synthetic Data

   Published in 2023 under IEEE

   This paper presents MathBlock, a language-agnostic mathematical expression parser and executor for batch data that simplifies arithmetic, logical, comparison, and statistical operations across diverse datasets.

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6. Diastema: data-driven stack for big data applications management and deployment

   Published in 2023 under Inderscience Publishers

   This paper introduces Diastema, a data-driven stack for big data management and deployment that delivers efficient data-as-a-service through distributed storage and analytics, demonstrated via a customer forecasting use case.

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7. Data Processing Tools for Graph Data Modelling Big Data Analytics

   Published in 2022 under IEEE

   This paper presents an event-based, low-code data processing architecture with network-exposed services and scalable “Data Workers” that automate common Big Data tasks while minimizing user input.

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8. A Comparison of Container Systems for Machine Learning Scenarios: Docker and Podman

   Published in 2022 under IEEE

   This paper benchmarks Docker and Podman, showing that while both offer standardized container runtimes, Docker outperforms slightly in performance and cost efficiency for cloud deployments.

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9. An Environmentally-sustainable Dimensioning Workbench towards Dynamic Resource Allocation in Cloud-computing Environments

   Published in 2022 under IEEE

   This paper presents a machine learning–based dynamic resource allocation model for cloud environments that leverages user feedback to optimize scalability and application performance in Big Data scenarios.

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10. An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics.

    Published in 2022 under IOS Press

    This paper introduces Diastema, a domain-agnostic autoscaling Big Data analytics platform that provides user-friendly graph-based modeling for both technical and non-technical users, validated through a COVID-19 healthcare prediction case study.

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publications.js

const publications = [
  {
    title: "How to Write a Paper That Nobody Will Read",
    authors: ["Me", "My Imposter Syndrome", "Coffee"],
    year: 2023,
    abstract: "A groundbreaking study on the art of academic writing that results in papers with 0 citations but 100% existential dread.",
    citations: 0,
    readTime: "2 hours (mostly staring at the wall)",
  },
];

// Function to check if anyone actually read my papers
const checkReadership = (paper) => {
  return paper.citations > 0 ? "Someone read it! 🎉" : "Just me and my mom... 📚";
};

publications.forEach(paper => {
  console.log(`📄 ${paper.title}`);
  console.log(`📊 ${checkReadership(paper)}`);
  console.log(`⏱️  Reading time: ${paper.readTime}
`);
});