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Marco Calenda

Full Stack Engineer @ Blue Reply

Professional Skills

Technical Skills

Python
Advanced
Java
Advanced
HTML & CSS
Expert
JavaScript / TypeScript
Expert
C
Intermediate

Soft Skills

Project Management
Expert
Leadership
Expert
Teamwork
Expert
Problem Solving
Advanced
Communication
Advanced

Work Experience

Full-stack Developer @ Blue Reply - Milan

February, 2024 - Present
  • Designed and implemented web and mobile native solutions using Angular and Flutter.
  • Collaborated with team members specializing in interfaces and accessibility.

Education

MSc in Computer Science 110/110 cum laude

University of Salerno (LM-18) | 2021 - 2023

Curriculum: Software Engineering for IT Management

  • Software Project Management
  • Artificial Intelligence and NLP
  • Software Maintenance and Evolution
  • Software Engineering for AI

Thesis: "Exploring the potential of quantum NLP for non-functional requirements classification."

BSc in Computer Science 93/110

University of Salerno (L-31) | 2016 - 2020

Focused on:

  • Logic, Mathematics, and Statistics
  • Algorithms and Data Structures
  • Computer Networks
  • Operating Systems

Thesis: "Verismart 3.0: Reengineering and Unification of LazyCseq and Verismart."

Portfolio

FundMeNow
FundMeNow

Solidity / ReactJS / Truffle

FundMeNow
FundMeNow is a decentralized crowdfunding platform based on Ethereum blockchain developed using Truffle and ReactJS for the Data Security course at @Unisa.

Click to view on GitHub
Winery
Winery

NodeJS / MongoDB

Winery
Winery is a web app developed for the Database II course at @Unisa using NodeJS, EJS, and MongoDB. It allows querying a database containing information about wines from all over the world.

Click to view on GitHub
HeartCare
HeartCare

Java Spring / ReactJS / Project Management

HeartCare
HeartCare is a web app developed for the Software Project Management course using Spring, ReactJS, and MySQL, where I played the role of project manager.

Click to view on GitHub
Quantum NLP
Quantum NLP for NFRs Classification

PyTorch / PennyLane / Lambeq

Quantum NLP
Research study conducted for the Software Engineering for Artificial Intelligence course and for my Master Thesis. The research aims to assess the classification capabilities of a quantum NLP model compared with shallow ML models.

Click to view on GitHub

Contact

+39 3202689148
marco.calenda14@gmail.com