Antonio Pelusi

Ph.D. student in Computer Science and Mathematics of Computation

Summary

Ph.D. student and software developer focused on Differential Privacy, synthetic data generation, and privacy-preserving Machine Learning systems.

Combines theoretical privacy research with practical system development, including production-grade random number platforms and standardized entropy distribution APIs.

Academic credentials include a Master's degree with distinction, CERN research collaboration, and an ongoing industry-academia partnership investigating the impact of true randomness on privacy mechanisms.

Specialized in bridging fundamental research with enterprise-grade technological solutions.

Experience

Random Power (Startup)

May 2024 - Nov. 2025 Full Stack Developer Remote
  • Developed a comprehensive Django-based web client (backend and frontend) for Random Power's 64x Multi-Generator Board quantum hardware platform, enabling secure multi-user access with authentication and session management to true quantum randomness generation.
  • Built Palo Alto QRNG API-compliant Entropy-as-a-Service platform supporting 1Gbit/s quantum random bit distribution with automated quality assurance and real-time compliance monitoring.
  • Collaborated in the design and development of scheduler and TEE components for secure random bit distribution.
  • Designed and maintained communication interfaces between hardware and software components, ensuring reliable and secure data exchange.

A. Paradisi High School

Sep. 2024 - Jul. 2025 Computer Science High School Teacher Vignola, Italy
  • Introduced 75+ high school students to programming, algorithms, and fundamental computer concepts using practical exercises and projects.

University of Modena and Reggio Emilia

Sep. 2024 - Dec. 2024 Computer Science University Tutor Modena, Italy
  • Conducted laboratory sessions, guiding 50+ undergraduate students in programming, algorithms, and data structures.
  • Provided hands-on support during labs, assisted with debugging, and facilitated practical understanding of theoretical concepts.

Liferay Inc.

Mar. 2021 - Jul. 2021 Junior Full Stack Developer (Internship) Remote
  • Integrated Stripe payment functionality into the open-source Liferay Portal.

Education

Ph.D. in Computer Science and Mathematics of Computation

Nov. 2024 - present University of Insubria - Random Power
  • Researched Differential Privacy frameworks analyzing how quantum vs. pseudo-random number generation impacts privacy mechanism effectiveness.

Master's Degree in Computer Science

Sep. 2022 - Apr. 2024 University of Modena and Reggio Emilia 110/110
  • Big Data & Machine Learning: Focus on predictive modeling, large-scale data analysis, and advanced machine learning methods.
  • Cybersecurity & Cryptography: Exploration of theoretical and applied aspects of data security and cryptographic protocols.

Post Graduate Degree CBI.Attract

Feb. 2023 - Jun. 2023 University of Bologna - CERN - AGH University 30/30 with Honors
  • Participated in research collaborations at CERN and AGH University, funded by the EU Attract Project, contributing to joint investigations on emerging technologies.
  • Explored technological opportunities through Design Thinking-based prototyping and testing.

Bachelor's Degree in Computer Science

Sep. 2018 - Apr. 2022 University of Modena and Reggio Emilia
  • Computer Science Foundations: Algorithms, data structures, programming languages, computer architecture, and operating systems.
  • Mathematical Foundations: Discrete mathematics, calculus, statistics, and linear algebra.

Publications

Projects

epsiloc-lib

 Oct. 2025 Local Differential Privacy library
  • Designed and implemented a library for applying Local Differential Privacy mechanisms to local databases.

ToolsTab

 Jan. 2025 Startpage browser extension
  • Developed a minimal and responsive browser startpage with useful tools.

Awards

Winner of Green & Digital Transformation Hackathon

Sep. 2023 University of Modena and Reggio Emilia Mantua, Italy
  • Winner of a hackathon with a machine learning-based predictive model for water consumption optimization.

Skills

Back-end:

Front-end:

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