root@hub:~/cv$ man ludovico_de_salvo

[cd ..]
[ ADMIN ]

LUDOVICO DE SALVO

AI Engineer & Management Engineer

SYS.LOC: Rome, ItalySYS.MAIL: ludovicodesalvo@proton.meSYS.NET: linkedin.com/in/ludovicodesalvoSYS.REPO: github.com/LudovicoDeSalvo
uid=0000

## ABOUT_ME

AI & Robotics Master’s student with a solid background in Management Engineering. This dual perspective defines my approach: building systems that work not only mathematically, but that solve real-world problems efficiently. My goal is to bridge the gap between advanced Deep Learning research and practical Business Optimization.

## TOP_SKILLS

Machine LearningDeep LearningReinforcement LearningComputer VisionPyTorchNLP

## EXPERIENCE

AI Engineer

[ 2026 - Present ]
Gallelli Antincendio SRL, Rome, Italy
  • Developing Retrieval-Augmented Generation (RAG) systems for internal use cases.
  • Managing and maintaining server infrastructure.
  • Building satellite-based vehicle tracking features.

Webmaster

[ 2026 - Present ]
Fondazione Magna Graecia ETS (website in development)

Designing and building the foundation’s website using Next.js, Tailwind CSS, Sanity CMS, and Firebase.

## PROJECTS

Bio-Adaptive Music Engine (BAME): Closed-Loop RL System
  • Developed a Deep Reinforcement Learning framework designed to regulate human physiological states (arousal/stress) through music.
  • Treated human physiology as a control problem, engineering a proprietary 'World Model' to simulate biological reactions.
  • Trained an agent to optimize physiological outcomes without real-time human risk.
End-to-End License Plate Recognition Pipeline
  • Engineered a modular pipeline for real-time license plate detection and recognition on the CCPD2019 dataset.
  • Orchestrated a full end-to-end workflow: Data ingestion -> YOLOv Detection -> Cropping/Alignment -> Recognition -> Inference.
  • Implemented and compared a baseline CNN+LSTM+CTC against a Transformer-based PDLPR (Parallel Decoder) model.
OCR Post-Correction with Fine-Tuned LLMs
  • Developed a portable pipeline to correct noisy OCR text outputs using Parameter-Efficient Fine-Tuning (PEFT) on LLMs.
  • Implemented LoRA and 8-bit quantization to fine-tune Llama and Minerva models on consumer hardware.
  • Designed an 'LLM-as-a-Judge' automated evaluation system using the Gemini API, validated against human scoring via correlation analysis.
ReMERT: Enhanced Deep Q-Learning via Experience Replay
  • Built a PyTorch implementation of Deep Q-Networks (DQN) enhanced with custom ReMERT (Replay Memory with End-Related Transitions).
  • Replaced uniform experience replay with a prioritized sampling strategy based on inverse distance to terminal states.
  • Significantly improved sample efficiency and convergence speed in the CartPole-v1 environment.
./VIEW_ALL_PROJECTS.sh

## EDUCATION

Master Student in Artificial Intelligence & Robotics

[ 2024 - Present ]
Sapienza University, Rome, Italy

English Master Degree. Relevant already completed courseworks: Machine Learning, Deep Learning, Computer Vision, Reinforcement Learning, Neural Networks, Multilingual Natural Language Processing (MNLP).

Bachelor Degree in Management Engineering (L-8)

[ 2017 - 2024 ]
Università della Calabria, Italy

## ADDITIONAL EXPERIENCE

Waiter

[ 2019 - 2020 ]
“Ristorantino”, Trebisacce, Italy

Developed strong time management skills and ability to work under pressure in a fast-paced environment.

## DETAILED_SKILLS

> Concepts

Artificial IntelligenceDeep LearningReinforcement LearningComputer VisionNatural Language Processing (NLP)Neural NetworksLarge Language Models (LLM)Retrieval-Augmented Generation (RAG)Convolutional Neural Networks (CNN)Long Short-Term Memory (LSTM)Transformer ModelsLLM fine-tuning

> Tools & Systems

LinuxDockerGitCLIJupyterRobot Operating System (ROS)KatharaMicrosoft Office (Word, Excel, PowerPoint)Android (ADB, Custom ROMs, Fastboot)PC Building & Hardware Assembly

> Languages

PythonJavaC++TypeScriptJavaScriptMATLABHTMLCSSPlanning Domain Definition Language (PDDL)

> Frameworks & Libs

PyTorchNumPyMatplotlibOpenCVYOLOFAISSMERTOpenAI GymGoogle GeminiNext.jsTailwind CSSSanity CMSFirebase

## LANGUAGES

Italian:Native (C2 level)
English:Full Professional proficiency (C1 level with IELTS certification)
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