LUDOVICO DE SALVO
AI Engineer & Management Engineer
## 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
## 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.
## EDUCATION
Master Student in Artificial Intelligence & Robotics
[ 2024 - Present ]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 ]## EXPERIENCE
Waiter
[ 2019 - 2020 ]Developed strong time management skills and ability to work under pressure in a fast-paced environment.
## DETAILED_SKILLS
> Concepts
> Tools & Systems
> Languages
> Frameworks & Libs
## LANGUAGES
Ludovico De Salvo
AI Engineer & Management Engineer
Profile Summary
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 solve real-world problems efficiently. My goal is to bridge the gap between advanced Deep Learning research and practical Business Optimization.
Technical Engineering Experience
Bio-Adaptive Music Engine (BAME): Closed-Loop RL System
Developed a Deep Reinforcement Learning framework regulating 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 CCPD2019. Orchestrated full workflow: Data Ingestion → YOLOv Detection → Cropping/Alignment → Recognition → Inference. Implemented and compared baseline CNN+LSTM+CTC against a Transformer-based Parallel Decoder (PDLPR) model.
OCR Post-Correction with Fine-Tuned LLMs
Developed a portable pipeline to correct noisy OCR text outputs using 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.
ReMERT: Enhanced DQN via Prioritized Experience Replay
Built a PyTorch implementation of Deep Q-Networks (DQN) enhanced with custom ReMERT (Replay Memory with End-Related Transitions), replacing uniform experience replay with prioritized sampling based on inverse distance to terminal states. Improved sample efficiency in CartPole-v1.
Education
M.Sc. in Artificial Intelligence & Robotics
2024 - PresentEnglish Master Degree. Completed coursework: Machine Learning, Deep Learning, Computer Vision, Reinforcement Learning, Neural Networks, Multilingual NLP.
B.Sc. in Management Engineering
2017 - 2024Additional Experience
Waiter
2019 - 2020Developed strong time management skills and ability to work under pressure in a fast-paced environment.