Remember this: Try.

I build end-to-end AI systems (multi-agent RAG, computer vision, LLM fine-tuning) and currently complete an MSc in Applied Data Science & AI @ OPIT. Background in industrial B2B consulting, so I care about systems that ship and stakeholders who are not engineers.

_{There are 10 types of people in the world: those who understand binary and those who don't.}

Automated malaria parasite detection and classification using YOLOv11 segmentation across 17 classes (4 species × 4 life stages). Microscopic blood smear images pass through an OpenCV preprocessing pipeline, reaching 62% mAP50 on multi-class segmentation. Full workflow: dataset conversion, augmentation, training with checkpoint management, real-time clinical inference.

Python PyTorch YOLOv11 OpenCV Google Colab Ultralytics → repo

Multi-agent RAG chatbot for Alzheimer's care, built on FastAPI + Milvus + Redis. Adapts tone and terminology for three user roles (patient, caregiver, doctor) across four interface languages. Full-stack: Next.js frontend, multi-agent pipeline (RAGAgent then SupervisorAgent), web and Telegram channels, and an optional fully offline deployment via Ollama.

Python FastAPI Next.js Milvus Redis RAG LLM Docker → repo

Local, privacy-first multi-agent system that turns a raw business idea into a structured business plan for Italian micro-businesses. Six specialist agents run under a custom Orchestrator applying 6 LLM-based consistency checks with targeted revision cycles. The distinctive part: a pure-Python verification layer re-derives unit margin, break-even and capital totals with no model in the loop, because computable ground truth should be computed, not delegated to a model. Charts are deterministic Plotly code from structured specs, never LLM-rendered.

Python Multi-Agent LM Studio Ollama Plotly XLSX → repo

Computer vision and deep learning system for Italian Sign Language (LIS). End-to-end ML pipeline: FFmpeg-based video augmentation, MediaPipe Holistic for 543-keypoint extraction, and a two-tier architecture. Static fingerspelling recognition (MLP / 1D-CNN) reaches 87.3% accuracy at 30+ FPS. Word-level recognition was explored as a research track using sequence models (Bi-LSTM, Transformer) and LLM-based gloss-to-Italian refinement (Gemma via LM Studio).

Python PyTorch TensorFlow MediaPipe OpenCV Bi-LSTM Transformer FFmpeg → repo

A map of the deep learning architectures and ML systems I've applied across real projects, not tutorials but working pipelines.

class WhoAmI:
    user = 'Vicio Di Cara'
    role = 'AI Engineer'
    based_in = 'Palermo, Italy'
    birth_fact = 'The first internet ping in Italy coincides with my birth'

    studying = 'MSc in Applied Data Science & AI @ OPIT'

    ai_portfolio = [
        "🥇 Malaria AI Scope: YOLOv11 segmentation, 1st Place AWS University Engagement Program",
        "🥈 Sherpa Alzheimer: multi-agent RAG chatbot, 2nd Place OPIT Hackathon 2026",
        "📊 Business Plan AI: 6-agent orchestrator with deterministic financial verification",
        "🤟 LisAI Interpreter: real-time LIS recognition (MLP/CNN letters 87.3%, Bi-LSTM/Transformer words)",
        "✅ Mental D0C: Qwen3 fine-tuned for mental health assessment (Unsloth)",
        "✅ Drug Repositioning: Random Forest + K-means, 92% accuracy",
        "🚀 Building AI agents to help others",
    ]

    hobbies = [
        "Surfing",
        "Bouldering",
        "Coding for Good",
    ]

    def get_city():
        return "Palermo, Italy"

    def future_ambitions():
        return "To travel the world, programming to help others."