🏄♂️ Vicio 
Automated malaria parasite detection & classification using YOLOv11 segmentation across 17 distinct classes (4 species × 4 life stages). Processes microscopic blood smear images through advanced OpenCV preprocessing, achieving 62% mAP50 on multi-class segmentation. Full pipeline: dataset conversion → augmentation → training with checkpoint management → real-time clinical inference.
Python PyTorch YOLOv11 OpenCV AWS Ultralytics
Multi-agent RAG chatbot for Alzheimer's care, built with FastAPI + Milvus + Redis. Adapts tone and terminology for three user roles (patient, caregiver, doctor) across 4 languages. Full-stack: Next.js frontend, multi-agent pipeline (RAGAgent → SupervisorAgent), Telegram & WhatsApp integration, and optional fully offline deployment via Ollama.
Python FastAPI Next.js Milvus Redis RAG LLM Docker
Real-time Italian Sign Language (LIS) recognition system bridging the accessibility gap. Engineered end-to-end ML pipeline: FFmpeg-based video augmentation, MediaPipe Holistic for 543-keypoint extraction, dual-model architecture (MLP/CNN for letters at 87.3% accuracy + Bi-LSTM/Transformer for words), and LLM-powered gloss-to-Italian translation (Gemma, Llama-3). Processes at 30+ FPS for letters, 15–20 FPS for words.
Python PyTorch TensorFlow MediaPipe OpenCV LSTM Transformer FFmpeg
A map of the deep learning architectures and ML systems I've applied across real projects — not tutorials, but production pipelines.
| Architecture | Project | Details |
|---|---|---|
| YOLOv11 Instance Segmentation | 🥇 Malaria AI Scope | 17-class parasite segmentation on microscopic blood smear images. Custom dataset pipeline with multi-format conversion (Masks/JSON/TXT → YOLO). Incremental learning support. 62% mAP50 |
| MediaPipe Holistic | 🎓 LisAI | Full-body landmark extraction: 468 face mesh + 33 pose + 42 hand points = 543 keypoints/frame. Spatial normalization relative to shoulder midpoint for scale/distance invariance |
| MLP / 1D-CNN (Static) | 🎓 LisAI | 63-dim input (21 hand landmarks × 3 coords). 3 Dense layers (256→128→64) + ReLU + Dropout(0.3). 87.3% accuracy on 26-class fingerspelling |
| OpenCV Preprocessing | 🥇 Malaria AI Scope | Advanced preprocessing pipeline for heterogeneous medical image datasets. Gaussian blur, brightness normalization, contour analysis |
| Architecture | Project | Details |
|---|---|---|
| Bidirectional LSTM (Bi-LSTM) | 🎓 LisAI | Sequence input: (T, 1662) feature matrix. Bidirectional layers let the model see future frames when classifying current state — critical for signs where the ending disambiguates the beginning |
| Transformer (Multi-Head Self-Attention) | 🎓 LisAI | Long-range dependency capture across full gesture sequences. Parallel frame attention vs. LSTM's sequential processing. Adam optimizer with decaying LR schedule |
| GRU | 🎓 LisAI | Evaluated alongside LSTM/Transformer as lighter alternative for temporal gesture modeling |
| Forward Padding + Masking | 🎓 LisAI | Variable-length sign sequences (20–180 frames) unified via Keras masking layer — ensures backpropagation ignores padded frames |
| Architecture | Project | Details |
|---|---|---|
| Multi-Agent RAG Pipeline | 🥈 Sherpa | Sequential: RAGAgent → SupervisorAgent. Score-threshold filtering (cosine sim ≥ 0.55) prevents hallucinated citations. Conversational fallback when no chunks pass threshold |
| LLM Fine-tuning (QLoRA) | ✅ Mental D0C | Fine-tuned Qwen3 with Unsloth on 12,000+ synthetic mental health conversations. Instruction-tuning for clinical assessment dialogue |
| Gloss-to-Natural Language (LLM post-processing) | 🎓 LisAI | Local LLM integration (Gemma, Llama-3 via Ollama/LM Studio) for: ① LIS gloss → Italian grammar ("IO MANGIARE MELA" → "Sto mangiando una mela") ② Ambiguity resolution via sliding context window |
| Beam Search Context Engine | 🎓 LisAI | Rule-based module combining model confidence scores + language frequency dictionary for most-likely word sequence decoding |
| Vector Similarity Search (Milvus) | 🥈 Sherpa | FLAT index, COSINE metric. 1024-dim embeddings. Top-k=5 retrieval with score-threshold gate. Snapshot system for portable pre-computed vector DB |
| Architecture | Project | Details |
|---|---|---|
| Random Forest Classifier | ✅ Drug Repositioning | 92% accuracy / precision / recall / F1 on 84,400-sample biomedical dataset for novel drug-disease association prediction |
| K-Means Clustering | ✅ Drug Repositioning | Identified 10 optimal semantic clusters in biomedical embedding space to surface hidden drug-disease patterns |
| Pattern | Project | Details |
|---|---|---|
| FFmpeg Video Augmentation Pipeline | 🎓 LisAI | Custom engine: geometric (H-flip for handedness swap, ±15° rotation, scale jitter) + temporal (0.8×–1.2× speed) + photometric (Gaussian blur, brightness) augmentation |
| Mixed Precision Training | 🎓 LisAI | FP16 training with checkpoint management for memory-efficient long-sequence model training |
| Incremental / Continual Learning | 🥇 Malaria AI Scope | YOLOv11 training pipeline supporting incremental dataset expansion without full retraining |
| Multi-format Dataset Conversion | 🥇 Malaria AI Scope | Unified ingestion from heterogeneous annotation formats (binary masks, COCO JSON, plain TXT) → YOLO segmentation format |
| Docker Multi-stage Build | 🥈 Sherpa | Separate builder/production stages for Python backend + Node.js frontend. Non-root users, health checks, depends_on ordering |
| RAG Snapshot System | 🥈 Sherpa | Export/import pre-computed Milvus vectors as portable JSON (with SHA-256 manifest). Eliminates re-embedding on fresh deployments |
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class WhoAmI:
user = 'Vicio Di Cara'
birth_fact = 'The first internet ping in Italy coincides with my birth'
currently_working_on = [
"🎓 LisAI Interpreter — OPIT Master's Capstone (CNN-LSTM + Transformer)",
"🥇 Malaria AI Scope — 1st Place AWS University Engagement Program",
"🥈 Sherpa Alzheimer — 2nd Place OPIT AI Competition (Multi-Agent RAG)",
"✅ Drug Repositioning Research (Random Forest + K-means, 92% accuracy)",
"✅ Mental D0C — LLM fine-tuned for mental health assessment (Qwen3)",
"🚀 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."