I am an Applied AI Engineer specializing in neuroinformatics. My core engineering philosophy centers on building Zero-Hallucination AI.

To process massive biomedical datasets and orchestrate autonomous research securely, I utilize a high-throughput, locally deployable stack:

• Applied AI & Local Inference: PyTorch, Hugging Face Transformers, vLLM (High-throughput serving), Ollama - Kimi (Local Kimi K3 - Llama-3/Qwen), BitsAndBytes.
• Agentic Orchestration: LangGraph (Cyclic workflows), LangChain, Model Context Protocol (MCP).
• Knowledge Graphs & RAG: Neo4j (Graph Databases), Qdrant (Hybrid Search), ChromaDB, Semantic Chunking.
• Formal Verification: Lean 4 (Theorem proving for logic verification).
• Backend, MLOps & Data: Python 3 (OOP/Asyncio), PostgreSQL, FastAPI, Docker, Prometheus + Grafana.

Technologies and systems I plan to explore and master in the future:

• Systems & Bare-Metal: Rust, Modern C++ (17/20), Embedded C.
• High-Performance & Neuromorphic Computing: CUDA, FPGA / Verilog, Lava (Intel Loihi), Hardware-accelerated SNNs.
• Signal Processing & Cybernetics: Digital Signal Processing (DSP), SpikeInterface, Kalman Filters, Optimal Control Theory.
• Neural Security: Fully Homomorphic Encryption (FHE) for secure BCI telemetry.

"Solve intelligence, and then use that to solve everything else."