🎓 M.S. in Computer Engineering @ University of South Florida
💡 AI Engineer | Computer Vision| FPGA/ASIC Enthusiast
📍 Tampa, FL | 🌐 dayngerous.me
- Graph Neural Networks (GNNs) and Differentiable Graph Matching
- Natural Language Processing: Integrating LLM bias into constituency parsing, multi-token generation
- Computer Vision: Fingerprint Recognition, Object Detection, Scene Understanding
- Hardware Acceleration: FPGA/ASIC Design, HLS C/C++, CUDA, and ONNX Deployment
- Model Optimization: Quantization, Multi-stage Learning, Energy-Efficient AI
Deep Level-3 fingerprint matching pipeline integrating graph matching and differentiable Sinkhorn algorithms.
Tech: PyTorch, Top-K Matching, CUDA, Optimal Transport
Bridges grammar-based parsing with LLM priors to improve low-data syntactic learning.
Tech: Transformers, PCFGs, PyTorch, Span Interpolation
Next.js-based portfolio showcasing AI and hardware projects with interactive components.
Tech: Next.js, TailwindCSS, React Hooks, SEO Optimization
Languages: Python, C/C++, Verilog, CUDA
Frameworks: PyTorch, TensorFlow, LangChain, OpenAI API
Hardware Tools: Vivado, Vitis IDE, HSPICE, Cadence Virtuoso
Other: Docker, Git, Next.js, Azure, AWS
- Multi-stage optimization for graph matching models
- Hardware-accelerated AI inference
- Sustainable AI & resource-efficient deep learning
💼 LinkedIn
🌐 Portfolio
📧 Email
“Bridging symbolic reasoning, neural computation, and hardware acceleration — one model at a time.”