Ognjen Jovanović

Graduation Thesis — End-to-End Deep Learning on Embedded Hardware

Built a complete autonomous driving pipeline: dataset collection (~20,000 labeled image-command pairs), PilotNet-inspired CNN training on NVIDIA GPU, dynamic quantization (FP32 → INT8, ~1.2x speedup), and TorchScript deployment on Raspberry Pi 4.

• Achieved real-time inference at ~20–25 Hz on ARM hardware, enabling autonomous hallway navigation using monocular vision with behavioral cloning.
• Integrated four HC-SR04 ultrasonic sensors as an independent safety layer with emergency obstacle avoidance, direction-aware recovery maneuvers, and a 500 ms watchdog timer.
• Developed a multi-threaded Python control server (drive_server.py) with dedicated threads for TCP control, telemetry, and MJPEG video streaming.
• Created a cross-platform Flutter mobile application with dual-joystick control, live camera feed, real-time sensor HUD, and seamless manual/autonomous mode switching.

Personal Project — Local AI Research Environment

Architected a dedicated Ubuntu 24.04 workstation with NVIDIA RTX GPU acceleration for ML experimentation, LLM serving, and generative AI research.

• Deployed containerized ML infrastructure via Docker: Ollama, OpenWebUI, ComfyUI, and Lobe Chat with NVIDIA container toolkit passthrough.
• Configured CUDA/cuDNN toolchain and integrated PyTorch, TensorFlow, Keras, and OpenCV for end-to-end model development and benchmarking.
• Implemented a remote workstation-client workflow over LAN, eliminating cloud dependency for GPU-intensive research.

Independent Project — CNN Binary Classifier for Veterinary Diagnostics

Designing a Jupyter-based ML system for classifying trichinoscopy slide images as Trichinella-positive or negative, targeting ≥95% sensitivity for food safety applications.

• Architecture: EfficientNet-B3 with custom classifier head, Grad-CAM explainability overlays, Albumentations augmentation pipeline, and Docker-containerized GPU environment.

Research Paper — Library Evaluation with Practical Demonstration

Authored a research paper surveying widely-used ML/AI libraries, analyzing their strengths, ecosystem fit, and industry adoption.

• Developed a companion Jupyter Notebook with an image recognition model trained on CIFAR-10 using PyTorch and NumPy with NVIDIA GPU acceleration.

Client Work — Full-Stack Websites

Designed and deployed production websites including autizamjuznibanat.com and boggyart.com using Astro.js with contact form integrations (Formspree) and responsive design.