Ali Hussain

Under the DOD HPCMD program and through Oak Ridge Institute Benchmarked latencies of EfficientDet on Classifying and Bounding Hydrilla Invasive Seaweed and Worked on adapting EfficientDet, YOLO, and VGG16 Pytorch deep learning models to be compatible with IBM's NorthPole Chip Neural Inference Chip.

Within the Airforce Research Lab Scholars Program worked at the University of Florida REEF Autonomous Vehicles Lab and created C++ and Python Robot Operating System Node to generate live optical flow using pre trained
computer vision model RAFT. Used CUDA toolkit to increase speed. Used Camera aperture and depth data to perform
Visual Odometry.

Worked on real time safety critical embedded modules to control train crossings in C++. Developed code automating the installation of embedded firmware. Worked on Django backend in Python for web update utility for track controllers.

Wrote C code to Interface with nrf1960-5280 dk microcomputer operating on Zephyr RTOS to collect and decode Bluetooth advertising data.