Mohammed Alnemari

Ph.D. in Computer Engineering, focused on "Efficient Deep Neural Networks on the Edge".
Investigated the use and combination of pruning, quantization, and tensor decomposition methods on state-of-the-art deep neural network models.
Developed techniques for efficient deployment of neural networks on low-energy-constrained devices.

Master’s degree in Computer Engineering.
Thesis: "Integration of a Low Cost EEG Headset with the Internet of Things Framework".
Integrated hardware of vital signals with IoT framework (EEG, EMG, heart beating).

Non-Degree Program in Philosophy, Politics, and Economics.

Bachelor’s degree in Computer Engineering.
Graduated with second honors.

Certificate in Game Programming, Computer Games and Programming Skills.
Lead programmer on a 3D game using Unreal Engine 5 as a group project.
Designed and programmed a 2D game using Unity as an individual project.

Team Leader for the WaterBacteria Research Initiative. Led a diverse team of 15 assistant professors from the USA and Saudi Arabia. Developed innovative methods using bacteria to produce water and irrigate arid soil in Saudi Arabia, utilizing AI to modify bacterial genetics for resilience in harsh environments.

Team Member for the RRAM Crossbar Based Analog In-Memory AI Accelerator SOC Chip project. Designed an AI accelerator SOC chip with non-volatile resistive memory devices, custom analog crossbar computational units, and fault-tolerant neural network architectures.

Project Leader for AI and Life Sciences Research Initiative. Led a team of 4 researchers in collaboration with Neom to develop innovative AI solutions for life sciences applications, managing project timelines with agile methodologies.

Member Team for Jellyfish Detection and Desalination Plant Risk Assessment. Used AI and sensing techniques to detect jellyfish emergence, analyze environmental data, and assess risks at desalination plants.

AI Engineer Intern (Remote). Collaborated on building deep neural network models for image and video compression, optimizing neural network performance for compression efficiency.

Chief Technology Officer (Remote). Led the technology team to develop innovative solutions, overseeing the deployment of new products and aligning technology with business goals.

Summer Program Participant. Developed deep neural network models for self-driving cars and integrated AI solutions into FPGA for real-time processing in automotive systems.

Summer Researcher. Developed an IoT framework, including an EEG module, as part of a research project sponsored by Intel, and integrated IoT components effectively.