Hizza Waseem

Papers

Total Citations

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About

Hizza Waseem is a rising researcher at the forefront of TinyML and intelligent manufacturing, whose work bridges the gap between edge computing and industrial automation. Her most-cited paper, "TinyML-Powered Tack Weld Detection for Robotic Welding" (2025), introduces a lightweight machine learning framework that enables real-time, low-power detection of tack welds directly on embedded devices—a critical step toward safer, more efficient robotic welding processes. Despite its recent publication, this work has already garnered 2 citations, signaling early impact in the niche but rapidly growing field of TinyML for manufacturing. Waseem’s contributions lie in demonstrating how resource-constrained models can achieve high accuracy in harsh industrial environments, reducing reliance on cloud computing and improving latency. Her research aligns with broader trends in Industry 4.0, emphasizing on-device intelligence for quality control and predictive maintenance. As a young scholar, she is poised to influence the next wave of smart factory solutions, making her a name to watch in applied machine learning and cyber-physical systems.