Masoud Jalayer
University of Turku, University of Victoria, Politecnico di Milano
Papers
8
Total Citations
76
H-Index
5
About
Masoud Jalayer is a rising researcher at the intersection of artificial intelligence, robotics, and manufacturing, whose work is shaping how machines perceive and interact with their environment. His primary research areas span deep reinforcement learning for scheduling, explainable AI (XAI) for condition monitoring, and vision- and audio-based human-robot interaction. Jalayer’s most impactful contribution is his comprehensive survey on deep reinforcement learning for machine scheduling, which has accumulated 45 citations across two versions, establishing a foundational roadmap for the field. He has also pioneered a unified XAI framework for signal-based deep learning models, addressing the critical “black box” problem in condition monitoring—a key barrier to deploying AI in mission-critical robotic systems. His work on ConvLSTM-based sound source localization in manufacturing and deep learning for hand gesture recognition in human-robot interaction demonstrates a commitment to creating safer, more intuitive collaborative environments. Notably, his research on testing hand segmentation under out-of-distribution data highlights his focus on robustness and real-world reliability. With a growing citation record and a portfolio that bridges theoretical frameworks and practical applications, Jalayer is establishing himself as a leading voice in intelligent manufacturing and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 4ConvLSTM-based Sound Source Localization in a manufacturing workplace7 citations · 2024
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