Reza Jalayer

About

Reza Jalayer is a researcher at the forefront of integrating deep learning with robotics, focusing on two pivotal sensory domains: auditory perception and visual hand analysis. His work on sound source localization (SSL) has advanced how robots pinpoint acoustic events—from human speech to machinery noise—in complex environments like manufacturing workplaces. His 2024 paper on ConvLSTM-based SSL, with 7 citations, demonstrates a practical, AI-driven approach to spatial hearing for industrial robots. Complementing this, Jalayer has made significant contributions to human-robot interaction (HRI) through vision-based hand analysis. His 2025 review on deep learning for hand detection, segmentation, and gesture recognition (5 citations) synthesizes key advances enabling more intuitive robot collaboration. He has also pioneered work on testing hand segmentation models under out-of-distribution conditions (4 citations), addressing a critical gap in real-world HRI robustness. With additional conceptual frameworks for SSL in manufacturing, Jalayer’s research bridges theoretical AI with applied robotics, offering tangible solutions for safer, more responsive human-machine systems.

Research Focus

Key Achievements

Top Papers

1. 1
   ConvLSTM-based Sound Source Localization in a manufacturing workplace

   7 citations · 2024
2. 2
   A review on deep learning for vision-based hand detection, hand segmentation and hand gesture recognition in human–robot interaction

   5 citations · 2025
3. 3
   A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods

   4 citations · 2025
4. 4
   Testing human-hand segmentation on in-distribution and out-of-distribution data in human–robot interactions using a deep ensemble model

   4 citations · 2025
5. 5
   A Conceptual Framework for Localization of Active Sound Sources in Manufacturing Environment Based on Artificial Intelligence

   3 citations · 2023

Key Collaborators

Contact & Links