Gábor Kiss

Gábor Kiss is an Associate Professor in the Department of Atomic Physics at Budapest University of Technology and Economics (BME). His research focuses on applied physics, surface physics, gas sensors, electrolytic capacitors, and hydrogen storage in metals. He has contributed to studies on metal hydride alloys, titanium-based medical implants, and gas detection technologies. His work involves advanced analytical methods like XPS, SIMS, and AES. Notable collaborations include investigations into hydrogen absorption/desorption processes and the effects of surface contaminations on hydrogen storage materials. Recent work extends to speech-based medical diagnostics, leveraging machine learning for depression and Parkinson’s disease detection.

Education details are not explicitly listed, but his research expertise spans materials science and interdisciplinary applications. His publications emphasize surface analysis techniques and their biomedical and energy storage applications. He has co-authored influential papers in journals like Analytical and Bioanalytical Chemistry and Sensors and Actuators B.

Research trends in his recent articles focus on computational linguistics (e.g., Hungarian speech data analysis), clinical diagnostics via speech processing, and optimization in wireless sensor networks. These reflect a shift towards AI-driven healthcare solutions while maintaining core strengths in materials physics.

No awards or grants are explicitly mentioned, but his extensive publication record and cross-disciplinary collaborations indicate significant contributions to both fundamental and applied research.