Jalal Taghia is a senior Algorithm Developer at BOSE Automotive GmbH, Germany. He received the Dr.-Ing. degree in Electrical Engineering and Information Technology from Ruhr University Bochum, Germany, in 2016. His research and industrial experience spans signal processing, machine learning, automotive radar systems, embedded software, and automotive noise control.

At BOSE Automotive, he develops algorithms for Active Noise Control (ANC), with a focus on automotive road-noise cancellation and engine harmonic cancellation and modification. Prior to joining BOSE, he worked at FORVIA/HELLA, where he contributed to the development of automotive radar systems, radar interference mitigation algorithms, and embedded software solutions for advanced driver assistance systems (ADAS). He also served as a Research Associate at Ruhr University Bochum, conducting research on speech enhancement, speech intelligibility assessment, and statistical signal processing.

Dr. Taghia has authored and co-authored numerous peer-reviewed journal and conference publications and is an inventor on multiple patents in automotive radar and signal processing technologies. His work spans both academic research and industrial development of advanced sensing, perception, acoustics, and signal processing systems for automotive applications. His research interests include automotive radar, signal processing, machine learning, deep learning, active noise control, sensor fusion, and intelligent vehicular systems.

Research Keywords:
Automotive Radar Systems, Radar Signal Processing, Radar Interference Detection and Mitigation, Active Noise Control (ANC), Road Noise Cancellation, Automotive Sensing and Perception, Sensor Fusion and Multi-Sensor Perception, Machine Learning for Vehicular Applications, Advanced Driver Assistance Systems (ADAS), Detection, Estimation, and Tracking Theory, Statistical Signal Processing, Embedded Automotive Systems, Automotive Electronics and Electronic Control Systems, Intelligent Vehicular Systems, Autonomous and Connected Vehicles