About me:
Howdy! I'm Nir Regev, a professor and professional in artificial intelligence, with a journey spanning 26 years focused on algorithm development across various sectors.
With a Ph.D. in Electrical Engineering from Ben-Gurion University of the Negev, Israel, my career has been dedicated to exploring and advancing the fields of radar and lidar signal processing, computer vision, machine learning and AI.
My experience covers areas like multi-target tracking, radar micro-Doppler phenomena, and statistical signal processing. I apply this expertise in both industry and academia, guiding projects to fruition and sharing knowledge.
At AlephZero.ai, I lead initiatives that bridge theoretical concepts with practical applications. As an Adjunct Professor at Cal Poly Pomona in Electrical and Computer Engineering, I enjoy teaching and inspiring the next generation of technologists.
Join me in exploring the intersection of technology and innovation, where every challenge is an opportunity for growth.
My publications
P. Bowen, G. Regev, N. Regev, B. Pedroni, E. Hanson and Y. Chen, "Analog, in-memory compute architectures for artificial intelligence," 2023 (preprint).
N. Regev, D. Wulich. "Radar-based, simultaneous human presence detection and breathing rate estimation," Sensors, vol. 21, no. 10, 2021.
N. Regev, D. Wulich. "Multi-modal, remote breathing monitor," Sensors, vol. 20, no. 4, 2020.
N. Regev, D. Wulich. "Remote sensing of vital signs using ultra-wide-band radar," Intl. J. Remote Sensing, vol. 40, no. 17, pp. 6596–6606, 2019.
N. Regev, D. Wulich. "A simple, remote, ultra-sonic based personal emergency response system," IEEE Texas Symp. Wireless & Microwave Circuits & Systems, 2020.
I. Yoffe, N. Regev, D. Wulich. "On direction of arrival estimation with 1-bit quantizer," IEEE Radar Conf., 2019.
N. Regev, I. Yoffe, D. Wulich. "Classification of miniature drones using multilayer perceptron artificial neural network," Intl. Conf. on Radar Systems, pp. 1-5, 2017.
I. Yoffe, N. Regev, D. Wulich. "On optimal receiver for nonlinearly distorted single carrier signal," IEEE Intl. Symp. Personal, Indoor, Mobile Radio Comm., pp. 1-6, 2017.
N. Regev, D. Wulich. "A simple, remote, video-based breathing monitor," IEEE Eng. in Medicine and Biology Conf., pp. 1788-1791, 2017.
N. Regev, D. Wulich, I. Iofedov. "Maximum likelihood detection of nonlinearly distorted OFDM signal," IEEE Global Comm. Conf., 2015.
Projects and demos
Radar based remote breathing in a highly cluttered environment
Radar based boiling water detection - smart appliances
Remotely capturing respiration of a newborn white the bassinet is moving using a radar.
More demos…