About me:
Howdy! I’m Dr. Nir Regev — professor, founder, and lifelong builder of algorithms. I’ve spent the last 27+ years developing advanced signal processing and AI systems across radar, LiDAR, embedded systems, and computer vision.
I hold a Ph.D. in Electrical & Computer Engineering from Ben-Gurion University, Israel and currently serve as a Professor at Cal Poly Pomona, where I teach courses in control systems and probability. I’m also the founder and Chief Scientist of AlephZero.ai, a consulting firm focused on turning cutting-edge ideas into robust engineering solutions — and drnirregev.com, a learning platform offering courses and books for professionals working in AI and signal processing.
My technical work spans multi-target tracking, micro-Doppler analysis, deep learning, classification pipelines, and statistical signal processing. Whether I’m optimizing real-world sensor systems or writing an expert declaration for court, I bridge the gap between theory and execution — helping teams deliver performance, clarity, and trust.
I support two main types of clients:
Engineering teams who want help diagnosing bottlenecks, improving detection/classification pipelines, or refining AI/ML models for real-world performance.
Attorneys seeking an expert witness who can explain complex technology clearly, back it with hard evidence, and communicate persuasively to judges and juries.
Beyond the technical work, I love teaching, mentoring, and exploring the edges where innovation happens. Every challenge is a chance to build something sharper, smarter, and more impactful.
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…