Omri grew up in Shoham and currently resides in Tel-Aviv. He earned a BSc in mathematics and physics at The Hebrew University of Jerusalem in the framework of the IDF’s Talpiot program. Omri served in the IDF for a total of nine years, six of which were dedicated to working as an R&D electronics engineer, where he led an algorithm R&D section focused on signal processing and deep learning. He completed his MSc studies in mechanical engineering at Ben-Gurion University of the Negev under the guidance of Prof. Jacob Bortman at the BGU-PHM laboratory, where he is currently a PhD student.

Omri’s research focuses on improving the prognosis and health monitoring of machines using a combination of hybrid simulations, digital twins, signal processing, and machine learning. He aims to enhance the current algorithms for fault location estimation and extend them for fault severity estimation under the assumption of zero-fault-shot learning, where there are no examples of faulty signals (labeled or unlabeled) of the tested machine. Additionally, Omri is trying to develop an order framework for fault diagnosis, based on digital twins, to replace traditional methods, which often require long and specific algorithm research and development for each fault type.