Danilo Maric
Chief Technology Officer, (CTO) LoRaSi d.o.o
M.Sc in Electronic Engineering from University of Bologna. Master thesis on FW development of medical ventilators. Development activities in university projects, such as Smart Helmet (prof. Benini) and Fruit Inspection (Prof. Di Stefano). Over 8 years of technical and leadership experience in HW-SW development of AI -powered embedded systems, targeting medical, industrial automation and IT sector. Technical background includes development of bare-metal projects, complex Linux Stack software, as well as ML on embedded devices. Software Team Leader at Datalogic Industrial Automation. Innovation activities via company's program for intellectual property (two patents pending). Completion of several advanced courses for Linux and C++ (Bootlin, KOAN, UNIBO). Presenter of LoRaSi's SAMST project at MIPRO Conference '25. Member of the OPEL Steering Committee of MIPRO Conference. Startup Enthusiast (Winner of WBC-Inno competition, participation at Start-up Spritzer in Graz). Fluent in 4 languages (Montenegrin, English, Italian and German). CTO of LoRaSi and co-author of SAMST and DTSB projects, filed to the Intellectual Property Office of Montenegro.
2026 Event Agenda Sessions
Integrated Asset Intelligence for Next-Generation Transmission Systems: SAMSTEO
Grid reliability increasingly depends on proactive, data-driven asset management rather than reactive maintenance. SAMSTEO introduces an intelligent monitoring solution that combines Earth Observation, IoT, and weather data to anticipate structural and operational risks across transmission infrastructure. By enabling condition-based maintenance and early fault prediction, the system empowers operators to optimise load distribution, reduce downtime, and lower operational costs.
Wednesday 20 May 12:00 - 12:30 Central Grid
Industrial AI and Digitalisation
Grid reliability increasingly depends on proactive, data-driven asset management rather than reactive maintenance. SAMSTEO introduces an intelligent monitoring solution that combines Earth Observation, IoT, and weather data to anticipate structural and operational risks across transmission infrastructure. By enabling condition-based maintenance and early fault prediction, the system empowers operators to optimise load distribution, reduce downtime, and lower operational costs.
Central Grid Europe/Madrid
