Sandi Baressi Šegota, PhD is a researcher focusing on the application of artificial intelligence in complex systems, particularly within engineering environments such as robotics, propulsion systems, and energy optimization. He holds a Master’s degree in Computer Science (2019) and a PhD in Electrical Engineering (2025), both from the University of Rijeka.
Over the course of his career, he has actively participated in seven R&D projects, including institutional initiatives and EU-funded research. He has collaborated extensively with industry on applied AI projects, involving AI application in smart homes and industrial automation.
He is the co-author of more than 60 peer-reviewed journal publications and over 30 conference papers, that have been cited over 500 times, and he serves as a reviewer or guest editor for several reputable journals in AI and engineering domains.
He has also supervised graduate theses and delivered guest lectures and workshops on machine learning in engineering contexts. His current research interests include metaheuristic optimization, synthetic data generation, and hybrid modeling techniques for energy-efficient automation.
Research Keywords & Expertise
Data Science
Evolutionary Computati...
machine leaning
Artificial Intelligenc...
Robotics and Artificia...
Engineering Automation
Fingerprints
18%
Artificial Intelligence (AI)
5%
Evolutionary Computation
Short Biography
Sandi Baressi Šegota, PhD is a researcher focusing on the application of artificial intelligence in complex systems, particularly within engineering environments such as robotics, propulsion systems, and energy optimization. He holds a Master’s degree in Computer Science (2019) and a PhD in Electrical Engineering (2025), both from the University of Rijeka.
Over the course of his career, he has actively participated in seven R&D projects, including institutional initiatives and EU-funded research. He has collaborated extensively with industry on applied AI projects, involving AI application in smart homes and industrial automation.
He is the co-author of more than 60 peer-reviewed journal publications and over 30 conference papers, that have been cited over 500 times, and he serves as a reviewer or guest editor for several reputable journals in AI and engineering domains.
He has also supervised graduate theses and delivered guest lectures and workshops on machine learning in engineering contexts. His current research interests include metaheuristic optimization, synthetic data generation, and hybrid modeling techniques for energy-efficient automation.