Present
Working on distributed AI and communication-efficient learning systems for edge environments, including split learning and resource-aware system design. Contributing to research proposal development and international project preparation.
Research Associate, The Institute for Artificial Intelligence Research and Development of Serbia — Postdoc/Teaching Assistant, Faculty of Technical Sciences, University of Novi Sad
Machine learning and wireless communications, with emphasis on distributed intelligence and efficient, task-aware information processing in edge systems.
Vukan Ninkovic received his BSc and MSc degrees in Electrical and Computer Engineering from the Faculty of Technical Sciences, University of Novi Sad, Serbia, in 2018 and 2019, respectively, where he was awarded the Telenor Foundation "Professor Ilija Stojanović" Award for the best BSc graduate in communications and signal processing. He obtained his PhD in Power, Electronic, and Communication Engineering in November 2024 from the same institution.
He is currently a Research Associate at the Institute for Artificial Intelligence Research and Development of Serbia and a Postdoc/Teaching Assistant at the Faculty of Technical Sciences. His research lies at the intersection of machine learning and wireless communications, with a focus on distributed intelligence, edge AI, and efficient information processing over wireless networks. His work explores how learning and communication can be jointly designed to enable intelligent systems under practical constraints on computation, energy, latency, and connectivity, with a growing focus on Physical AI and real-time intelligent systems.
Working on distributed AI and communication-efficient learning systems for edge environments, including split learning and resource-aware system design. Contributing to research proposal development and international project preparation.
Worked on split learning and communication-efficient AI methods for distributed inference in edge and IoT systems.
Teaching courses in communications, signal processing, and machine learning. Conducting research on machine learning-based physical layer design and autoencoder-based communication systems.
Developed deep learning-based methods for synchronization tasks in wireless systems, including packet detection and carrier frequency offset estimation.
Defended 01/11/2024. Supervisor: Prof. Dejan Vukobratović.
Defended 05/07/2019.
Defended 06/09/2018.
The list below may lag behind the latest record. Full & up-to-date list on Google Scholar ↗
Journal PublicationsDeveloped split learning frameworks and communication strategies for distributed AI in rural IoT networks.
Worked on distributed AI and split learning for real-world water quality monitoring, integrating IoT sensing, UAV-assisted communication, and data-driven prediction models.
Developed AE-based rateless and UEP coding schemes in collaboration with partners from Chalmers University of Technology.
Contributed to IoT security in industrial environments, focusing on device localization and data-driven system modeling.
Participated in international research collaboration on sensing and intelligent systems.
Reviewer for IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Wireless Communications Letters, IEEE Communications Letters, and IEEE Internet of Things Journal.
Novi Sad, Serbia