
Dr. Jernej Hribar
- Role: MSCA Fellow
- Joined: October, 2022
- Affiliations: Jožef Stefan Institute
- Research Interests: age of information, reinforcement learning, deep learning, smart grids, and embedded systems.
Jernej Hribar is a Marie Skłodowska-Curie Action Fellow in the Department of Communication Systems since October 2022. His MSCA action - TimeSmart - investigates the applicability of the novel Age of Information metric in smart grids. From 2019-2022, he was a postdoctoral researcher in the CONNECT Centre for Future Networks and Communications at Trinity College Dublin, Ireland. He was involved in an international collaborative project between Trinity College Dublin and Tsinghua University using machine learning techniques to assist future networks with decision-making problems in smart cities. He received an undergraduate degree in electrical engineering from the University of Ljubljana, Slovenia, in 2014 and a Ph.D. from Trinity College Dublin, Ireland, in 2020. His PhD focused on improving the sustainability of large-scale sensor deployments using reinforcement learning and leveraging the freshness of correlated information measured by the Age of Information metric.
Publications:
- A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and TrendsIEEE access, 2023
- Machine Learning Operations Model Store: Optimizing Model Selection for AI as a ServiceBalkanCom 2023, 2023 International Balkan Conference on Communications and Networking (BalkanCom) took place 5-8 June 2023 in İstanbul, Turkey, 2023
- Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement LearningICAART 2023: proceedings of the 15th International Conference on Agents and Artificial Intelligence, 2023
- Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANSITU journal: future and evolving technologies, 2022
- Timely and Sustainable: Utilising Correlation in Status Updates of Battery-Powered and Energy-Harvesting Sensors Using Deep Reinforcement LearningComputer communications, 2022
- Enabling Deep Reinforcement Learning on Energy Constrained Devices at the Edge of the NetworkIEEE Conference on Wireless Communications and Networking, 10-13, Austin, Texas, Usa, 2022
- Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and SpaceIEEE internet of things journal, 2022
- Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
- Analyse or Transmit: Utilising Correlation at the Edge With Deep Reinforcement Learning2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain 7 - 11 December 2021: proceedings, 2021
- SMART: Situationally-Aware Multi-Agent Reinforcement Learning-Based TransmissionsIEEE transactions on cognitive communications and networking, 2021
- Resource Reservation Within Sliced 5G Networks: A Cost-Reduction Strategy for Service Providers2020 IEEE International Conference on Communications Workshops (ICC): proceedings, 2020
- Utilising Correlated Information to Improve the Sustainability of Internet of Things DevicesWF-IoT: 2019 IEEE 5th World Forum on Internet of Things, 2019
- Using Deep Q-Learning to Prolong the Lifetime of Correlated Internet of Things Devices2019 IEEE International Conference on Communications Workshops, (ICC Workshops): proceedings, 2019
- Using Correlated Information to Extend Device LifetimeIEEE internet of things journal, 2019
- Using Deep Q-Learning to Prolong the Lifetime of Correlated Internet of Things Devices
- Updating Strategies in the Internet of Things by Taking Advantage of Correlated SourcesIEEE Globecom: global hub: connecting East and West, 2017