José Mairton Barros da Silva Júnior
Biträdande universitetslektor vid Institutionen för informationsteknologi; Datorteknik
- Telefon:
- 018-471 12 27
- E-post:
- mairton.barros@it.uu.se
- Besöksadress:
- Hus 10, Regementsvägen 10
- Postadress:
- Box 337
751 05 UPPSALA
- ORCID:
- 0000-0002-4503-4242
Kort presentation
I am currently an Assistant Professor in the Division of Computer Systems at Uppsala University in Sweden. I am part of the Emerging Systems (ECoS) research group. My research interests include mathematical optimization and learning for future wireless networks, specifically federated learning over vehicular networks, machine learning driven mobile networks, and communication efficiency for distributed learning.
Check my Personal Webpage and Google Scholar for more info.
Nyckelord
- wireless communications
- machine learning
- optimization
- federated machine learning
- distributed machine learning
- wireless networks and communications
- distributed optimization
- 6g
Biografi
I am currently an Assistant Professor in the Division of Computer Systems at Uppsala University, Sweden. I am part of the Emerging Systems (ECoS) research group. My current research focuses on mathematical optimization and learning for future wireless networks, specifically federated learning over vehicular networks, machine learning driven mobile networks, and communication efficiency for distributed learning.
In 2019, I earned a Ph.D. degree in Electrical Engineering and Computer Science from KTH Royal Institute of Technology in Stockholm, Sweden. Prior to that, I earned a BSc (with honors) and an MSc in Teleinformatics Engineering from the Federal University of Ceará in 2012 and 2014, respectively. I worked as a research engineer at the Wireless Telecommunication Research Group (GTEL) in Fortaleza from July 2012 to March 2015. During the autumn/winter of 2013–2014, I completed an internship at Ericsson Research in Stockholm, Sweden. In Spring/Fall 2018, I was a visiting researcher at Rice University in Houston, Texas, USA. From 2019 to 2021, I worked as a Postdoctoral Researcher with the KTH Royal Institute of Technology. Between 2022 and 2023, I held a Marie Skłodowska-Curie Postdoctoral Fellowship at Princeton University in the Department of Electrical and Computer Engineering, USA, and at KTH Royal Institute of Technology in the Division of Network and Systems Engineering.
I currently serve as the Workshops, Tutorials, & Symposia Officer of the IEEE Communications Society Emerging Technology Initiative on Machine Learning for Communications. From 2018 to 2021, I served as Secretary for the Full-Duplex Wireless Communications Emerging Technologies Initiatives (FD-ETI), created by IEEE Communications Society to promote full-duplex communications. Additionally, I have been actively involved in the organization of several IEEE international conferences and workshops, including serving as co-chair for GLOBECOM 2025, ICMLCN 2024, and SECON 2022–2023. I presented several tutorials on "Wireless for Machine Learning" at several flagship conferences sponsored by the IEEE Communications Society, including IEEE ICASSP, IEEE PIMRC, IEEE GLOBECOM, and IEEE ICC. In 2021, I was recognized as an Exemplary Reviewer for the IEEE Open Journal of the Communications Society.
Publikationer
Senaste publikationer
Federated Learning over MU-MIMO Vehicular Networks
Ingår i Entropy, 2025
Ingår i Wireless Sensor Networks in Smart Environments, s. 273-298, John Wiley & Sons, 2025
Federated Learning over MU-MIMO Vehicular Networks
Ingår i Entropy, 2025
- DOI för Federated Learning over MU-MIMO Vehicular Networks
- Ladda ner fulltext (pdf) av Federated Learning over MU-MIMO Vehicular Networks
Interpretable Water Leakage Detection Using Federated Prototype-Based Learning
Ingår i Proceedings of the 17th Brazilian Congress on Computational Intelligence (CBIC 2025), 2025
SumComp: Coding for Digital Over-the-Air Computation via the Ring of Integers
Ingår i IEEE Transactions on Communications, s. 752-767, 2025
- DOI för SumComp: Coding for Digital Over-the-Air Computation via the Ring of Integers
- Ladda ner fulltext (pdf) av SumComp: Coding for Digital Over-the-Air Computation via the Ring of Integers
Alla publikationer
Artiklar i tidskrift
Federated Learning over MU-MIMO Vehicular Networks
Ingår i Entropy, 2025
Federated Learning over MU-MIMO Vehicular Networks
Ingår i Entropy, 2025
- DOI för Federated Learning over MU-MIMO Vehicular Networks
- Ladda ner fulltext (pdf) av Federated Learning over MU-MIMO Vehicular Networks
SumComp: Coding for Digital Over-the-Air Computation via the Ring of Integers
Ingår i IEEE Transactions on Communications, s. 752-767, 2025
- DOI för SumComp: Coding for Digital Over-the-Air Computation via the Ring of Integers
- Ladda ner fulltext (pdf) av SumComp: Coding for Digital Over-the-Air Computation via the Ring of Integers
Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks
Ingår i IEEE Transactions on Machine Learning in Communications and Networking, s. 1265-1282, 2024
- DOI för Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks
- Ladda ner fulltext (pdf) av Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks
On differential privacy for federated learning in wireless systems with multiple base stations
Ingår i IET Communications, s. 1853-1867, 2024
- DOI för On differential privacy for federated learning in wireless systems with multiple base stations
- Ladda ner fulltext (pdf) av On differential privacy for federated learning in wireless systems with multiple base stations
Blind Federated Learning via Over-the-Air q-QAM
Ingår i IEEE Transactions on Wireless Communications, s. 19570-19586, 2024
- DOI för Blind Federated Learning via Over-the-Air q-QAM
- Ladda ner fulltext (pdf) av Blind Federated Learning via Over-the-Air q-QAM
Machine Learning for Spectrum Sharing: A Survey
Ingår i Foundations and Trends® in Networking, s. 1-159, 2024
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning
Ingår i IEEE Transactions on Wireless Communications, s. 11076-11093, 2024
- DOI för FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning
- Ladda ner fulltext (pdf) av FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning
ChannelComp: A General Method for Computation by Communications
Ingår i IEEE Transactions on Communications, s. 692-706, 2024
- DOI för ChannelComp: A General Method for Computation by Communications
- Ladda ner fulltext (pdf) av ChannelComp: A General Method for Computation by Communications
Federated Learning Using Three-Operator ADMM
Ingår i IEEE Journal on Selected Topics in Signal Processing, s. 205-221, 2023
- DOI för Federated Learning Using Three-Operator ADMM
- Ladda ner fulltext (pdf) av Federated Learning Using Three-Operator ADMM
Leakage detection in water distribution networks using machine-learning strategies
Ingår i Water Science and Technology, s. 1115-1126, 2023
- DOI för Leakage detection in water distribution networks using machine-learning strategies
- Ladda ner fulltext (pdf) av Leakage detection in water distribution networks using machine-learning strategies
Kapitel i böcker, delar av antologi
Ingår i Wireless Sensor Networks in Smart Environments, s. 273-298, John Wiley & Sons, 2025
Konferensbidrag
Interpretable Water Leakage Detection Using Federated Prototype-Based Learning
Ingår i Proceedings of the 17th Brazilian Congress on Computational Intelligence (CBIC 2025), 2025
Computing Functions Over-the-Air Using Digital Modulations
Ingår i ICC 2023, s. 5780-5786, 2023
Sub-Band Full-Duplex for 5G New Radio: Challenges, Solutions and Performance
Ingår i 57th Asilomar Conference on Signals, Systems, and Computers, IEEECONF, s. 167-173, 2023