ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
IEEE INFOCOM 2019 (353 citations)
Research topics
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My work lies at the intersection of Deep Learning and Wireless Communications, especially related to applications of DL to PHY layer communication.
Selected Publications
For a complete list of publications, please check my Google Scholar Profile.
Last updated: August 2025
No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments
IEEE Transactions on Cognitive Communications and Networking, 2019 (227 citations)
DeepRadioID: Real-time Channel-Resilient Optimization of Deep Learning-Based Radio Fingerprinting Algorithms
ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2019 (161 citations)
Deep Learning at the Edge for Channel Estimation in Beyond-5G Massive MIMO
IEEE Wireless Communications, 2021 (64 citations)
Going Beyond RF: A Survey on How AI-Enabled Multimodal Beamforming Will Shape the NextG Standard
Computer Networks, 2023 (21 citations)
Machine Learning on Camera Images for Fast mmWave Beamforming
IEEE MASS 2020 (21 citations)
Neural Network-Based OFDM Receiver for Resource Constrained IoT Devices
IEEE Internet of Things Magazine, 2022 (19 citations)
T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge
arXiv preprint, March 2024
TRACTOR: Traffic Analysis and Classification Tool for Open RAN
IEEE ICC 2024 (5 citations)
MEGATRON: Machine Learning in 5G with Analysis of Traffic in Open Radio Access Networks
International Conference on Computing, Networking and Communications (ICNC), 2024 (2 citations)
Improve Your Aim: A Deep Reinforcement Learning Approach for 5G NR mmWave Beam Refinement
Published 2023 (2 citations)
About me
My passion for academic research emerged at the end of my undergraduate studies, when in 2012 I was awarded a 2 years fellowship as a technical student by INFN (National Institute of Nuclear Physics) in Italy. I studied and conducted my own research on heterogeneous computing and GPGPU applications. I had the opportunity to collaborate with different teams at INFN that wanted to integrate these technologies in their experiments.
I received my BS in 2013 and MS in 2017 in Computer Science at University of Bologna.
At the end of my MS, I was selected, along with another student, to participate in a 6 months internship at Datalogic ADC in Pasadena (CA) to work in the field of Deep Learning and Computer Vision applied to OCR systems.
Following my Master's degree, I joined the Genesys Lab at Northeastern University (Boston, MA) as a Research Assistant and Ph.D. candidate under the supervision of Prof. Kaushik Chowdhury. I successfully completed my Ph.D. in March 2025.
At the end of March 2025, I joined NVIDIA as a Senior Software Engineer on the AERIAL team, where I continue my work on Deep Learning applications for Next-Generation Cellular Networks.
Research interests:
- Deep Learning
- Swarm Intelligence
- Mobile systems
- Wireless Communications
- GPGPU
- Heterogeneous computing
- Parallel and Distributed computing
Get In Touch
For any question or information, don't hesitate to reach out!