Tailai Song

Dottorando in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 38^o ciclo (2022-2025)
Dipartimento di Elettronica e Telecomunicazioni (DET)

Collaboratore Esterno
Dipartimento di Elettronica e Telecomunicazioni (DET)

tailai.song@polito.it

Profilo

Dottorato di ricerca

Titolo della tesi

From Coarse Single Theoretical to Fine Multiple Practical: Machine Learning-Empowered Real-Time Communications

Argomento di ricerca

Machine learning for real-time communication.

Tutori

Keywords

Big Data, Machine Learning, Neural Networks and Data Science

Communication and Computer Networks

Modeling, simulation and CAD

Biografia

I obtained the B.Sc. in Automotive Engineering at Politecnico di Torino in 2020 and the M.Sc. in ICT for Smart Societies at Politecnico di Torino in 2022. Currently, I'm a PhD student at Politecnico di Torino (PoliTO), Italy, and a member of Telecommunication Networks Group (TNG) as well as the SmartData@Polito research center. My research focuses on machine learning techniques applied to real-time communication to improve Quality of Experience (QoE).

Premi e riconoscimenti

The Best Student Paper Award in IEEE 26th International Symposium on Multimedia (ISM) 2024 (2024)

Didattica

Insegnamenti

Corso di laurea magistrale

Machine learning for networking. A.A. 2024/25, CYBERSECURITY. Collaboratore del corso
Machine learning for networking. A.A. 2023/24, COMMUNICATIONS ENGINEERING. Collaboratore del corso

MostraNascondi A.A. passati

Pubblicazioni

Pubblicazioni più recenti Vedi tutte le pubblicazioni su Porto@Iris

Song, Tailai; Garza, Paolo; Meo, Michela; Matteo Munafo, Maurizio (2026)
One Is Enough: Efficient Modeling of RTP Traffic for QoS Predictions in Real-Time Communications. In: IEEE TRANSACTIONS ON NETWORKING, vol. 34, pp. 2589-2604. ISSN 2998-4157
Contributo su Rivista
Song, Tailai; Organokov, Mukharbek; Gulikers, Lennart; Grassi, Giulio; Carofiglio, ... (2025)
Advancing Cloud-Native Cyber Threat Detection with Graph-Based Feature Engineering. In: IEEE International Conference on Data Engineering, Hong Kong (Chi), 19-23 May 2025, pp. 4291-4297. ISBN: 979-8-3315-3603-9
Contributo in Atti di Convegno (Proceeding)
Song, Tailai; Meo, Michela (2025)
Debunking Reinforcement Learning for Bandwidth Estimation in Real-Time Communications: When a Simple Regressor Suffices. In: 2025 21st International Conference on Network and Service Management (CNSM), Bologna (Ita), 27-31 October 2025
Contributo in Atti di Convegno (Proceeding)
Song, Tailai; Perna, Gianluca; Garza, Paolo; Meo, Michela; Munafo, Maurizio Matteo (2025)
Packet Loss in Real-Time Communications: Can ML Tame its Unpredictable Nature?. In: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, vol. 22, pp. 72-91. ISSN 1932-4537
Contributo su Rivista
Song, Tailai; Garza, Paolo; Meo, Michela; Munafo, Maurizio Matteo (2024)
Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications. In: 26th International Symposium on Multimedia, Tokyo (JP), 11-13 December 2024, pp. 63-70. ISBN: 979-8-3315-1111-1
Contributo in Atti di Convegno (Proceeding)