Md Tariqul Islam
tariqsaj [at] dca.fee.unicamp.br| mtarislam [at] gmail.com
Md Tariqul Islam (wherein ‘Md’ is the abbreviation of ‘Muhammad’) is currently enrolled in the PhD program under the supervision of Prof. Dr. Christian Esteve Rothenberg in the Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC) at University of Campinas (UNICAMP), Brazil. He is one of the members of the INTRIG Lab.
He obtained the MSc degree from the DCA/FEEC/UNICAMP in early 2021. The prime focus of his master’s thesis was the prediction of end-user QoE for DASH video service at network edge premises through machine learning to help network orchestrators detect SLA violations and take run time action, which was part of the Ericson-funded CLAML (Control Loop Architecture with Adaptive Policy and Machine Learning Capabilities) project.
He completed the BSc degree in Information and Communication Technology (ICT) from Mawlana Bhashani Science and Technology University (MBSTU), Bangladesh, in 2018.
- QoS and QoE of XR/VR/DASH Streaming
- 5G-and-beyond Cellular Networks
- Edge Computing
- Machine Learning and Deep Learning
- Machine Learning-Assisted Closed-Control Loops for Automated Healing in Multi-Domain Zero-Touch Networks. Nathan Saraiva, Md Tariqul Islam, Raza Ul Mustafa, Danny Lachos Perez, Christian Esteve Rothenberg, Pedro Henrique Gomes. In Journal of Network and Systems Management volume 30, Article number: 46, 2022. [PDF]
- Predicting XR Services QoE with ML: Insights from In-band Encrypted QoS Features in 360-VR. Md Tariqul Islam, Christian Esteve Rothenberg, Pedro Henrique Gomes. Masuscript submitted for publication, 2023.
- EFFECTOR: DASH QoE and QoS Evaluation Framework For EnCrypTed videO tRaffic. Raza Ul Mustafa, Md Tariqul Islam, Christian Esteve Rothenberg, Pedro Henrique Gomes. To appear in the 36th IEEE/IFIP Network Operations and Management Symposium, May-2023.
- DASH QoE Performance Evaluation Framework with 5G Datasets. Raza Ul Mustafa, Md Tariqul Islam, Christian Esteve Rothenberg, Simone Ferlin, Darijo Raca, Jason J. Quinlan. In IEEE CNSM AnServApp Workshop, 2020. [PDF] [Video] [GitHub]
- HAS Based Empirical QoE Study over TCP and QUIC on Diverse Networks. Md Tariqul Islam, Christian Esteve Rothenberg. In VII Pre-IETF Workshop, 2020. [Slides] [Video]
- Intent-based Control Loop for DASH Video Service Assurance using ML-based Edge QoE Estimation. Christian Esteve Rothenberg, Danny Lachos Perez, Nathan Saraiva, Raphael Rosa, Raza Ul Mustafa, Md Tariqul Islam , Pedro Henrique Gomes. In IEEE NetSoft Demo Session, 2020. [PDF] [Video]
- QoE Estimation by Virtual Probe at Edge Computing Facilities. Md Tariqul Islam, Christian Esteve Rothenberg. In XII EADCA Poster Session, 2019.
- A predictive DASH QoE approach based on machine learning at multi-access edge computing: Uma abordagem preditiva de DASH QoE baseada em aprendizado de máquina em multi-access edge computing. Md Tariqul Islam. 1 recurso online (108 p.) Dissertação (mestrado) – Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação, Campinas, SP. Disponível em: https://hdl.handle.net/20.500.12733/1641360, Acesso em: 10 nov. 2022.