Person:
AlRegib, Ghassan

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https://hdl.handle.net/1853/71054
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    ML@GT Lab presents LAB LIGHTNING TALKS 2020
    ( 2020-12-04) AlRegib, Ghassan ; Chau, Duen Horng ; Chava, Sudheer ; Cohen, Morris B. ; Davenport, Mark A. ; Desai, Deven ; Dovrolis, Constantine ; Essa, Irfan ; Gupta, Swati ; Huo, Xiaoming ; Kira, Zsolt ; Li, Jing ; Maguluri, Siva Theja ; Pananjady, Ashwin ; Prakash, B. Aditya ; Riedl, Mark O. ; Romberg, Justin ; Xie, Yao ; Zhang, Xiuwei
    Labs affiliated with the Machine Learning Center at Georgia Tech (ML@GT) will have the opportunity to share their research interests, work, and unique aspects of their lab in three minutes or less to interested graduate students, Georgia Tech faculty, and members of the public. Participating labs include: Yao’s Group - Yao Xie, H. Milton Stewart School of Industrial Systems and Engineering (ISyE); Huo Lab - Xiaoming Huo, ISyE; LF Radio Lab – Morris Cohen, School of Electrical Computing and Engineering (ECE); Polo Club of Data Science – Polo Chau, CSE; Network Science – Constantine Dovrolis, School of Computer Science; CLAWS – Srijan Kumar, CSE; Control, Optimization, Algorithms, and Randomness (COAR) Lab – Siva Theja Maguluri, ISyE; Entertainment Intelligence Lab and Human Centered AI Lab – Mark Riedl, IC; Social and Language Technologies (SALT) Lab – Diyi Yang, IC; FATHOM Research Group – Swati Gupta, ISyE; Zhang's CompBio Lab – Xiuwei Zhang, CSE; Statistical Machine Learning - Ashwin Pananjady, ISyE and ECE; AdityaLab - B. Aditya Prakash, CSE; OLIVES - Ghassan AlRegib, ECE; Robotics Perception and Learning (RIPL) – Zsolt Kira, IC; Eye-Team - Irfan Essa, IC; and Mark Davenport, ECE.
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    Studying the Performance of Cooperative Delivery Techniques to Support Video-on-Demand Service in IPTV Networks
    (Georgia Institute of Technology, 2013) Azgin, Aytac ; AlRegib, Ghassan ; Altunbasak, Yucel
    In this paper, we study the use of peer-assisted server-based cooperative transmission strategies in IPTV networks for the delivery of on-demand services to end users. The proposed techniques aim to support the resource efficient delivery of on-demand content to end users in a timely manner. Within the proposed framework, a cooperative transmission strategy suggests that users who have access to the requested content cooperatively transmit to the targeted set of users. In doing so, we can minimize the servicing requirements at the server side and improve the scalability performance in the network. We conducted extensive simulations using different request arrival models and showed that significant performance improvements can be achieved with the proposed delivery techniques to enable efficient access to an ever growing on-demand content. We also showed the robustness of the proposed techniques in regard to variations observed in network state.
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    HP testing and research agreement
    (Georgia Institute of Technology, 2007-10-26) AlRegib, Ghassan
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    Delay-constrained 3-D graphics streaming over lossy networks
    (Georgia Institute of Technology, 2003-08) AlRegib, Ghassan