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Ahmed Saeed

Assistant Professor
School of Computer Science
College of Computing
Georgia Institute of Technology
asaeed [@] cc [.] gatech [.] edu
        

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Bio

Ahmed Saeed is an Assistant Professor in the School of Computer Science at Georgia Tech. He was a Postdoctoral Associate at MIT working with Prof. Mohammad Alizadeh. He completed his PhD in August 2019 at Georgia Tech, where he was advised by Prof. Mostafa Ammar and Prof. Ellen Zegura. During his PhD, he interned several times at Google, where he collaborated with Nandita Dukkipati and Amin Vahdat. He received his bachelor's degree in Computer and Systems Engineering from Alexandria University in 2010.

I am looking for highly motivated students interested in building and understanding large-scale systems and networks.


Research Interests

Theory, design, and implementation of scalable computer networks and computer systems, including resource scheduling, congestion control, wireless networks, and cyber-physical systems.

Active projects (more details here):

Scalable End Host Networking

The goal of the project is develop end-host networking stacks that can scale, not only in terms of their messages per second capacity, but in terms of the number of network connections they can handle. To that end, we tackle problems in schedulers, the VM-hypervisor API, and the transport layer.

WAN Congestion Control

Wide area networks (and the Internet at large) are getting more heterogeneous and programmable, proving to be a challenging environment to manage while providing many new knobs to provide better performance. To that end, we explore new tools to better characterize the challenges and new algorithms to address them.

Formal Verification of Performance Properties of Distributed Systems

This project attempts to provide concrete analytical tools to understand the performance of heuristics used in resource allocation and management of distributed system. The project leverages progress made in formal verification tools (e.g., Z3) that can efficiently search through all potential scenarios that can encounter a heuristic. We use such tools to identify scenarios where systems underperform, helping system designers avoid them or plan for them.


Selected Publications (full list)

Students

Teaching