My research lies at the intersection of computer networks and human-computer interaction (HCI). The overarching theme of my research is improving the usability of computer networks, by improving the performance of computer networks. Broadly, I work to understand how people experience computer networks by studying the performance of the applications they use on computer networks. I determine the network conditions that lead to good or acceptable quality of experience (QoE) for the end users. Understanding these conditions in turn helps networks researchers design better protocols, better network architectures, and better network operations policies. These, in turn, will lead to increased QoE for the end users, and thus better network usability.

Early on in my career (my Master’s and PhD research), I took a more theoretical approach to this problem, constructing mathematical and queueing models to represent computer networks. Today, my research methods are more experimental and operational in nature. I build testbed networks, smaller-scale models of representative networks using real computers and devices, collect measurements from these networks (and from the people who use them), and analyze the results using machine learning algorithms to deduce trends in the data. I also build tools to collect data, since it’s often difficult to obtain the type of data I’d like from existing tools.

Current projects

Self-Healing Home Networks

Self-healing networks detect existing or potential pathologies and fix or mitigate them with minimal to no human intervention. For example, a network that detects a drop in the available bandwidth along a particular link, or within a series of links, and then reroutes data around these links is a self-healing network. Because such pathologies are visibly evident in the end-users' quality of experience (QoE), self-healing networks are by definition responsive to the needs of networked applications, such as streaming video. Self-healing networks, then, are fundamentally important to the design, engineering, and development not just of future applications, but of future computer networks. By understanding how video applications are affected by network conditions, we can design network protocols and structure new networks to better support these and future (high quality, high-definition, immersive) video applications. Similarly, understanding how network conditions impact application performance leads to the design of more robust application protocols that can better utilize existing network resources.

Building upon or work on video QoE, we are identifying heuristics for determining when network and/or application conditions signal potential eminent degraded QoE, and constructing system architectures to support QoE measurement, analysis, and feedback into the system.

Project publications

  1. A. Csizmar Dalal. "A Framework for Self-Healing Home Networks." In Proceedings of the Tenth International Conference on Heterogeneous Networking for Quality, Reliability, Security, and Robustnests (Qshine), Rhodes, Greece, August 2014. (Short paper, pdf)

Intuitive tools for home network maintenance

Past Projects


Full list