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Kamyar Mirzazad Barijough

Ph.D. Candidate at The University of Texas at Austin

Bio

Hey there, I’m Kamyar.

I have been pursuing a Ph.D. degree in Electrical and Computer Engineering Department of The University of Texas at Austin since Fall 2015. On my way to doctorate, I earned my Master of Science in Engineering degree in Fall 2017. I am in Architecture, Computer Systems and Embedded Systems (ACSES) track and a member of the System-Level Architecture and Modeling (SLAM) Research Group.

My research at UT has mostly been focused on providing real-time guarantees in distributed edge computing. Edge computing often utilizes open public networks for communication between devices, which cannot provide timing guarantees for reliable delivery. To enable static analysis and modeling of these applications, we are proposing extensions to data flow models that capture network losses and delays, tools to analyze these models and runtime systems for deployment of such applications. We are using network simulators, emulators and real-world Wi-Fi insfrastructure along with a cluster of development boards to develop and verify our models and runtime systems. This work is part of the Network-Level Design of Cyber-Physical Networks-of-Systems project at SLAM Lab.

Prior to joining UT, I got my Bachelor of Science in Electrical Engineering (Digital Systems track) from Sharif University of Technology. As my senior design project, I worked on improving the accuracy of throughput estimation of streaming applications for a given buffer size allocation. I also got a minor degree in Computer Engineering while I was in Sharif.

As any other computer engineer, I enjoy programming in multiple low and high level languages. You can view some of my work in my GitHub.



Research Interests

Embedded Systems Distributed Systems


Talks

"Quality/Latency-Aware Real-Time Scheduling of Distributed Streaming IoT Application," Embedded Systems Week (ESWEEK), New York City, NY, 2019. [slides]

"Distributed Deep Learning Inference on Resource-Constrained IoT Edge Clusters," ARM Research Summit, Austin, TX, 2019. [slides]



Teaching Experience

EE382N.23: Embedded System Design and Modeling (Fall 2019)

EE319k: Introduction to Embedded Systems (Spring 2017)

EE306: Introduction to Computing (Fall 2016, Spring 2016, Fall 2015)



Selected Publications

K. Mirzazad, Z. Zhao and A. Gerstlauer, "Quality/Latency-Aware Real-Time Scheduling of Distributed Streaming IoT Applications," CODES+ISSS, special issue of ACM Transactions on Embedded Computer Systems (TECS), 2019.

Z. Zhao, K. Mirzazad and A. Gerstlauer, "DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters," CODES+ISSS, special issue of IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2018.

K. Mirzazad, M. Hashemi, V. Khibin and S. Ghiasi, Implementation-Aware Model Analysis: The Case of Buffer-Throughput Tradeoff in Streaming Applications," Proceedings of the ACM SIGPLAN/SIGBED Conference on Languages, Compilers and Tools for Embedded Systems (LCTES), 2015.



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