PhD in Computer Science, Arizona State University. 2015 – till now

– Research Assistant in Computer Science, Florida International University. 2013 – 2015

– Bachelor in Computer Engineering, Tehran University. 2009 – 2013



Virtualized Infrastructure, Systems and Applications(VISA Lab), ASU. [Supervisor: Dr. Ming Zhao]

– Heterogeneous Distributed Computing: After decades of development, data technologies have been successfully applied to many disciplines for knowledge discovery and decision making. However, the further growth and adoption of the “big data” paradigm, which promises solutions to urgent societal challenges, face several critical challenges. First, it is challenging to meet the performance needs of modern big data problems which are inherently more difficult, e.g., learning of heterogeneous and imprecise data, and have more stringent performance requirements, e.g., real-time analysis of dynamic data. Coupled with the exponential growth of data, these problems are difficult to solve using conventional hardware with a satisfactory performance. Second, power consumption is becoming a serious limiting factor to the further scaling of big data systems and the applications that it can support. Most existing systems rely on increasing CPUs and DRAMs to achieve higher data processing parallelism and faster data access, which is unfortunately not sustainable given the power constraints. These challenges demand new types of big data systems that incorporate unconventional hardware capable of accelerating data processing and accesses while lowering the system’s power consumption. In particular, GPUs and FPGAs can accelerate data processing tasks with high data concurrency and high loop-carried dependency.

– Mentee-Mentor Deep Neural Systems: Deep learning has gained tremendous attention and success over last decade. Multiple deep neural network architectures have been proposed to solve highly complicated data models. Due to size and complexity of these deep networks, they tend to be computationally expensive, such that they need powerful accelerators such as GPU to reduce training time into a reasonable size. Recently there is an interest in utilizing AI in mobile applications. Current approach is offloading computationally expensive training phase onto cloud, and locate the simple interface on the mobile side. Fortunately there are methods such as information distillation which may enable training feasible on the mobile devices.

Virtualized Infrastructure, Systems and Applications(VISA Lab), FIU. [Supervisor:  Dr. Ming Zhao]

Studying network QoS on the Big Data systems. Big Data is a new trend in all different areas. There are companies which offer 10s to 1000s of nodes to process terabytes of data. A Big Data application goes into different operations such as disk read/write or network communication. In some specific applications, this communication overhead could be less or more. We wanted to study network in such systems, and see how they can affect the QoS target for applications, where the whole infrastructure is shared between multiple applications.

Institute of Studies for Theoretical Physics and Mathematics( IPM ).

Implementing HPC applications in bioinformatic area. Basically I was developing complex algorithms and move them from sequential execution to the parallel version. I was mostly using CUDA and MPI frameworks. Here I have listed three most important works that I was doing here:

Active-Site Finder: A software for prediction of protein’s active sites, developed for biology scientists to predict protein’s active site location.

FoldSim: A software for prediction of protein’s folding. Finding protein’s correct folding is one of the biggest challenges in computational biology.

MemBuilder: A web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS Biomolecular Simulation Program. Here you can access the program


(2) Biookaghazadeh, S., Xu, Y., Zhou, S., & Zhao, M. (2015, October). Enabling scientific data storage and processing on big-data systems. In Big Data (Big Data), 2015 IEEE International Conference on (pp. 1978-1984). IEEE.

(1) M. M. Ghahremanpour, S. S. Arab, S. Biookaghazadeh, J. Zhang and D. van der Spoel (2013) MemBuilder:, “A Web-Based Graphical Interface to Build Heterogeneously Mixed Membrane Bilayers for the GROMACS Biomolecular Simulation Program”, Bioinformatics, doi: 10.1093/bioinformatics/btt680


(1) Rangaswami, Raju, Saman Biook Aghazadeh, and Steven Lyons. “TECHNIQUES AND SYSTEMS FOR LOCAL INDEPENDENT FAILURE DOMAINS.” U.S. Patent No. 20,170,091,055. 30 Mar. 2017.

Work Experiences

EITR Systems, Founder Engineer (January-August 2016)

Modern Informatics Services Corporation,  [Internship]

– Developing core banking information system.

Integrating Core Banking’s solution and it’s dependent applications with the CAS Single Sing On technology.

Analysis and Implementation of Business Processes of banking domain within core context, using JBoss BRMS framework.

–  Researching about implementation of distributed transactions over Spring framework transaction manager. 

Paper Reviews


  • Usenix Fast’ 17, 14th Usenix Conference on File and Storage Technologies (Peer Reviewer)

  • IEEE Cloud 2015, 8th IEEE International Conference on Cloud Computing (Short Paper Reviewer)

  • ICNC 2016, 5th International Conference on Computing, Networking and Communications (Technical Program Committee)

  • ICPADS 2016, The 22nd IEEE International Conference on Parallel and Distributed Systems (Peer Reviewer)

  • ICNC 2017, 6th International Conference on Computing, Networking and Communications (Technical Program Committee)

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s