I am a Ph.D. student in Computer Science at UMASS Amherst working under the supervision of Prof. Narges Mahyar. My research area is Digital Civics with the goals of building technologies to increase communication between the public, event organizers and city officials.

Prior to UMASS I got my M.S. in Computer Science at University of Central Florida under supervision of Prof. Joseph Laviola.

I received my bachelor’s degree in Computer Science as the top student from University of Tabriz, Tabriz, Iran in 2013.




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Code Park: A New 3D Code Visualization Tool

We introduce Code Park, a novel tool for visualizing codebases in a 3D game-like environment. Code Park aims to improve a programmer’s understanding of an existing codebase in a manner that is both engaging and intuitive, appealing to novice users such as students. It achieves these goals by laying out the codebase in a 3D park-like environment. Each class in the codebase is represented as a 3D room-like structure. Constituent parts of the class (variable, member functions, etc.) are laid out on the walls, resembling a syntax-aware “wallpaper”. The users can interact with the codebase using an overview, and a firstperson viewer mode. We conducted two user studies to evaluate Code Park’s usability and suitability for organizing an existing project. Our results indicate that Code Park is easy to get familiar with and significantly helps in code understanding compared to a traditional IDE. Further, the users unanimously believed that Code Park was a fun tool to work with.

Code Park: A New 3D Code Visualization Tool
Pooya Khaloo, Mehran Maghoumi, Eugene Taranta II, David Bettner, Joseph Laviola Jr. Vissoft '17.
[pdf] [video]



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Jackknife: A Reliable Recognizer with Few Samples and Many Modalities

Despite decades of research, there is yet no general rapid prototyping recognizer for dynamic gestures that can be trained with few samples, work with continuous data, and achieve high accuracy that is also modality-agnostic. To begin to solve this problem, we describe a small suite of accessible techniques that we collectively refer to as the Jackknife gesture recognizer. Our dynamic time warping based approach for both segmented and continuous data is designed to be a robust, go-to method for gesture recognition across a variety of modalities using only limited training samples. We evaluate pen and touch, Wii Remote, Kinect, Leap Motion, and sound-sensed gesture datasets as well as conduct tests with continuous data. Across all scenarios we show that our approach is able to achieve high accuracy, suggesting that Jackknife is a capable recognizer and good first choice for many endeavors.

Jackknife: A Reliable Recognizer with Few Samples and Many Modalities
Eugene M. Taranta II, Amirreza Samiei, Mehran Maghoumi, Pooya Khaloo, Corey R. Pittman, Joseph J. LaViola Jr. CHI '17.
Received SIGCHI Best of CHI Honorable Mention Award (top 5%).
[pdf] [Project Page] [Code]