Niki Gitinabard
About Me
I am a third year Computer Science PhD student at North Carolina State University. Working with Dr. Collin Lynch and Dr. Tiffany Barnes, at the Center for Educational Informatics, I'm currently modeling different student activities as a social graph and analyzing it. Generally we are trying to use educational data mining to identify useful behavioral patterns of students using online tools in CS courses. Eventually we want to find ways to help students perform better.
If my first hobby is reading novels, travelling is definitely the second one. I hope one day comes that I put a big map on my wall and fill it with marking the places I have visited. I like to know different cultures which makes the range of the books I read really wide, but I still think I need to travel and get to know people and see places to experience life better.
  • Gitinabard, N., Lynch, C. Heckman, S. Barnes, T. Identifying Student Communities in Blended Courses. Proceedings of the 10th International Conference on Educational Data Mining (p. 378-379).
  • Gitinabard, N., Xue, L., Lynch, C., Heckman, S., Barnes, T. (2017). A Social Network Analysis on Blended Courses. GEDM 2017 proceedings(p. 22-26). In: EDM 2017 Extended Proceedings: Workshop Proceedings of the 10th International Conference on Educational Data Mining. Wuhan (China).
Workshop Organizations
My Education
  • PhD Student, Computer Science, 2015-current
    NC State University
  • B.Sc. in Computer Engineering(Software), 2010-2014
    University of Tehran
Work/Research Experience
    • Collaborated in revising O'Reilly's text summary project using deep learning methods and Tensor-Flow
    • Gathered and prepared data for training the deep learning model
    • Trained a text clustering model to categorize user responses to a survey, using scikit-learn and pandas.
    • Collaborated in building large, distributed, and multi-threaded software applications that allow MaxPoint's platform to respond to billions of events each day
    • Designed and built advanced software solutions that scale across hundreds of servers and meet aggressive fault tolerance standards, also implemented architecture and design patterns to help ensure that systems scale well into the future
    • Changed Apache Impala access code to use Apache Spark because of the change in company policies
    • Collaborate with cross-functional teams
    • Experienced using Git and Jira
    • Worked on a distributed graph analyzing system called X-Scale which was the distributed version of their former Project X-Stream
    • Implemented graph algorithms like Triangle Counting and Betweenness Centrality based on the scatter-gather model of the system
    • Ran an analysis on twitter data to compare the run-time of X-Stream and X-Scale
    • Developed in C++
    • Maintained the server side of Cafe Bazaar, the leading app store in the Iranian market, installed on more than 23 million devices
    • Worked as a Python-Django developer, using Django's internal ORM, South migrations, PostgreSQL, Nginx
    • Worked in a team of ten people practicing Kanban, Scrum, and XP
    • Experienced in using Git, Clean Coding, Pair Programming, Code Review, being Scrum Master
    • Utilized Dockers to implement separate test environments and databases on a test server for developers
    • Designed a new architecture for page layouts based on Json instead of SQL DB
Teaching Experience
  • NC State University
    • Data Structures for Computer Scientists
    • Programming Concepts- Java
    • Software Engineering
  • University of Tehran
    • Intelligent Systems
    • Artificial Intelligence
    • Theory of Formal Languages and Automata
    • Introduction to Computing Systems and Programming
  • Riot Predictor
    • Data Driven Decision Making Course
    • Predicting how likely it is for a protest to turn into a riot based on the target of the protest, the issue, number of participants, crime rating in the area, and violence rating of the articles or social media posts. Information was extracted using natural language processing from articles on Bing and Duckduckgo, and another structured database.
    • Developed in Python, used machine learning methods such as neural networks, naive bayes, logistic regression, and random forest
    • Social Computing Course
    • Predicting number of retweets of a tweet, based on its links, hash-tags, mentions, author, etc.
    • Developed in Python and R, Using Snap, MASS, and Neural Networks
    • Course Project for Software Engineering | Spring 2016
    • Collected and analyzed GitHub activity data from 14 “Software Engineering Groups”, defined “Bad Smells” in these projects’ behaviors including their use of issues, milestones, commits and comments, and provided a case-study on them.
    • Built “Bad Smell” early detectors based on our definition
    • Developed in Python, practiced data collection, pre-processing, anonymization, feature extraction, and used z-score for finding outliers.
    • Software Engineering Course
    • Developed a helper tool for Github novice users. Provided them with a choice of commandline help, email bot, and search engine. Email bot and search engine used Stack-overflow search results, sorted by TF-IDF, while the commandline help used a decision tree. The product was also tested on novice users to find the best solution.
    • Developed in Python