Research Assistant in R.I.T.
Data scientist with a demonstrated ability to deliver valuable insights via data analytics and advanced data-driven methods.
I am a third year Ph.D. student in Computing and Information Science at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT) under the advisement of Prof. Matt Huenerfauth. My research goal is to build technologies that address real-world problems by integrating data-driven methods and human-computer interaction. I am interested in investigating human needs and challenges that may benefit from advancements in artificial intelligence. My research involves the application of machine learning and analytics research to benefit people with disabilities, especially assistive technologies that model human communication and behavior such as sign language avatars.
Ph.D. Student - Rochester Institute of Technology (RIT), 2015 -
Conducted research on modeling 3D motion data of human movements, based on a large dataset of recordings of people performing American Sign Language (ASL), to create predictive models of aspects of human movement, which can be used to synthesis ASL animations.
Completed individual research projects in the areas of feature engineering, optimization, classification, regression, clustering, and visualization – using Python, R and SQL – in courses on Quantitative Foundations, Statistical Machine Learning, Regression Analysis, and Intro to Big Data.
Master of Science - University of Mosul, 2008 - 2011
Master’s Thesis: Implementing Real-Time Appliances in Heterogeneous Operating Systems.
Bachelor of Science - University of Mosul, 2000 - 2004
Awards: ranked 2nd out of 73 B.S.
Certifications - Udacity, Coursera, ... etc
Trained Gradient Boosted Regression Trees model to adjust signing speed in ASL. My model lowered RMSE by 23.8% compared to the state-of-art models.
Analyzed data of ASL recordings using Python and engineered features based on the sentence syntax, for training and evaluating classification models to predict, where to insert pauses in ASL animations. Trained Linear-Chain CRF, Tree Based, and SVM models. My model outperformed the baseline with 80% F1-Score accuracy.
I built a framework to evaluate the performance of ad networks. I measure the performance along five dimensions: Memory, CPU, Network, System calls, and Energy. I evaluate the 10 most popular ad networks using my toolchain. This analysis assists Android developers with various developments recommendations.
Analyzed the locational distribution of spatial reference points established by an ASL signer in motion captured dataset and modeled them using Gaussian Mixture Model (GMM) in three most common pointed clusters which helped in improving the pointing feature of existing ASL animation tool.
Used statistical techniques for hypothesis testing and regression modeling to predict pause duration in ASL animations. Prepared data, detected outliers, transformed data, selected features, and evaluated models in R.
The goal of this research is to develop technologies to generate animations of a virtual human character performing American Sign Language. In current work, we are investigating how to create tools that enable researchers to build dictionaries of animations of individual signs and to efficiently assemble them to produce sentences and longer passages.
I am generating animated ASL stimuli for linguistic research experiments, including minor variations in handshape, location, orientation, or movement. This technology can produce stimuli for display in experimental studies with ASL signers, to study ASL linguistics.
Sedeeq Al-khazraji, Larwan Berke, Sushant Kafle, Peter Yeung, and Matt Huenerfauth. 2018 (to appear). "Modeling the Speed and Timing of American Sign Language to Generate Realistic Animations." The 20th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '18),
Conference acceptance rate of 25% (preliminary)
Sedeeq Al-khazraji. 2018. "Using Data-Driven Approach for Modeling Timing Parameters of American Sign Language." In Proceedings of the Doctoral Consortium of the 20th ACM International Conference on Multimedia Interaction.
Sedeeq Al-khazraji, Sushant Kafle, and Matt Huenerfauth. 2018. "Modeling and Predicting the Location of Pauses for the Generation of Animations of American Sign Language." In Proceedings of the 8th Workshop on the Representation and Processing of Sign Languages: Involving the Language Community, The 11th International Conference on Language Resources and Evaluation (LREC 2018),
Jigar Gohel, Sedeeq Al-khazraji, Matt Huenerfauth. 2018. "Modeling the Use of Space for Pointing in American Sign Language Animation." Journal on Technology and Persons with Disabilities, California State University, Northridge.
Dhananjai Hariharan, Sedeeq Al-khazraji, Matt Huenerfauth. 2018 (to appear). "Evaluation of an English Word Look-Up Tool for Web-Browsing with Sign Language Video for Deaf Readers." Universal Access in Human-Computer Interaction, Lecture Notes in Computer Science.
Sedeeq Al-khazraji and Matt Huenerfauth. 2017. Modeling the Distribution of Spatial Reference Points Established in Space by American Sign Language Signers in a Motion Capture Data Corpus. 10th
Annual Graduate Showcase, Rochester, New York, USA. November 3, 2017. Poster Presentation.
"Best Poster Award"
Sedeeq Al-khazraji and Matt Huenerfauth. 2017. Modeling the Speed and Timing of American Sign Language to Generate Animations. Effective Access Technologies Conference, Rochester, New York, USA. April 21, 2017. Poster Presentation.
Sedeeq Al-khazraji and Matt Huenerfauth. 2017. Modeling the Speed and Timing of American Sign Language to Generate Animations. Move 78 Retreat on Artificial Intelligence, Rochester Institute of Technology, Rochester, New York, USA. February 17, 2017. Poster Presentation.
Completed individual research projects in the areas of feature engineering, optimization, classification, regression, clustering, and visualization – using Python, R, and SQL – in courses on Quantitative Foundations, Statistical Machine Learning, Regression Analysis, and Intro to Big Data.
National Science Foundation (NSF-REU) Mentored undergraduate students in Summer 2017 on research methods and statistics.
Taught courses in Computer Science, e.g. Software Programming, Structured Programming, Distributed Databases, and Linux Operating System. Served on various university committees.
Developed multiple applications for companies and large government organizations, e.g. the Department of Education for the City of Nineveh. Gathered user requirements, defined system functionality, and wrote code in Visual C#, Visual Basic, and ASP.NET, Matlab, and C++, using various database management systems (e.g. Microsoft SQL Server).
Developed applications for individual customers and small businesses. Gathered user requirements, defined system functionality, and wrote code in Visual Basic and C++, using database management systems such as SQL Server, Oracle, and Microsoft Access.
Full scholarship and stipend support for Ph.D.
I ranked 2 of 73 students.
Modeling the Distribution of Spatial Reference Points Established in Space by American Sign Language Signers in a Motion Capture Data Corpus. The Annual Graduate Showcase, Rochester, New York, USA. November 3, 2017.
If you have any questions please do not hesitate to contact me.
102 Lomb Memorial Drive
74-1620 Computing and Info Sciences
Rochester, NY 14623