Developing a large scale knowledge graph of available public data for easy querying.Contributing in creating a web application that will help users interact with this knowledge graph.Using scripting languages to create JSON mapping among the data to create semantics.

Teaching Assistant for CSE 566 - Software Project, Process and Quality Management.Instructional Assistant for SER 316 - Software Enterprise: Construction and Transition.Instructional Assistant for SER 460: Software Analysis and Design.

• Implemented Tech Stack: HTML, CSS, JavaScript, ReactJS, NodeJS, ExpressJS, MongoDB, Php, XML, Postman, Git, Github.• Developed Full Stack web applications.• Developed Applications on various JavaScript frameworks: React.js, Express.js, Node.js also used MongoDB for database.• Developed responsive web applications as per software requirements specification (SRS).• Conducted unit testing, API testing (Postman), and acceptance testing.• Performed static code analysis.• Implemented Selenium to conduct automated testing.• Applied Agile Methodologies and participated in SCRUM ceremonies

• Implemented Tech Stack: Python, Tensorflow, CNN, Mask R-CNN, OCR, AWS, Flask, OpenCV, NumPy, Pandas, Pytorch.• Built Deep Learning Model to detect objects using the Tensorflow library.• Created CNN and Mask R-CNN Models for detecting complex objects within images.• Experimented with Image classification, Object detection and Image Segmentation to improve results.• Improved the prediction results by 87 percent.• Experimented with other models and libraries to improve performance, for eg: Pytorch.• Implemented OpenCV for image processing, and used AWS and flask to deploy the model on application.

• Implemented Tech Stack: Python, Pandas, Numpy, matplotlib, seaborn, Gramian Angluar Field (GAF), Statistical Analysis System(SAS), OpenCV, Data cleaning, Data Modeling, Image Classification, Clustering, K-means, Conv. LSTM.• Led a collaborative effort with Curtin University, Australia, to pioneer the development of an advanced model aimed at uncovering the distinctive strategies employed by football teams.• Engineered a robust time-series model utilizing historical data to predict the strategies deployed by football teams during live matches.• Utilized Statistical Analysis System (SAS) to manage multivariate data, visualize it, and convert Cartesian coordinate plots to polar coordi-nates, enhancing football data analysis for predictive modeling.• Applied expertise in data cleaning, preprocessing, and analytics to enhance the accuracy and reliability of the predictive model.• Leveraged adv. techniques:- Gramian Angular Field (GAF) and Convoluted LSTM to extract valuable insights from complex football data.• Implemented an image classification model to further refine the analysis and predict match outcomes with precision.• Demonstrated proficiency in utilizing a diverse tech stack tailored to the demands of data analytics in the sports domain.

• Implemented Tech Stack: Python, Pandas, Numpy, matplotlib, seaborn, Gramian Angluar Field (GAF), Statistical Analysis System(SAS), OpenCV, Data cleaning, Data Modeling, Image Classification, Clustering, K-means, Conv. LSTM.• Led a collaborative effort with Curtin University, Australia, to pioneer the development of an advanced model aimed at uncovering the distinctive strategies employed by football teams.• Engineered a robust time-series model utilizing historical data to predict the strategies deployed by football teams during live matches.• Utilized Statistical Analysis System (SAS) to manage multivariate data, visualize it, and convert Cartesian coordinate plots to polar coordi-nates, enhancing football data analysis for predictive modeling.• Applied expertise in data cleaning, preprocessing, and analytics to enhance the accuracy and reliability of the predictive model.• Leveraged adv. techniques:- Gramian Angular Field (GAF) and Convoluted LSTM to extract valuable insights from complex football data.• Implemented an image classification model to further refine the analysis and predict match outcomes with precision.• Demonstrated proficiency in utilizing a diverse tech stack tailored to the demands of data analytics in the sports domain.