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Master Project

• Did research on Fake news and how to detect it based the utilization of linguistic analysis on the datasets
• Used the filtered datasets to teach a python based deep learning program to understand what type of article was fake or not
• The result of the experiment revealed that utilizing solely deep learning and linguistic analysis was not a good method to detect fake news. Even though the result of was a failure the process of researching and experimenting will help push the problem towards a solution

CS 249: Distributed Computing Java Project

• Participated, designed, and developed a Java RMI application that could communicate between multiple clients and a server host. The purpose of the project was to build a distributed Java application where a Java program on one system could communicate and access objects of another Java program on a separate system.
• I created the prototype for application where the client environment could call a method within the server environment and create a file with various texts. The client would then be able to modify and delete the file. At the same time the file was broadcast to other Java Clients so that they also had access to the files. A cache system was implemented for the purpose of dealing with old files when reading. This system updated files so that the client will never be out of sync with the server host.

CS 218: Topics in Cloud Computing Java Web Project

• Coordinated, designed, developed, and deployed a cloud based application that utilizes a data-set for the generation of a google based heat map. The project was built from the ground up without pre-built software or architecture.
• The main programming language used for the application was Java, where it was used to communicate from the SQL database to the front end. To do this communication method JavaScript and AJAX was utilized to send a request from the client side to the server side. The server would respond and send back data to the client where the JavaScript method is used to update the HTML and Google map application. A third party CSS library was used to speed up the development process.
• Utilized various Google cloud platform services during the development process. Google cloud BigQuery was used as a database platform to store the data in the data-set. Google Kubernetes was a load balancing service that helped with deploying and scaling the application.
• The foundation for the application utilized a RESTful approach where the data is easily access by other applications that want to use the data, but not the application. The reason for this architecture was to decouple the dependencies of the data from the application so that it increase the modularity, usability, and reliability of the data.