14001179_isuranga

Isuranga Perera

Technically savvy software engineering student with a strong ability to determine projects’ operational feasibility and design, and implement correlating solutions.


Email: isurangamperera@gmail.com
Phone: +94717357034
Course: CS

Interests: Blockchain Architecture, Natural Language Processing, Middleware Architecture, Image Processing, Machine Learning, Information Security, Data Analytics, Open-source Contribution, Parallel Computing

Technical Skills: Java, C/C++, Javascript, Python, Golang, PHP, SQL, React, NodeJS, Git, Maven, Ruby, Shell Scripting, Apache CXF, MSF4J

Project Experience

WS-Trust Implementation for WSO2 IS (GSoC 2017) ( Java, Maven, Apache CXF, MSF4J )
Implementation of ‘WS-Trust’ in WSO2 Identity Server 6.0.0

Bahmni Notifications on Patient Events (GSoC 2018): ( Java, Maven, React )
Implementation of a publisher-subscriber module for Bahmni

HMS ( Javascript, HTML, CSS, PHP, Restful Web Services, AWS )
Hospital Management System for Central Chest Clinic. This is our 2nd-year group project.

Expressio ( Android Studio, Twitter API, Apache OpenNLP )
Mobile app for detect depression through natural language processing technology and incorporation of
machine learning algorithms.

Complaint Management System for CEA  ( PHP, CodeIgniter, MySQL, Ajax, Jquery )
Web-based system to manage complaints received by Central Environment Authority.

Command Line Tool for WSO2 Identity Server ( Golang, SOAP protocol, Restful Web Services )
command line tool which provides operations on Identity Server.

Work Experience

  • Sysco LABS – Software Engineering Intern – 2017/2018 (6 months)
  • Open-Source Contributor – WSO2, OpenMRS, Bahmni, Apache Syncope, SCoRE

Achievements | Awards

  • Google Summer of Code 2018 – OpenMRS
  • Google Summer of Code 2017 – WSO2
  • 1st runner-up National Information Security Quiz 2017

Final Year Project

Research on the Optimization of Bitcoin Address Clustering

Attackers and other malicious users can use blockchain analysis techniques to extract personal details of users. Address clustering is one of the major blockchain analysis technique which allows the attackers to extract users’confidential information by tracing transactions back to owners. However, existing address clustering techniques focus more on the accuracy of clusters than the speed. In this research we are aiming to
optimize(memory usage & time consumption of) Bitcoin address clustering without affecting the clustering
accuracy.