Kalindu Chethiya Perera

A self-motivated individual who always learns from experience. Excellent team player and a team leader. Self-learner who tries hard until the work successfully completed. Individual with Highly adaptive and multi tasking abilities with efficient workload management.

Email: kkc199408@gmail.com
Phone: 071 1850868
Course: IS

Interests: QA, BA, Test automation tools, New technologies, IOT, Environmental friendly applications, Project Management, Big data

Technical Skills: Python, Mysql, CSS,PHP, HTML, GIS, WordPress, BPM,UML

Project Experience

Project TRAWATER (Imagine Cup finals) | JAN 2016 – APR 2016
An app and a hardware sensor to check the purity of water. Also Provide assistance in finding places where pure water for travelers. Created as community-driven platform.
2nd year learning project (House Of Diamante’) JAN 2016 – DEC 2016
A website for online designing of jewelry. VR(Virtual Reality) functionality to virtually wear the jewelry
Completed admin page with backend functions

Work Experience

Internship at OrangeHRM | AUG 2017 – JAN 2018
Bug verification for customer success team ,Verification of new features, improvements to the system
Manual testing was done by creating test cases using the System Requirement specification and bug reports.
Work collaboratively with developers to reproduce bugs and check the fixes for bugs.

Achievements | Awards

  •  Sri Lankan National finalist of Imagine Cup 2016
  • Participated for SLIIT CODEFEST 2015
  • Selected for top 16 of FIONTRAI business development competition done by University Of Sri Jayawardenapura
  • Completed CCHRM(Certificate In Human Resource Management) from IPM

Final Year Project

An Enhanced model for wildfire Spread prediction

Even Though the severity of the wildfire crisis has been steadily increasing in the recent years, mostly only the developed countries have the necessary resources required for a systematic wildfire disaster management solution. One of the major reasons of this being, it requires multiple environmental variables in order to accurately calculate the Rate of Spread (RoS) of a wildfire. Therefore the aim of this research is to investigate the possibility of optimizing a surface wildfire behavior model, by reducing the number of variables and derive an optimized model that can be used to reduce the resources and costs associated with implementing a wildfire disaster management system in developing countries.