Nathaliya Jayawardena

I’m an energetic, dynamic, hardworking individual who is willing to accept challenges that will come across my journey towards success.

Course: IS

Interests: Travelling, Doodling and Reading

Technical Skills: Project Management, Business Process Modelling Requirement Engineering Python, MYSQL, PHP, HTML, Bootstrap, Joomla

Project Experience

Common Alert System : A disaster management system, developed for the Disaster Management Centre of Sri Lanka, that uses web scraping techniques to gather disaster details throughout the world, and alert them to the authorities.

LeafOfLife : A mobile application that uses image processing and machine learning technologies to detect brown spot disease pattern, present in the rice leaf.

Watch Over Me : A mobile application that extends Common Alert System to provide disaster notifications, based on geographical location of an end user.

Vendor Evaluation Projects : Evaluated many leading ERP offering companies for ERP establishments as an ERP Consultant Trainee.

Work Experience

Ernst & Young
IT Advisory, Intern (2016 September -2017 January)
AIESEC in University of Colombo
AIESEC COLOMBO CENTRAL, Vice President Finance (2015 July -2016 July)

Achievements | Awards

  • IMAGINE CUP 2016 Finalist
  • Innovation Category & World Citizen Categories
    AIESEC in University of Colombo (Excellentia Awards Night 2015)
  • Best Project – Beyond the Ability 2.0
  • Chartered Institute of Management Accountants Sri Lanka
    Exams Completed – Managerial stage

Final Year Project

How Dirty is your data? Mitigating the effects of unclean data on Business Intelligence.

The domain of the research mainly lies within dirty data management. This research addresses many adversative effects of dirty data within the business field. The research will showcase theses effects through case studies for better understanding. It analyzes many string matching, name matching and record linkage techniques to give an optimum controlling technique for a Sri Lankan data set plagued with dirty data.