chulani_weerathunga

Chulani Weerathunga

I consider myself as an energetic and self-motivated individual who is willing to accept challenges in the journey towards the achievement of goals and I believe that I’m a good team player and a fast learner.


Email: wchulaniweera@gmail.com
Course: CS

Interests:
Business Analysis, R&D, Music, Volunteering

Technical Skills: 
Java SE, C, Python, PHP, JavaScript, JSF, Spring, Hibernate, Prime faces, REST API, Maven, HTML, CSS, Ajax, MySQL, SVN, MATLAB, R

Project Experience

“Viaggio” Trip Itinerary Builder (PHP, MySQL, JavaScript, CSS, Bootstrap, Ajax, HTML)
A web based project which facilitates tour planning of a travel agency. It helps a tourist to plan his tour according to his interests and helps the travel agent to schedule and organize the activities in an efficient way.
Digital Signage Version III (JSF, Hibernate, Spring, Maven, MySQL, Prime faces)
It is a web and mobile based system designed for Dialog Axiata to cater the scheduling of content in digital signage devices.
In App In Web (JSF, Hibernate, Spring, Maven, MySQL, Prime faces)
It is also designed for Dialog Axiata to allow public to publish advertisements in web and mobile applications.
POS System for Diner (Java RMI, MySQL)
This project is basically about the functioning of a restaurant which facilitates message passing between the restaurant, kitchen and stores of the client.

Work Experience

  • InovaIT Systems (pvt) Ltd. – Trainee software engineer (Java) from September 2016 to January 2017.

Achievements | Awards

  • University Colors – Chess (2014)
  • “Excellentia” awards ceremony (2015) AIESEC Colombo Central – Award for best GIPer (Matching)
  • Participated in IEEE Xtreme (2014, 2015, 2016), SLIIT Codefest (2014) and Imagine cup (2015, 2016)
  • President’s Guide
  • Sri Lanka Festival of Music (2006) – All island finalist (playing-piano)

Final Year Project

Deep end to end onset detection for extracting and classification of public radio broadcast context.

This research studies on the applicability of the technique ‘onset detection’ for the extraction and classification of radio broadcast context. The main aim of this research is to assist a deep automated analysis for the application levels of radio broadcasting context monitoring process.