Sandunika Dissanayake

I am a talented undergraduate who is eager to gain and improve knowledge as I believe myself as a fast self-learner. I have good communication skills with the ability to work well with a diverse team. I hope to work as a Software Engineer and achieve the best in my career life.

Email: sandunikahansinie@gmail.com
Phone: 0778974927
Course: SE

Interests: Research and Development, Web Development, OOD, Image Processing, Travelling, Dancing

Technical Skills: Java, J2EE, HTML, CSS, Bootstrap, JavaScript, PHP, REST, NodeJS, AngularJS, Embedded JS, Ionic, Firebase, Python, PyQt5 OpenCV, MySQL

Project Experience


FinNetwork (J2EE, JavaScript, Hibernate, REST, C3 JS, D3 JS)
A financial network analyzing tool to facilitate financial research by studying past behaviors of financial
institutions and relationships between them based on FEIII2018 dataset. A research project done with
Maryland University. USA.

PIXSELL (NodeJS, Ionic, Firebase, EJS)
A micro level investment platform based on web advertising with main functionalities including website
ad-blocks monitoring, demand base bidding services for adblocks, and mobile application.
MobileTrain (Ionic, Firebase, Visual Studio Code)
This project was done as a proof of concept to prove that tracking train locations using GPS locations,
has a practical potential. It analyzes GPS coordination variations real time and identify train location.
FindYourRaft (CodeIgniter, PHP)
A web based system to advertise Kitulgala adventure activities where adventure providers can
advertise their services, packages, rates and travelers can reserve adventure packages as they need.

Work Experience

  • Intern Software Engineer : 2017 Aug – 2018 Jan

Achievements | Awards

  • IEEEXtreme 10.0 – Sri Lanka Rank : 55
  • Selected for the inter-university hackathon organized by the ISACA student group of UCSC
  • Diploma in Information Technology : ESOFT

Final Year Project

Guide and Automate Endotracheal Intubation Process
Endotracheal Intubation is an emergency medical procedure that is performed on patients who cannot breathe on their own. This study is done as a proof of concept to prove that guiding physician in the Intubation process through a device which can predict directions using a neural network based on the images, has a practical potential.
Technologies : Python, OpenCV, TensorFlow, PyQt5, Convolutional Neural Networks, Image processing