Amoda Dissanayake

I’m a self- motivated and highly dedicated individual who love to see challenges as opportunities to explore and improve my knowledge. I enjoy building creative solutions especially when it involves new technologies.

Course: CS

Programming, New technologies ,Web Development, Operating Systems

Technical Skills: 
NodeJS, ReactJS, Java, PHP, HTML, MySQL, Python, Docker, MongoDB, CouchDB, C++

Project Experience

Barcode Reader integration to POS devices
New feature integration to Point Of Sale devices to support barcode reader functionalities (NodeJS, ReactJS, ReduxJS, PHP)

Dockerized Solution for Sales Demo
Enables full functioning local instances of POS app for sales demo purposes (Docker, NodeJS)

Resume Trekker – CV and Recruitment Management System for IFS R&D
Manages full recruitment life cycle including CV filtering, interview scheduling and tracking interview rounds(PHP, Javascript, JQuery, HTML5, CSS3, MySQL, AJAX)

See More – A Project On Eye Health
Project that addresses vision impairment issues (PHP, Javascript, MySql, ideamart SMS API)

Resource File Manager
Helps to manage and maintain static resources of a given project (Java, Maven)

Work Experience

  • CAKE LABS (Pvt.) Ltd. (Currently Sysco Labs) – Software Engineer Intern (Sep 2016 – Feb 2017)
  • ISACA UCSC Student Group – Executive Board member (2015/2016)

Achievements | Awards

  • Code Sprint 2015 (First Inter-University Hackathon) – First Runners Up
  • IEEEXTREME 9.0 Student Ambassador
  • Nominated for best performance award for internship placement programme

Final Year Project

Computational Approach for Homology Discovery of Keratin Digestion Genes in Zebrafish

Industries such as leather industry face bottlenecks due to its necessity for keratin processing but interestingly some organisms have natural capability to do so. This is a bioinformatics based research that computationally predicts the genes that involved in performing such functionality. In the first phase of the research, a homology discovery based gene prediction towards the function of keratin digestion using known datasets is performed while in the next phase, statistically learning is used for the prediction of gene inactivation.