2015se107_maneesha-maneesha-rajaratne

Maneesha Rajaratne

An adaptable, exuberant and responsible individual interested in emerging technologies with prior experience Computer Science and Software Engineering skills. I am adaptable for any software engineering life cycle. Myself can be expressed as a good team worker as well as a solo player and quick learner with good communication skills and leadership skills.


Email: maneesharajaratne@gmail.com 
Phone: 07677121527
Course: SE

Interests:Web Development, Machine Learning, Blogging, Dancing, Singing, Hockey

Technical Skills: Java, Python, Android, NodeJs, ExpressJs, Angular-6, MongoDB, SQL, Git, Javascript, HTML5, CSS, AWS

Project Experience

FMRI Data Portal – Online Web Portal
A functional neuroimaging online data portal with 3D visualization
Contribution: UI/UX design, backend of user login/registration
Selected for “Best 8 Projects” 2nd year group projects
(HTML5, CSS, Javascript, PHP, UNITY, WebGL, MySQL)
An Algorithm to distinguish Animal Breeds
Using Asirra Dataset built an ML model to distinguish dogs from cats.
(Supervised Learning, CNN, Deep Learning)
A Lab Reservation System
An online lab reservation system using MEAN Stack.
(Node, Express, Angular-cli-6, MongoDB, Mongoose)
Credit Card Fraud Detection in Finance
A machine learning model to detect frauds from financial datasets provided by Kaggle. Achieved 96% accuracy.
(Supervised Learning, K-Nearest Neighbors)

Work Experience

Research Assistant : Modeling and Simulation Research Group – UCSC (August 2018 – January 2019)
Responsibilities : Building an ML model to detect frauds in financial domain, building a dynamic model using ML to predict the path of a Drone.

Achievements | Awards

• 1st place -Inter Faculty Freshers’ Hockey Tournament 2016

Final Year Project

An Extensible Audio Music Streaming Mobile Application

• An Android mobile application for Sri Lankan music listeners as well as Sri Lankan artists to publish songs and make a profit out of the system.System Includes audio streaming, user analytics, auto suggestion and music recommendation components. Research aspect: auto suggestion & recommendation engine.
(Android, Node, AWS, Python)