Lasini Liyanage

I am an enthusiastic, dedicated and organized individual, eager to explore, learn new things; seeking an employment opportunity which will allow the development and growth of my existing skills. I value work ethic. I can work unsupervised or as a member of a team and I aim to use my skills to make a positive contribution to the workforce.

Email: lasini.1021@gmail.com 
Phone: 0769235676
Course: IS

Interests:Machine Learning, Data Analytics, GIS, R&D, Web Development

Technical Skills: JavaSE, Python, C++, PHP, JavaScript, NodeJS, AngularJS, MySQL, MongoDB

Project Experience

A smart surveillance platform enabling real-time detection, recognition, and tracking of individuals entering university premises and identification of unauthorized individuals accessing geo-fenced locations, working hand in hand with cloud-based storage and administration to address security concerns of universities and institutes
Toyota Explore
Android mobile application to explore details about Toyota car models
Garage Partner
Web application to search nearby garages in case of an emergency, request assistance and make appointments
Information management system to manage locations of elephants, electric fence location information, elephant rumble audios and videos collected from users
Pharmacy Management System
Web based pharmacy management module for a hospital with stock, drug dispense, drug order, and drug details management
Hotel Management System
Stand-alone application which automate day-to-day work flow of a hotel varying from room management to event, catering, stock, finance, transport and HR management

Work Experience

Quality Assurance Engineer – Intern
XONT Software (Pvt) Ltd | Aug 2018 – Feb 2019

Achievements | Awards

• Awarded with Dean’s List Honors for 2015,2018 academic semesters by SLIIT
• Merit based scholarship awardee for 2015,2018 academic semesters by SLIIT
• Got selected to be among the 20 teams to represent SLIIT at NBQSA 2018 under Business Category
• SHECODERess V1.0 – 2017 | Participant
• IEEEXtreme 10.0 (Country Rank:40)
• IEEEXtreme 11.0
• SLIIT CodeFest – 2016 | Participant

Final Year Project

A Landslide Susceptibility Prediction Model using Random Forest for Kalutara District, Sri Lanka

• A proof of concept implementation to predict susceptibility of landslides in Kalutara District, employing geospatial technologies with machine learning techniques.