Samali Supathma

Fantasist, self-motivated and results-driven person.Enjoy being part of and motivating a team. Thrive in highly pressurized environment.

E-mail: | phone: +94788383243 | Course: IS

A dynamic, self-motivated and results-driven person. Achieve goals while, remaining focused on providing an exceptional standard of service. I enjoy being part of, as well as managing and motivating, a successful and productive team and thrive in highly pressurized and challenging working environment

Interests: Travelling, Reading, Swimming, Music, Bio informatics, Indigenes cultivation

Technical Skills: JavaSE, Selenium, JIRA, Confluence, Mindjet, JavaScript, XShell, PHP, HTML, CSS, MySQL, Mockups, Bizagi, Eclipse, MATLAB, Weka

Project Experience

Biostatic approach to diagnose diseases automatically, using computational “Nadi Patterns”
Side effect free system to diagnose diseases using computational “Nadi Patterns” unique to diseases.,

“Railway travel planner”
Web based system that helps travelers to find both direct and indirect train schedules to their destinations.

E learning course for “Jathaka katha”
Online course for those who are interested in “Jathaka katha”. Main objective is to increase the public awareness about “Jathaka Katha” and increase accessibility to the materials.

Work Experience

London Stock Exchange Group –MillenniumIT Software (Pvt) Ltd, Malabe (Aug 2015 – Mar 2016) :
As a Quality Assurance Engineer Intern, carried out testing in the areas of functional, usability, System, regression, multi user, alert testing and test automation. Designed and developed test scenarios, test cases and automation test scripts

Achievements | Awards

• Best Industrial Training : Among top four students for best performance (2016)
• Unilever Lipton Talent Hunt : All Island Competitions Third Place (2014)
• Cadet band sergeant : Led platoon from sixth place to third place in all island competitions (2011)
• Interfaculty swimming relay : Third place (2014)

Final Year Project

Biostatic approach to diagnose diseases automatically, using computational “Nadi Patterns”

Identify a side effect free, low cost method to diagnose diseases using computational “Nadi Patterns”. A system will be trained to identify “Nadi patterns” unique to diseases using pattern recognition techniques and low cost Arduino platform base sensor module. As the first phase the research focus on “Type 2 diabetes mellitus”

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