2015is045_sachini-sachini-thaksala

Sachini Palliyaguru

An ambitious, self-motivated individual, seeking to start a challenging career as a software engineer, through a fine tuned combination of knowledge and skills being part of an esteemed, supportive company, where I would be able to utilize and enhance my true potentials.


Email: sachini.thaksala@gmail.com 
Phone: 0713256039
Course: IS

Interests:Machine learning, Music, Traveling

Technical Skills: Java, Python, C++, HTML, CSS, PHP, JavaScript, NodeJS, Angular, MySQL, MongoDB, Oracle

Project Experience

SPCS Core banking system – Internship project
A web based banking application designed to operate the core functionalities of the Singapore Police Corporation Bank System, Singapore.
Allocated tasks: Generate jasper reports for different modules by accessing the main database, Front-end and back-end implementation of report generation module, Front-end & back-end implementation of the AGM (Annual General Meeting)-Registration application
Technologies: Spring MVC, Hibernate, Java 8, MS SQL, Jasper Reports, MongoDB, HTML5, Jquery
ECBS (Core banking system) – Internship project
A web based core banking application, implemented with core banking functionalities with customization features for the potential clients in financial sector.
Technologies: Angular 6, Node JS, Type Script, MySQL
“Govi Sewana” – final year research project (SLIIT)- 2018
Web based R&D application to provide insights on predicted trends in paddy harvest and rice demands on district basis in Sri Lanka, and implements a rice-distribution-schedule to optimized the usage of limited resources and minimize harvest wastage.
Machine learning concepts: Recurrent Neural networks (RNN) – LSTM, Genetic Algorithm (GA), Iterated Local Search Algorithm (ILS)
Technologies: Python 3.5, TensorFlow, Django web framework, My SQL
Smart traffic light management system- 2018
An IOT based project, aimed in implementing a feasible solution for traffic congestion in busy cities in Sri Lanka.
Allocated tasks: Implemented the web platform of the system which includes administrative tasks and viewing traffic statistics.
Technologies: Angular5, Type Script, NodeJs, HTML5, CSS, MySQL, Google maps API (javascript)

Work Experience

• Software Engineer Intern – Epic Lanka (Pvt) Ltd. (September 2018 – February 2019)

Achievements | Awards

• Research publication: M. R. S. Muthusinghe, Palliyaguru. S. T, W. A. N. D. Weerakkody, A. M. H. Saranga and W. H. Rankothge, “Towards Smart Farming: Accurate Prediction of Paddy Harvest and Rice Demand”
2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Malambe, Sri Lanka, 2018, pp. 1-6. doi: 10.1109/R10-HTC.2018.8629843
Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8629843&isnumber=8629798

• Microsoft Imagine cup | Smart Seat Belt | National finalists – 2017
• IEEEXTREME Programming Competition | 10.0 – 2016 ( Island rank 40) , 11.0-2017
• IEEE HTC R10 – Humanitarian Technology Products Competition – 2018 | Participation
• NBQSA – 2018, Sri Lankan section of the BCS | Participation.
• She Coders (v 1.0), IEEE WIE Affinity Group- Uva Wellassa University | Participation
• SLIIT CodeFest- 2016, Faculty of Computing Students Community of SLIIT | Participation
• Australian National Chemistry Quiz -2009 | Awarded High Distinction.

Final Year Project

● Towards Prediction of Landslide Susceptibility using Random Forest for Kalutara District, Sri Lanka

• This implements a machine learning based prediction model, integrated with Geographic Information Science (GIS) to identify landslide susceptibility areas in Sri Lanka. The case study area focused on Kalutara district – Sri Lanka, where the highest number of landslides reported in recent years. For the prediction of a landslide event the presence of several conditioning factors such as; terrain and triggering factors and their level of impact to the event is identified here. At the end a landslide susceptibility map indicating landslide prone areas will be generated as the proof of concept.

Technologies: TensorFlow, Python 3.5, QGIS 2.8, Django web framework