14001411_medhavi

Medhavi Siyambalapitiya

I am a self-motivated and adaptable undergraduate who seeks new knowledge about emerging technologies. I am very keen in working in an ever changing dynamic environment and looking forward to take any challenge. I am a good team player with satisfactory communication skills and my ultimate carrier goal is to be one of the best outgoing persons in Software Engineering.


Email: mhasarenu@gmail.com
Phone: 0714770834
Course: SE

Interests: Machine Learning and Neural Computing, Web development, Image Processing, Parallel Computing, Research and Development

Technical Skills: Java SE/EE, C, C++, Python, PHP, HTML, CSS, MySQL, OpenCV, REST, TensorFlow, QT

Project Experience

Neural UI (C++, Machine Learning, Artificial Neural Network, QT, Make, Vim)

An intelligent, light weight tool that increase user productivity by automating repetitive user interactions and adjust itself by learning from user choices and habits

Content Management System for the Official Website of University of Colombo School of Computing
(PHP, HTML, CSS, JavaScript, MySQL, Apache)
A dynamic web site which uses a Content Management System to edit content.
Locomotive Transport System (CodeIgniter, PHP, HTML, CSS, MySQL, JavaScript)
This system enables selecting suitable vehicle according to the amount of passengers, department, purpose and define the journey.
Inventory Control Management System (Java SE, Jasper Reports)
A standalone application which manages inventory, sales and reporting of Sarangee Music Center.

Work Experience

  • Software Engineering Intern at Synopsys (Sri Lanka) Pvt Ltd (August 2017 – January 2018)

Achievements | Awards

  • Participated in IEEE Extreme Programming Competition 2015 and Microsoft Imagine Cup 2014
  • Bronze Award – ASAP Comprehensive Curriculum – Charted Institute for IT(bcs) 2014
  • Certification of Merit High Distinction – Australian National Chemistry Quiz 2012
  • All Island Prize Winner in Spoken and Written English Graded Examinations

Final Year Project

Deep Learning Approach to Guide and Automate Endotracheal Intubation Process
Endotracheal Intubation is an emergency medical procedure of placing a flexible plastic tube into trachea to facilitate breathing for patients who cannot breathe on their own. This study is done as a proof of concept to prove that guiding physician in the Intubation process through a device which can predict directions using a neural network based on images, has a practical potential. The ultimate goal of the research is to build an AI to perform Intubation process.
Technologies : Python, TensorFlow, OpenCV, PyQt5, Convolutional Neural Network, Image Processing