Bhanuka Gamage

Colombo · Sri Lanka · (9477) 834-1049 · [email protected]

I'm keen on solving real world problems using my knowledge in machine learning, python and swift. I also enjoy attending networking meetups, meeting new people, and widening my network.


Machine Learning Engineer

Staple, Singapore
March 2021 - Present

iOS Engineer

Map72, Malaysia
December 2020 - Present


Monash UReview, Malaysia
November 2019 - Present

Software Developer (Machine Learning)

Map72, Malaysia
February 2020 - November 2020

Machine Learning Intern

Map72, Malaysia
November 2019 - February 2020

Peer Assisted Student Session (PASS) Leader

Monash University, Malaysia
March 2019 - December 2020

Software Engineering Intern

WINHE Software Engineering Academy, Sri Lanka
October 2017 - February 2018


Monash University Malaysia

Bachelor of Computer Science (Honours)
Machine Learning

GPA: 4.00 (First Class Honours)

February 2020 - December 2020

Monash University Malaysia

Bachelor of Computer Science
Advanced Computer Science

GPA: 3.75

February 2018 - February 2020

Monash College Sri Lanka

Diploma in Engineering (IT)
Introduced to computer science principles such as sorting, big o and good design. Also allowed me to explore the business side of building compeling software.

GPA: 3.625

February 2017 - October 2017

Deep Learning Specialization
Gave a good theoretical foundation for the concepts in deep learning and how to use deep learning to aid real world software.

February 2017 - October 2017

ESOFT Metro Campus Sri Lanka

Diploma in Web Engineering (Pearson Assured Accreditation)
The start of my programming career, where the joy of working with code and people was ignited and the never-ending passion for programming began.
February 2017 - October 2017

St. Joseph’s College Sri Lanka

GCE Advance Level
April 2014 - August 2016


Programming Languages & Tools
  • Rest Architecture
  • Progressive Web Applications
  • Single Page Applications
  • MVVM Architecture
  • MVC Architecture


  • Higher Degree by Research Pathway Scholarship 2020 - Monash University Malaysia
  • Nominee for Sir John Monash Award 2019 - Monash University Australia
  • Monash High Achiever Award 2019 & 2020 - Monash University Malaysia
  • Finalist at the 20th APICTA Competition - MSC APICTA Malaysia
  • High Achiever Award in Engineering (IT) - Monash College Diploma Sri Lanka
  • Global Award for Excellence in Engineering Mobile Apps - Monash College Diploma Sri Lanka
  • Global Award for Excellence in Computer Systems, Networks & Security - Monash College Diploma Sri Lanka
  • Josephian College Colorsman for Basketball - St.Joseph's College Sri Lanka
  • Western Province Sri Lanka Basketball Colorsman - St.Joseph's College Sri Lanka


BAITRADAR - A multi-model clickbait detection algorithm using deep learning

Undergraduate project accepted for conference ranked 13th in computer science

Coming soon...


Efficient Generation of Mandelbrot Set Using Message Passing Interface

FIT3143 - Parallel Computing Project

With the increasing need for safer and reliable systems, Mandelbrot Set's use in the encryption world is evident to everyone. This document aims to provide an efficient method to generate this set using data parallelism. First Bernstein's conditions are used to ensure that the Data is parallelizable when generating the Mandelbrot Set. Then Amdhal's Law is used to calculate the theoretical speed up, to be used to compare three partition schemes. The three partition schemes discussed in this document are the Naïve Row Segmentation, the First Come First Served Row Segmentation and the Alternating Row Segmentation. The Message Parsing Interface (MPI) library in C is used for all of the communication. After testing all the implementation on MonARCH, the results demonstrate that the Naïve Row Segmentation approach did not perform as par. But the Alternating Row Segmentation approach performs better when the number of tasks are <16, where as the First Come First Served approach performs better when the number of tasks is ≥16. 2020

Simulation and Analysis of Distributed Wireless Sensor Network using Message Passing Interface

FIT3143 - Parallel Computing Project

Wireless Sensor Networks (WSN) are used by many industries from environment monitoring systems to NASA's space exploration programs, as it has allowed society to monitor and prevent problems before they occur with less cost and maintenance. This document aims to propose and analyze an efficient inter process communication (IPC) architecture using a nearest neighbor/grid based socket architecture. A parallelized version of the AES encryption algorithm is also used in order to increase the security of the WSN. First the proposed architecture is compared and contrasted against other well established architectures. Next, the benefits and drawbacks of the AES encryption algorithm is elucidated. The Message Parsing Interface (MPI) library in C is used for the communication while OpenMP is used for parallelizing the encryption algorithm. Next an analysis is performed on the results obtained from multiple simulations. Finally a conclusion is made that the grid based IPC architecture with AES parallel encryption helps WSNs maintain security in communication while being cost and power efficient to operate. 2020


Apart from being a software engineer, I enjoy most of my time being outdoors. I enjoy hiking, photography and long road trips. Doing the Everest base camp trek with my significant other is one of my life goals.

When forced indoors, I enjoy playing racing simulators and watching movies. I also spend a large amount of my free time exploring the latest advancements in the apple eco-system and tech.