Pubudu Sanjeewani Thihagoda Gamage

Dr. Pubudu Sanjeewani Thihagoda Gamage

Associate Lecturer (Education Focused) (ACDF)

Details

Open to

  • Masters Research or PhD student supervision
  • Industry Projects
  • Collaborative projects

About

I currently serve as an Associate Lecturer (Education Focused) (ACDF) at RMIT University, where I teach several courses in artificial intelligence, data science, and video games development. My research primarily focuses on deep learning, computer vision, machine learning, generative AI, reinforcement learning, and the development of explainable and robust AI models for practical applications, particularly in the area of digital health.

Academic positions

  • Associate Lecturer
  • RMIT University
  • School of Computing Technologies
  • Melbourne, Australia
  • 1 Jul 2025 – Present
  • Research Fellow
  • Griffith University
  • Institute for integrated and the Intelligent Systems
  • Brisbane, Australia
  • 19 Aug 2024 – 31 Dec 2024
  • Postdoctoral Research Assistant
  • RMIT University
  • School of Computing Technologies
  • Melbourne, Australia
  • 25 Oct 2024 – 15 Dec 2024
  • Sessional Academic: Computing Technologies
  • RMIT University
  • School of Computing Technologies
  • Melbourne, Australia
  • 14 Jul 2023 – 30 Jun 2025
  • Postdoctoral Research Assistant
  • RMIT University
  • School of Computing Technologies
  • Melbourne, Australia
  • 13 Jul 2023 – 22 Sep 2023

Non-academic positions

  • Machine Learning Engineer
  • Smart AI Connect
  • Brisbane, Australia
  • 27 Jun 2022 – 30 Sep 2023
  • Software Engineer
  • Sierra Construction Pty Ltd
  • Telecom
  • Sri Lanka
  • 16 Sep 2017 – 3 May 2019
  • Software Engineer
  • SIoT Services Pvt Ltd
  • Colombo, Sri Lanka
  • 1 Jun 2017 – 15 Sep 2017
  • Software Engineer
  • Vogue Tex Pvt Ltd.
  • Matara, Sri Lanka
  • 6 Jun 2016 – 31 May 2017

Degrees

  • Ph.D, Computer Science
  • Central Queensland University
  • Australia
  • 2019 – 2022
  • Bachelor Degree (Honours) Level, Engineering and Related Technologies
  • University of Ruhuna
  • Sri Lanka
  • 2011 – 2016

Teaching interests

Postgraduate Level:

• Semester 1 2026 - COSC2527 Games and AI Techniques - 159 students 

• Semester 1 2026 - COSC2820 Advanced Programming for Data Science - 245 students 

• Semester 2 2025 - COSC2820 Advanced Programming for Data Science - 307 students (OSI: 4.12/5, CES Score: 4.03/5) (This course was recognized as one of the top courses with the highest student satisfaction, with enrolments exceeding 300 students.)

• Semester 2 2025 - COSC2667/2777 Data Science & AI Postgraduate Project - Academic Supervisor for 15 capstone projects

• Semester 1 2025 - COSC2793 Computational Machine Learning - 148 students

• Semester 2 2024 - COSC2670 Practical Data Science (with Python) - 499 students
• Semester 2 2024 - COSC2531 Programming Fundamentals - 536 students (This course was recognized as one of the top 5 courses in the School of Computing Technologies, RMIT University.)

• Semester 1 2024 - COSC2793 Computational Machine Learning - 99 students 

• Semester 2 2023 - COSC2779 Deep Learning - 60 students

Undergraduate Level:

• Semester 1 2026 - COSC3144 Games and AI Techniques - 62 students 

• Semester 1 2026 - COSC2815 Advanced Programming for Data Science - 92 students 

• Semester 1 2026 - COSC2960 Foundations of Artificial Intelligence - 752 students

• Semester 1 2026 - COSC3094 Foundations of Artificial Intelligence - 752 students

• Semester 2 2025 - COSC2815 Advanced Programming for Data Science - 133 students 

• Semester 1 2025 - COSC2673 Machine Learning - 284 students
• Semester 1 2024 - COSC2673 Machine Learning - 142 students 
• Semester 1 2024 - COSC2960 Foundations of Artificial Intelligence for STEM - 943 students
• Semester 2 2024 - COSC2960 Foundations of Artificial Intelligence for STEM - 1813 students
• Semester 2 2024 - COSC2738 Practical Data Science (with Python) - 499 students

• Semester 2 2023 - COSC2972 Deep Learning - 60 students

Research interests

  • Computer vision
  • Deep learning
  • Machine learning
  • Explainable AI
  • Generative AI
  • Reinforcement learning
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