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Universitas Gadjah Mada Cloud Experience Research Group
Department of Electrical Engineering & Information Technology
Faculty of Engineering Universitas Gadjah Mada
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    • Modern Enterprise and Software Engineering Methodology (MESEM)
    • Technology Enhanced Learning and Optimization (TELO)
    • Cloud Adoption and Cognitive Application (CACA)
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Roadmap

  • 13 February 2020, 13.14
  • By : ridi

Research Agenda

1. Modern Enterprise and Software Engineering Methodology (MESEM)

Core Theme (2026-2029): Using AI to write/test code and optimizing cloud architecture for cost efficiency.

Student Level Year 1 (July 2026 – 2027) Year 2 (July 2027 – 2028) Year 3 (July 2028 – 2029)
Bachelor (BSc) AI-Assisted Testing: Automating standard code reviews and testing pipelines using AI tools. Automated Component Tracking: Building tools to automatically list and track all third-party software parts used in an app. Auto-Fixing Bots: Creating small AI bots that can read basic bug tickets and automatically write patches.
Master (MSc) Cost-Aware Architecture: Designing cloud applications that automatically adjust themselves to minimize server costs. Built-In Security: Creating systems that automatically scan for and block vulnerabilities the moment code is written. AI Dev Teams: Building frameworks where multiple AI bots work together to plan, code, and deploy software.
PhD AI-First Software Rules: Creating the foundational engineering standards for software that is built entirely by machines. Self-Healing Systems: Designing enterprise networks that automatically detect crashes or hacks and fix themselves without going offline. Managing AI Workforces: Developing methods to oversee and orchestrate massive teams of AI “developers” across global systems.

 

2. Cloud Adoption and Cognitive Application (CACA)

Core Theme (2026-2029): Building smarter cloud networks and AI systems that understand human emotions.

Student Level Year 1 (July 2026 – 2027) Year 2 (July 2027 – 2028) Year 3 (July 2028 – 2029)
Bachelor (BSc) Smart Device Processing: Building IoT devices that process data locally instead of sending everything to the cloud. Emotion-Reading Chatbots: Creating chatbots that can tell if a user is happy or frustrated based on their text. Green Cloud Apps: Writing lightweight cloud functions designed specifically to use as little electricity as possible.
Master (MSc) Local AI Training: Designing systems where AI learns directly on the user’s phone or laptop to save network bandwidth and protect privacy. Combined Emotion Sensors: Building tools that analyze video, voice tone, and text all at once to understand user moods accurately. Smart Data Routing: Creating algorithms that automatically move computing power closer to where the data is stored to reduce lag.
PhD Brain-Inspired Cloud: Researching cloud hardware and software architectures that mimic how the human brain processes information. Scattered AI Systems: Designing massive AI programs that can run smoothly in pieces across both local devices and remote servers. Zero-Lag Networks: Developing highly predictive cloud systems that allocate server resources so perfectly that users experience zero delay.

 

3. Technology Enhanced Learning and Optimization (TELO)

Core Theme (2026-2029): Virtual/Augmented Reality (XR) learning, smart AI tutors, and keeping educational servers running smoothly.

Student Level Year 1 (July 2026 – 2027) Year 2 (July 2027 – 2028) Year 3 (July 2028 – 2029)
Bachelor (BSc) AR Learning Apps: Building Augmented Reality apps to help students visualize basic science and math concepts. Smart Quizzes: Creating testing apps that automatically make questions harder or easier depending on how well the user is doing. Focus Tracking: Prototyping systems that use typing speed or eye-tracking to see if a student is paying attention.
Master (MSc) AI Personal Tutors: Designing conversational AI bots that act as personal teachers to guide students through their courses. VR Job Simulators: Building fully immersive Virtual Reality environments for hands-on, high-risk job training (like engineering or medicine). Smooth E-Learning Servers: Creating algorithms to prevent school servers from crashing when hundreds of students load heavy VR or video files.
PhD Emotion and Learning: Studying the exact relationship between a user’s stress/emotion levels and how well they learn from software. Auto-Generated Lesson Plans: Researching AI that completely rewrites and customizes a syllabus on the fly based on what the student struggles with. Global Education Networks: Designing complex systems to efficiently distribute massive amounts of educational computing power across the globe.

Universitas Gadjah Mada

CLOUD EXPERIENCE RESEARCH GROUP

Department of Electrical Engineering & Information Technology

Faculty of Engineering 

Universitas Gadjah Mada

 

Jl. Grafika No.2 Sinduadi, Mlati, Sleman

Daerah Istimewa Yogyakarta 55281, Indonesia

+ 62 123 456 789

cloudex@yeah.com

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