Himindu.

Hello 👋

Himindu Kularathne

Software engineering undergraduate and full-stack developer building practical systems across AI, mobile, web, and cloud. I like turning academic thinking into usable products with clean execution.

Current focus

Software Engineering Undergraduate

Full Stack Developer | Research-minded Builder

AI

Applied systems

Full-stack

Product delivery

Mobile

iOS and cross-platform

Research

Academic mindset

Selected work

Built across product, AI, mobile, and systems.

A tighter view of the work that best represents my engineering range, with the rest kept compact for easier scanning.

Academic profile

Publications & Research.

I am building an academic profile around software engineering, intelligent systems, mobile computing, and human-centered web platforms. This section collects publications, research work, technical writing, and university-driven projects as they mature.

Research Interests

AI, SE, DS

Applied AI systems, scalable software architecture, mobile computing, and user-centered product engineering.

Current Focus

CSE

Undergraduate work at the Department of Computer Science and Engineering, University of Moratuwa.

Academic Output

Papers & Reports

A dedicated place for conference papers, preprints, course research reports, posters, and technical articles.

Final Year Project Showcase · 2026

Generating Distributed Code from Single Node Code

Presented

Mineth Weerasinghe, Himindu Kularathne, Methmini Madhushika, Dhanuka Lakshan

A research poster presenting a graph-based framework for monolith-to-microservices decomposition using heterogeneous program graphs, edge-aware graph autoencoders, triplet loss, and clustering-based service boundary identification.

Software ArchitectureMicroservicesGraph Neural NetworksResearch Poster
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In preparation

Himindu Kularathne, Mineth Weerasignhe, Danuka Lakshan, Methmini Madhushika, Dr. Nisansa de Silva, Dr. Srinath Perera

This paper proposes a novel hybrid approach for decomposing monolithic applications into microservices. The method combines static code analysis with dynamic runtime profiling to identify optimal service boundaries, aiming to improve scalability and maintainability while minimizing disruption to existing systems.

Artificial IntelligenceSoftware EngineeringDistributed SystemsMicroservices
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Mineth Weerasignhe, Himindu Kularathne, Danuka Lakshan, Methmini Madhushika, Dr. Nisansa de Silva, Dr. Srinath Perera

This paper presents a comparative evaluation of existing frameworks for decomposing monolithic applications into microservices. Using the PointMaster POS system as a case study, we analyze the effectiveness of various decomposition strategies in terms of scalability, maintainability, and performance. The findings provide insights into best practices for transitioning to microservices architecture.

Distributed SystemsBackend EngineeringCloud ComputingSoftware ArchitectureMicroservices
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Team contribution

What I bring to a team.

Pick a working mode to see how I contribute across product, engineering, research, and delivery.

Active mode

Ship

I move from unclear requirements to working software by breaking problems into small, testable decisions and keeping the product experience visible.

Full-stack

Delivery

Web + mobile

Platforms

Practical

Mindset

Builds complete flows across frontend, backend, data, and deployment concerns.
Keeps scope realistic so teams can make visible progress without losing quality.
Himindu Kularathne

Based in

Panadura, Sri Lanka

About

An engineering generalist with academic depth.

Adaptable Software Engineering undergraduate at the University of Moratuwa with a strong interest in research-informed product development. I enjoy connecting rigorous technical thinking with practical full-stack engineering, especially in AI-enabled systems, mobile applications, and scalable web platforms.

Product-minded engineering

I think beyond implementation details and care about workflows, clarity, maintainability, and user value.

Academic discipline

I bring a research mindset to software work: framing problems, comparing approaches, and communicating tradeoffs.

Cross-platform execution

Experience spans web, backend, iOS, AI-enabled products, databases, cloud tooling, and deployment workflows.

Fast learner

Comfortable moving across unfamiliar tools and domains while keeping output practical and grounded.