About Me
I am Ravindu Tharuka Weerasinghe, an Artificial Intelligence undergraduate at the University of Moratuwa, with a strong focus on building practical and efficient AI systems.
My work spans small language models, MLOps, and real-world system design. I am particularly interested in how AI systems move beyond isolated models into real-world environments, especially in robotics and edge systems.
Experience
WSO2 - Research & AI Intern
- Developed and fine-tuned small language models using techniques such as LoRA, adapters, and prefix tuning
- Evaluated models using RAGAS metrics, improving performance of a local model (~60% - ~65%)
- Built automated MLOps pipelines for training, tracking, and deployment using MLflow, RunPod, and HuggingFace
- Focused on privacy-sensitive and resource-constrained AI applications
Education
University of Moratuwa
B.Sc. (Hons) in Artificial Intelligence
CGPA: 3.86 / 4.00
Dean's List: 4/6 semesters
* Strong foundation in Neural Networks, Machine Learning, NLP, and Software Engineering.
BCS, The Chartered Institute for IT
BCS Level 6 Professional Graduate Diploma in IT
* Gained knowledge in Advanced Database Management Systems, Big Data Management, Programming Paradigms, and Network Information Systems.
Research
Type II Diabetes Risk Prediction using Multifactor Machine Learning
IEEE Conference Publication (2026)
- Developed a LightGBM-based prediction system using ~100,000 NHANES records integrating laboratory and lifestyle features
- Designed a multi-stage feature selection pipeline reducing ~5000 features to 43 clinically relevant predictors
- Achieved 95.27% ROC-AUC with 88.48% sensitivity and 87.19% specificity on real-world test distribution
- Built a practical system capable of handling missing data without imputation, enabling real-world deployment
Small Language Models for Privacy-Critical Applications
Independent Review (2025)
- Explored compression techniques such as quantization, distillation, and pruning
- Analyzed privacy-preserving techniques (DP, SMPC, FL, HE) for SLMs
- Investigated future trends including agentic and multi-agent SLM systems
Interests
- Efficient AI systems (small language models, optimization)
- Robotics and embodied AI
- Neuro-symbolic systems and reasoning
- Machine Perception
- Real-world deployment of AI systems
Personal Note
I enjoy building systems that go beyond theory and work in real-world settings. One of my most impactful experiences was developing a fully autonomous chess-playing machine, which sparked my interest in robotics and hardware-integrated AI systems.
Outside of work, I enjoy badminton, playing guitar, and reading mystery novels.
Feel free to explore my projects or reach out if you'd like to connect.