BrAINLabs Inc. is a research laboratory dedicated to exploring the intersection of Artificial Intelligence (AI), Machine Learning (ML), and Neuroscience to develop innovative AI solutions to face the challenges of the 21st century. At the core of our research work, we draw inspiration from nature to develop AI systems that are explainable and efficient.

At BrAINLabs, we strive to explore the intricate relationship between artificial intelligence and the neuroscience of the human brain. By harnessing the latest advancements in technology and research, we aim to:

• 🧠 Develop intelligent systems that interpretable and/or efficient.
• 🔍 Enhance the understanding of neural mechanisms through computational modeling.
• 🌍 Provide insdutry services .

Here are some of our current and past projects:

1. LARGE LANGUAGE MODELS
   Developing deep learning models that simulate brain activity to help researchers understand neural dynamics.

   • Optimized Compression for Transformers
     Designing efficient techniques to reduce the size and computational requirements of transformer-based models while maintaining their accuracy. This work focuses on model pruning, quantization, and other compression methods to enhance scalability and deployment on resource-constrained systems.

   • Security and Privacy of LLM
     Investigating vulnerabilities in large language models and developing robust solutions to mitigate risks, including adversarial attacks, data leakage, and model misuse. This ensures LLMs can operate securely in sensitive applications, such as healthcare and finance.

   • Applications of LLM for Cybersecurity
     Utilizing the advanced reasoning and pattern recognition capabilities of LLMs to detect and mitigate cyber threats. Applications include automated threat intelligence, phishing detection, and generating secure coding recommendations to prevent vulnerabilities.

2. NEUROMORPHIC COMPUTING
   Utilizing machine learning techniques to analyze neuroimaging data for the early detection of neurological disorders.

   • Learning Algorithms for Spiking Neural Networks (SNN)
     Developing algorithms tailored to SNNs, which emulate the biological neuron spiking process. These algorithms focus on event-driven learning paradigms, enabling real-time processing with low energy consumption, suitable for edge AI systems.

   • Applications of SNN
     Exploring the use of SNNs in robotics, prosthetics, and neuromorphic hardware. These applications leverage the brain-inspired efficiency of SNNs to enable adaptive, low-power solutions for real-world problems, including speech recognition and autonomous navigation.

Dr. Mahima Weerasinghe Position: Researcher / Lead – Neuroinformatics University: Sri Lanka Institute of Information Technology , Sri Lanka Research Interests: Computational Neuroscience, Artificial Neural Networks, AI in Health, AI in Agriculture Contact: mahima.w@sliit.lk Website: mahima.w	Dr. Dinuka Sahabandu Position: Researcher / Lead – Efficient AI University: University of Washington , USA Research Interests: Optimization, Reinforcement Learning, Efficient Machine Learning, AI Ethics, Security in Machine Learning Contact: sdinuka@uw.edu Website: dinuka.s	Dr. Dharshana Kasthurirathna Position: Senior Researcher / Mentor University: Sri Lanka Institute of Information Technology , Sri Lanka Research Interests: Evolutionary Game Theory, AI Ethics, Security in Machine Learning, Machine Learning Contact: dharshana.k@sliit.lk Website: dharshana.k
Dr. Kapila Weerasinghe Position: Researcher / Lead – Explainable AI University: Sri Lanka Institute of Information Technology , Sri Lanka Research Interests: Computational Neuroscience, Artificial Neural Networks, AI in Health, AI in Agriculture Contact: kapila.d@sliit.lk Website: kapila.d	Mr. Jeewaka Perera Position: Researcher University: Sri Lanka Institute of Information Technology , Sri Lanka Research Interests: Reinforcement Learning, Bio-Inspired Machine Learning, AI Ethics, Security in Machine Learning, Machine Learning Contact: dinuka.s@sliit.lk Website: jeewaka.p
Ms. Madhumini Gunaratne Position: Graduate Research Assistant University: Sri Lanka Institute of Information Technology , Sri Lanka Research Interests: Computational Neuroscience, Artificial Neural Networks, AI in Health, AI in Agriculture Contact: madhumini.g@sliit.lk Website: madhumini.g	Mr. Asiri Gawesha Position: Graduate Research Assistant / MPhil Student University: Sri Lanka Institute of Information Technology , Sri Lanka Research Interests:Computational Optimization in Machine Learning, Federate Learning, Edge AI, Computer Vision Contact: asiri.g@sliit.lk Website: asiri.g	Mr. Sanka Mohottala Position: Academic Instructor / MPhil Student University: Sri Lanka Institute of Information Technology , Sri Lanka Research Interests: Computer Vision, Graph Neural Networks, Applications of Deep Learning in Scientific Domains, Brain-Inspired Neural Networks Contact: sanka.mo@sliit.lk Website: sanka.mo

1. Mental Stress Recognition on the Fly Using SNNs
   M. Weerasinghe, G. Y. Wang, J. Whalley, M. Crook-Ramsey. Nature Scientific Reports, 2023. doi:10.21203/rs.3.rs-1841009/v1
   Read the article here

2. Ensemble Plasticity and Network Adaptability in SNNs
   M. Weerasinghe, D. Parry, G. Y. Wang, J. Whalley. ArXiv Preprint. Link to paper

3. Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling
   M. Weerasinghe, J. I. Espinosa-Ramos, G. Y. Wang, D. Parry. IEEE Access, 2021. doi:10.1109/ACCESS.2021.3099492
   Read the article here

While we don’t have specific collaborations at the moment, BrAINLabs is constantly exploring new partnerships with top-tier research institutions, universities, and industry leaders. Stay tuned for exciting announcements in the near future!

We have successfully organized the following TinyML-focused workshops:

• TinyML: A Compact Revolution in Engineering AI
  Pre-Conference Workshop – MERCon (Moratuwa Engineering Research Conference), August 2025

• All Roads Lead to TinyML: The Rome of Efficient Machine Learning in Engineering
  Pre-Conference Workshop – SICET (SLIIT International Conference on Engineering and Technology), August 2025

🔗 Find recordings, materials, and workshop details here: tinyml-in-action.github.io

At the moment, there are no scheduled events, but we are actively planning several workshops and seminars related to AI in Neuroscience and Machine Learning in Health. Make sure to check back soon for updates on our next event!

Looking ahead, BrAINLabs is committed to pushing the boundaries of AI and we are excited to pursue:

• AI for Cognitive Enhancement: Developing intelligent systems to assist with learning, memory, and decision-making.
• NeuroAI Integration: Creating seamless integration between brain-computer interfaces and AI models for advanced applications in health.
• AI in Mental Health Diagnostics: Exploring AI applications for the detection and treatment of mental health conditions.
• Provide interpretable and efficient AI solutions to real-world problems.

Q: How can I join BrAINLabs?
A: We regularly accept interns and PhD candidates. Available positions will be updated in future.

Q: Where can I find your published papers?
A: Our publications can be found in the Publications section.

We welcome contributions from researchers, developers, and enthusiasts in AI, ML, and neuroscience. If you're interested in collaborating or contributing to our projects, please refer to our CONTRIBUTING.md file for guidelines.

Feel free to reach out through our social media channels or email: