# Manith Marapperuma

> Manith Marapperuma is an AI researcher and machine learning engineer based in Colombo, Sri Lanka, and Lead Research Engineer at Neuralgap. He works on molecular dynamics simulation and engineering of viral capsids, agentic explainability, and quantum research on molecules, building on a background in computer vision, knowledge graphs and ontology-driven reasoning. His research thread is explainability: AI systems whose reasoning can be inspected rather than taken on trust.

Last updated: 2026-08-22
Canonical: https://manithj.github.io/

## Profile
- Name: Manith Marapperuma
- Role: Lead Research Engineer at Neuralgap
- Location: Colombo, Sri Lanka
- Email: manithjm@gmail.com
- Website: https://manithj.github.io
- ORCID: https://orcid.org/0009-0006-7142-5884
- Education: BSc (Hons) Computer Engineering, General Sir John Kotelawala Defence University, Sri Lanka (2022-2025). Dean's List semesters 7 and 8; final year research project graded A.

## Research interests
- Computer vision
- Knowledge graphs and ontology-driven reasoning
- Agentic and multi-agent AI systems
- Computational biochemistry
- Quantum computing

## Current work
- Neuralgap, Lead Research Engineer (Aug 2026 - present), Delaware, USA (remote)
  - Molecular dynamics simulation and engineering of viral capsids for gene therapy delivery.
  - Research and development on agentic explainability, making the reasoning inside multi-step pipelines inspectable rather than opaque.
  - Quantum research on molecules, applying quantum methods to molecular simulation and electronic structure.

## Previous work
- Neuralgap, Foundation AI Researcher (Nov 2025 - Aug 2026): foundation model research on protein-ligand binding affinity and pose confidence estimation; multi-engine docking pipelines integrating AutoDock Vina, DiffDock and Boltz-2; agentic architectures with memory, planning and tool-use orchestration.
- Augustory Corp., Machine Learning Engineer (May 2024 - Nov 2025), Arizona, USA (remote): GraphRAG enterprise retrieval pipeline on Neo4j; data-ingestion pipelines with 36% faster processing and 16% better retrieval accuracy; real-time conversational agent using Whisper and WebRTC.
- Freelance developer, data science and machine learning (2023 - 2025).

## Verified links
- [GitHub](https://github.com/Manithj)
- [LinkedIn](https://www.linkedin.com/in/manithj/)
- [Google Scholar](https://scholar.google.com/citations?user=Sa3m8noAAAAJ)
- [ORCID](https://orcid.org/0009-0006-7142-5884)
- [Hugging Face](https://huggingface.co/AI-Manith)
- [Medium](https://manithj.medium.com/)

## Publications
- [AI-Assisted Evaluation of Martial Arts Techniques Using Body Pose and Visual Features](https://ieeexplore.ieee.org/document/11453795) - Marapperuma, M., Maduranga, M.W.P., Damsuvi, J.K.A.T. ICARC 2026, 6th International Conference on Advanced Research in Computing.
- [Beyond Black Boxes: An Ontology-Based Vision Framework for Transparent Fruit Disease Detection](https://ieeexplore.ieee.org/document/11318424) - Marapperuma, M., Vidanage, K. SLAAI 2025, International Conference on Artificial Intelligence. Best Paper Award, AI Ontology & Knowledge Engineering track.
- [Computer Vision for Object Detection in Assistive Technologies: A Comparative Review](https://ir.kdu.ac.lk/handle/345/8254) - Marapperuma, M., Vidanage, K. Faculty of Computing, General Sir John Kotelawala Defence University.

## Research projects
- Ontology-Based Vision Framework for Fruit Disease Detection (OWL, Pellet Reasoner, OpenCV, Python): hybrid framework pairing a semantic knowledge base with a real-time vision pipeline; a phytopathological ontology in OWL codifying disease symptoms, HSV spectral phenotypes and morphology; 85.0% F1 on MangoFruitDDS, competitive with VGG16-class models while remaining interpretable.
- [OntoGraph, drug interaction analysis](https://huggingface.co/spaces/CSAT/OntoGraph) (LangGraph, semantic web ontologies, open-source LLMs): graph-based biomedical reasoning system identifying and explaining potential drug-drug interactions.
- [SkinDet, skin oiliness detection](https://huggingface.co/spaces/AI-Manith/SkinDet-vit) (Vision Transformers, PyTorch, Streamlit): fine-tuned ViT reaching 92% test accuracy for oiliness classification from facial images.
- [Gemma-2B fine-tuned for English-Sinhala](https://huggingface.co/AI-Manith/manith-gemma-sinhala-gpt) (Hugging Face, PyTorch, LoRA/QLoRA, PEFT): instruction-tuned translation model trained within a single-GPU memory budget.

## Writing
- [Demystifying Project AlphaFold - Where AI Meets Biology](https://medium.com/@manithj/demystifying-project-alphafold-where-artificial-intelligence-meets-biology-e7f13ba1e655) (Medium, 9 May 2024): what bioinformatics is, how AlphaFold gets from an amino acid sequence to a folded structure, and what changed in AlphaFold 3.

## Awards
- Best Paper Award, AI Ontology & Knowledge Engineering track, SLAAI International Conference on Artificial Intelligence (2025)
- Best Technology in AI Award, Data Odyssey (2024)
- Shubhra Kar Linux Foundation Training (LiFT) Scholarship
- IEEEXtreme 17.0 Global Programming Competition, Island Rank 10
- IEEEXtreme 16.0 Global Programming Competition, Island Rank 40
- Finalist, Genesiz 2024 Inter-University Robotics Competition
- Finalist, Cypher 2.0 Capture the Flag Hackathon
- Runner-up, BleedCode 2.0 Programming Competition
- President Scout Award, World Organization of the Scout Movement

## Invited talks
- Guest speaker, IEEEXtreme 19.0 Workshop: "Utilizing Generative AI to Accelerate Problem Solving."
- Guest speaker, IEEEXtreme 18.0 Awareness Session: team strategies behind a top-10 national ranking.

## Technical skills
- Agentic AI: LangGraph, LangChain, MCP, multi-agent orchestration, LLMs
- Retrieval: RAG, GraphRAG, Neo4j, FAISS, Pinecone, Qdrant
- Machine learning: PyTorch, TensorFlow, OpenCV, Vision Transformers, LoRA/QLoRA/PEFT
- Computational biochemistry: AutoDock Vina, DiffDock, Boltz-2, GNINA, DynamicBind, molecular dynamics
- Languages: Python, SQL, C/C++ (CUDA)
- Infrastructure: Docker, Kubernetes, AWS, MLflow, FastAPI, Linux

## Q&A
Q: Who is Manith Marapperuma?
A: Manith Marapperuma is an AI researcher and machine learning engineer based in Colombo, Sri Lanka. He is Lead Research Engineer at Neuralgap and holds a BSc (Hons) in Computer Engineering from General Sir John Kotelawala Defence University.

Q: What does Manith Marapperuma work on?
A: Molecular dynamics simulation and engineering of viral capsids for gene therapy, agentic explainability in multi-step AI pipelines, and quantum research on molecules. His wider research covers computer vision, knowledge graphs and ontology-driven reasoning, agentic and multi-agent systems, computational biochemistry and quantum computing.

Q: What is Manith Marapperuma's research contribution to explainable AI?
A: His SLAAI 2025 best paper, "Beyond Black Boxes", introduced an ontology-based vision framework for transparent fruit disease detection. It pairs an OWL phytopathological ontology with a real-time vision pipeline and reaches 85.0% F1 on MangoFruitDDS, competitive with VGG16-class deep models while remaining fully interpretable.

Q: Has Manith Marapperuma won any awards?
A: Yes. He received the Best Paper Award in the AI Ontology & Knowledge Engineering track at the SLAAI International Conference on Artificial Intelligence (2025), the Best Technology in AI Award at Data Odyssey 2024, and a Shubhra Kar Linux Foundation Training (LiFT) Scholarship. He ranked 10th nationally in IEEEXtreme 17.0.

Q: What is Manith Marapperuma's background in computational drug discovery?
A: At Neuralgap he has worked on protein-ligand binding affinity and pose confidence estimation, built multi-engine docking pipelines integrating AutoDock Vina, DiffDock and Boltz-2, and developed molecular dynamics pipelines for protein-ligand complexes and viral capsids.

Q: How can I contact Manith Marapperuma?
A: By email at manithjm@gmail.com, through LinkedIn at https://www.linkedin.com/in/manithj/, or via the contact section of https://manithj.github.io.

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