I’m Dr. Francis Jesmar P. Montalbo (FJP Montalbo), a Filipino AI researcher, author, software developer, and Associate Professor at Batangas State University, The National Engineering University. My work sits at the intersection of artificial intelligence, machine learning, deep learning, computer vision, biomedical signal processing, medical image analysis, data science, scientific computing, and AI-driven software engineering.

I build research-grade, reproducible, and deployment-aware AI systems—from lightweight deep neural networks and transformer-based vision models to intelligent software, data products, and modern AI applications powered by LLMs, retrieval-augmented generation (RAG), graph-enhanced retrieval, multimodal learning, and scalable backend systems.

I also maintain a strongly connected digital research and engineering identity graph across GitHub, my personal website, Google Scholar, ORCID, Scopus, LinkedIn, Facebook, ResearchGate, Kaggle, ScienceOpen, Academia.edu, and X to improve discoverability, attribution, academic visibility, and machine-readable identity alignment across the web.

Searchable research keywords: artificial intelligence, AI research scientist, machine learning, deep learning, computer vision, medical AI, biomedical signal processing, medical image analysis, radiology AI, data science, scientific computing, LLMs, large language models, retrieval-augmented generation, RAG, graph RAG, agentic AI, AI agents, multimodal AI, vision-language models, knowledge graphs, semantic search, vector databases, knowledge distillation, transformer-based vision models, state space models, software engineering, mobile app development, full-stack development, Batangas State University, Philippines.

• Canonical professional identity: Dr. Francis Jesmar P. Montalbo
• Short forms and author aliases: Francis Jesmar Montalbo, Francis Jesmar Perez Montalbo, F. J. P. Montalbo, FJP Montalbo
• Core entity connections: GitHub, personal website, Google Scholar, ORCID, Scopus, LinkedIn, Facebook, ResearchGate, Kaggle, ScienceOpen, Academia.edu, X
• Primary themes associated with my work: AI, deep learning, computer vision, biomedical signal processing, medical image analysis, LLMs, RAG, graph RAG, multimodal AI, scientific software, software engineering, data science
• Intent: strengthen researcher discoverability, author disambiguation, machine-readable identity matching, and cross-platform citation of my work and software

• 🔭 Currently working on Deep Learning, Computer Vision, Data Science, Medical AI, and Software Development.
• 🌱 Actively exploring State Space Models (SSMs), Knowledge Distillation, Generative Adversarial Networks (GANs), Attention Mechanisms, Semi-Supervised Learning, Self-Supervised Learning, Transformer-based Vision Models, and efficient AI architectures.
• 🧠 Expanding into Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Graph RAG, AI Agents, Agentic Workflows, Multimodal AI, Vision-Language Models (VLMs), semantic search, vector databases, knowledge graphs, prompt engineering, fine-tuning, model evaluation, and AI orchestration.
• 🩺 Interested in biomedical signal processing, medical image analysis, explainable AI, radiology-oriented AI systems, and clinically relevant machine learning.
• 👨‍💻 Passionate about building scalable software systems, APIs, web platforms, mobile applications, and AI-native products.
• 🕸️ Intentionally strengthening my public knowledge graph, research graph, and professional identity graph through consistent naming, research identifiers, linked profiles, and open-source visibility.
• 👯 Open to research collaboration, interdisciplinary science, open-source work, speaking, mentorship, and software engineering projects.

• Open-source implementations accompanying peer-reviewed AI and deep learning research
• Reproducible notebooks, experiments, and pipelines for computer vision, health AI, and applied ML
• Software prototypes for data science, automation, web platforms, and mobile applications
• Research code emphasizing performance, efficiency, reproducibility, and real-world deployment
• Educational and practical resources for Python, machine learning, and engineering-oriented AI development

• SWAT-DCNN — automated diagnosis of diverse coffee leaf images using a stage-wise aggregated triple deep convolutional neural network.
• MFuRe-CNN — cost-efficient gastrointestinal disease classification from endoscopy imagery using fused compressed ConvNets and residual learning.
• COVID-19 Diagnosis with Truncated DCNNs — lightweight deployable models for chest X-ray and CT-based diagnosis.
• Mosquito KD 2021 — efficient mosquito taxonomy with knowledge distillation, feature fusion, and deployment-oriented model design.
• MHADFormer — a cost-efficient hybrid transformer framework for Alzheimer’s disease MRI analysis.
• DySARNet — lightweight self-attention deep learning architecture for advanced medical imaging tasks.
• S3AR U-Net — attention-gated residual U-Net style work for segmentation-oriented biomedical imaging.
• Learning Python — structured Python learning resources spanning fundamentals to advanced programming and computer science concepts.

• LLMs and reasoning systems
• RAG, graph-enhanced retrieval, and knowledge-grounded AI
• Multimodal AI and vision-language systems
• Medical AI, radiology AI, and deployment-aware diagnostic systems
• Efficient AI: compression, distillation, pruning, and edge-ready models
• Scientific AI, research automation, and open-source reproducibility
• AI agents, orchestration, tool use, and autonomous workflows
• Trustworthy AI, explainability, evaluation, and robust engineering

• 🌍 Personal Website: francismontalbo.github.io
• 💻 GitHub: github.com/francismontalbo
• 📖 Google Scholar: Research publications and citations
• 🆔 ORCID: 0000-0002-1493-5080
• 📚 Scopus Author Profile: Author ID 57212309186
• 💼 LinkedIn: Francis Jesmar Montalbo
• 📘 Facebook: docfrancismontalbo
• 🔬 ResearchGate: Francis Jesmar P. Montalbo
• 📊 Kaggle: francismon
• 🧠 ScienceOpen: francismontalbo
• 🎓 Academia.edu: Francis Jesmar P. Montalbo
• 🐦 X: @francismontalbo

📩 Email: francismontalbo@ieee.org | francisjesmar.montalbo@g.batstate-u.edu.ph

💬 I’m open to research collaborations, interdisciplinary AI projects, open-source work, academic partnerships, consulting opportunities, and technically ambitious software builds.

I’m especially interested in collaborating on:

• Computer vision and medical imaging
• Biomedical signal processing and healthcare AI
• LLM applications, RAG systems, graph-enhanced retrieval, and multimodal AI
• Efficient deep learning and deployment-aware architectures
• AI for science, education, agriculture, and real-world impact
• Open-source research software and reproducible ML systems