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👋 Rahul Ranjan

PhD Researcher in AI & Mobile Health | Biomedical Signal Processing | Deep Learning

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📍 Melbourne, Australia | 🎓 Monash University


🔬 About Me

I'm a PhD researcher at Monash University's Department of Electrical & Computer Systems Engineering, specializing in AI-driven mobile health sensing. My research focuses on developing robust, contactless vital sign measurement systems using smartphone cameras.

Research Interests:

  • 📱 Remote Photoplethysmography (rPPG)
  • 🩺 Cuffless Blood Pressure Estimation
  • 🫀 Smartphone-based Vital Sign Measurement (SpO₂, HR, BP)
  • 🧠 Deep Learning for Biomedical Signal Processing
  • 📊 Computer Vision for Healthcare Applications

Current Work:

  • Building end-to-end smartphone video vital-sign pipelines with 95%+ accuracy
  • Achieving MAE < 5 mmHg in contactless blood pressure estimation
  • Developing CNN/Transformer models for robust HR/SpO₂/BP estimation across diverse subjects
  • Processing and analyzing 5000+ video samples across multiple datasets

📚 Education

🎓 Master of Artificial Intelligence (2023 – 2025)
Monash University, Melbourne, Australia

🎓 M.Sc. (Hons.) Physics + B.E. (Hons.) Electronics & Instrumentation (2017 – 2022)
Birla Institute of Technology and Science (BITS), Pilani, India
Thesis: Monte Carlo Simulations of Phase Transitions in Ising Models


📄 Publications

📖 Journal Articles

Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models
Roha, V. S., Ranjan, R., & Yuce, M. R. (2025)
Journal of Medical Systems, 49(1), 97
DOI

🎤 Conference Proceedings

VITAL Net: A Hybrid Framework for SpO₂ and HR Estimation Using Smartphone rPPG Video
Ranjan, R., Roha, V. S., & Yuce, M. R. (2026)
Accepted at IEEE Applied Sensing Conference 2026
Status


🛠️ Technical Skills

🔥 Core Competencies

Python PyTorch TensorFlow OpenCV

📊 ML/CV/Signal Processing

Deep Learning    : CNN, RNN, LSTM, Transformers, Self-Attention
Computer Vision  : Object Detection, Face ROI Extraction, Video Processing
Signal Processing: rPPG, Spectral Filtering, Bandpass Filters, Anomaly Detection
ML Algorithms    : XGBoost, Random Forest, Scikit-learn, Keras

💾 Data & Systems

Languages        : Python, C++, MATLAB, R, Java, SQL, Bash, Swift, Rust, Go
Data Libraries   : NumPy, Pandas, Polars, Matplotlib, Seaborn, Plotly
Databases        : PostgreSQL, MongoDB, SQL
Tools            : Docker, Git, Flask, Streamlit, LaTeX, Cron

🧪 Research Methods

  • Experimental Design & Statistical Analysis
  • Cross-Subject Evaluation & Validation
  • Technical Writing & Reproducibility
  • Dataset Processing & Benchmarking

🏆 Awards & Recognition

🥇 Monash Research Scholarship (2026 – 2029)
Department of Electrical and Computer Systems Engineering

🥈 The Duke of Edinburgh's International Award – Silver (2015)


🚀 Current Projects

Note: Research code is released per publication guidelines. For detailed project information, visit my portfolio website.

🔬 Active Research

  • VITAL Net Framework: Hybrid CNN-Transformer architecture for simultaneous SpO₂ and HR estimation
  • Contactless BP Monitoring: Deep learning models for cuffless blood pressure measurement
  • rPPG Robustness: Improving signal quality across lighting, motion, and demographic variations

🌱 Currently Learning

  • 🔄 MLOps and reproducible research workflows
  • 🎯 Motion-aware modeling and sensor fusion for biosignals
  • 🏗️ Scalable ML infrastructure with Rust

🤝 Let's Connect

I'm always interested in collaborating on:

  • Mobile health and wearable sensing research
  • Biomedical signal processing challenges
  • Real-world ML robustness and generalization
  • Healthcare AI applications

📧 Email: [email protected]
🌐 Website: rahul201722.github.io
💼 LinkedIn: linkedin.com/in/rahul-ranjan-b595891b1


💡 "Building robust AI systems for accessible healthcare"

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