🎓 M.Tech in Computer Engineering | Aligarh Muslim University (CGPA: 8.80/10)
🏥 Clinical Data Scientist @ QIRAIL Lab, CMC Vellore
🧠 Radiomics • Auto-Segmentation • Adaptive Radiotherapy • Cancer AI • Uncertainty Quantification

I am a clinical data scientist and medical imaging researcher working at the Quantitative Imaging Research and Artificial Intelligence Lab (QIRAIL), Department of Radiation Oncology, Christian Medical College, Vellore — one of India's leading cancer centers.

My work focuses on building AI pipelines that help oncologists make smarter, faster, and more personalized treatment decisions for head and neck cancer patients.

My research primarily focuses on:

• Radiomics-Based Cancer Outcome Prediction
• Automated Tumor Segmentation (3D CNNs, nnU-Net)
• Adaptive Radiotherapy & Treatment Planning Automation
• Uncertainty Quantification in Clinical AI
• DICOM/PACS Engineering & Clinical Data Pipelines
• Multimodal Deep Learning for Oncology

I have authored and co-authored multiple papers, including a Poster Highlight accepted at ESTRO 2026 (European Society for Radiotherapy and Oncology), a Springer LNEE conference paper, a medRxiv preprint, and an Elsevier book chapter. I also organize hands-on radiomics and auto-segmentation workshops for clinicians and researchers.

I genuinely enjoy talking with people — researchers, clinicians, or anyone curious about AI in healthcare. Feel free to reach out anytime.

🎯 I am actively looking for PhD positions in medical imaging, radiation oncology AI, uncertainty quantification, adaptive radiotherapy, and cancer research.

• 📄 Development and Validation of a Prospective Radiomics-Clinical Signature for Locoregional Recurrence in Locally Advanced Head and Neck Cancer
  Balu Krishna S, Amal Joseph Varghese, Hasan Shaikh et al.
  European Society for Radiotherapy and Oncology (ESTRO 2026), Stockholm, Sweden — ✅ Accepted as Poster Highlight

• 📄 Metaheuristic-Driven Machine Learning Pipelines for Radiomics-Based Prediction of Locoregional Recurrence in Head and Neck Cancer
  Hasan Shaikh, Balu Krishna S, Amal Joseph Varghese et al.
  Lecture Notes in Electrical Engineering, Springer Nature — AIHC 2025, NIT Calicut — ✅ Accepted

• 📄 Automated Segmentation of Head and Neck Cancer from CT Images Using 3D Convolutional Neural Networks
  Piyus Prabhanjans, Asjad Nabeel P, ..., Hasan Shaikh et al.
  medRxiv, 2026

• 📄 Cancer Survival Prediction Using Artificial Intelligence: Current Status and Future Prospects
  Hasan Shaikh and Rashid Ali
  Data Science in the Medical Field, Academic Press, Elsevier, 2024

• 🪧 "Before We Treat, Can We Tell? A Locoregional Recurrence Signature in Head & Neck"
  15th Annual Research Day, CMC Vellore, 2025

• 🪧 "Can CT Radiomics Predict Recurrence in Head and Neck Cancer? Early Results from a Prospective Imaging Trial"
  14th Annual Research Day, CMC Vellore, 2024

• 🛠️ 2nd Workshop on Radiomics and Auto-Segmentation (March 2026)
  Dept. of Radiation Oncology & QIRAIL Lab, CMC Vellore — Hands-on training for clinicians and researchers on radiomics pipelines and auto-segmentation tools.

• 🛠️ 1st Workshop on Radiomics and Auto-Segmentation (November 14–15, 2024)
  Dept. of Radiation Oncology & QIRAIL Lab, CMC Vellore — Certificate

I'm actively applying for PhD positions. If your lab works on any of the following, I'd love to connect:

• 🧠 Medical image analysis & segmentation
• 🎯 Radiomics & cancer outcome prediction
• 📐 Uncertainty quantification in clinical AI
• ⚙️ Adaptive radiotherapy & treatment planning automation
• 🔁 Auto-segmentation for radiation oncology workflows
• 🧬 Multimodal learning for cancer research

Medical Imaging: 3D Slicer · ITK-SNAP · Orthanc PACS · XNAT · DICOM · NIfTI
ML/DL: TensorFlow · Keras · scikit-learn · nnU-Net
Infra: Docker · GitHub · PostgreSQL · Bash

• 💬 I love talking with people — researchers, clinicians, engineers, or anyone curious about AI in healthcare. Just message me.
• 🤖 I believe AI will eventually automate radiation oncology end-to-end — radiation therapy delivery, treatment planning, auto-segmentation, adaptive radiotherapy, and a fully paperless AI-driven EMR. We're close.
• 🌍 The biggest impact of medical AI won't be in well-resourced hospitals — it'll be in under-resourced settings where expert clinicians are scarce.
• 📍 Based in Vellore, Tamil Nadu, India

I'm always open to conversations about research, PhD opportunities, or collaborations.

💡 "Making radiotherapy smarter, one pipeline at a time."