Skills
Machine Learning & DL : TensorFlow, Keras, scikit-learn, PyRadiomics, nnU-Net
Medical Imaging : 3D Slicer, ITK-SNAP, Orthanc PACS, XNAT, DICOM, NIfTI
Programming Language : Python (NumPy, Pandas, SciPy), SQL (PostgreSQL), Bash
Data Pipeline & Deployment: Docker, Git, GitHub Actions
Specialized Methods : Radiomics, Feature Selection (Metaheuristic), 3D CNNs,
Graph Neural Networks, Survival Analysis, Statistical Modeling
About
I am Hasan Shaikh, a Project Assistant and Clinical Data Scientist at QIRAIL (Quantitative Imaging Research and Artificial Intelligence Lab), Department of Radiation Oncology, Christian Medical College (CMC) Vellore, India. I hold an M.Tech in Computer Engineering from Aligarh Muslim University. Before transitioning into medical AI research, I worked as a Research Analyst at STARlab Capital, after which I found my true calling at the intersection of artificial intelligence and clinical oncology. My day-to-day work involves building and maintaining end-to-end clinical AI pipelines, including multi-organ auto-segmentation, radiomic feature extraction using PyRadiomics, and machine learning models for locoregional recurrence prediction in head and neck cancer on a prospectively recruited cohort funded by the Department of Biotechnology and the Wellcome Trust India Alliance.
My research contributions span both the technical and clinical domains. I presented at AIHC 2025 (NIT Calicut) on metaheuristic-driven ML pipelines for radiomics-based recurrence prediction, and I have a Poster Highlight accepted at ESTRO 2026. Beyond publications, I have worked extensively on DICOM pipeline engineering including Orthanc PACS integration, RT-STRUCT manipulation, and continuous PACS monitoring for daily patient processing.
I am currently seeking a PhD position in Europe in areas including medical image segmentation, radiomics, AI-driven radiotherapy, precision and personalized oncology, radiation-induced toxicity and inflammation prediction, and explainable AI in clinical workflows. I bring a rare combination of clinical deployment experience and research depth, having worked in a high-volume oncology centre where my models and pipelines are actively used in real patient care.
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