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Medical Dicom
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VideoI am a data-driven researcher with hands-on experience in data labeling, annotation, and AI training data pipelines, especially in the context of large-scale scientific and astronomical datasets. My work spans developing and applying machine learning algorithms—including CNNs and clustering methods—for image and spectroscopic data classification, anomaly detection, and regression tasks. I have created and curated balanced datasets of over 150,000 galaxy images for morphological classification, annotated spectroscopic anomalies in SDSS data, and handled multi-modal data from telescopes and surveys. My technical toolkit includes Python (Numpy, Pandas, Scikit-learn, TensorFlow, OpenCV), PyTorch, and data visualization tools, along with experience in cleaning, organizing, and validating complex datasets. I am meticulous and detail-oriented, with a proven ability to optimize annotation processes and ensure high data quality for AI model development, with strong skills in scientific writing and collaborative research.
We were required to analyse images from the polar expedition, mark the penguins in the images, and classify their behaviour
We were supposed to classify galaxy images if they were mergers or stagnant individual galaxies
BSMS Dual Integrated Degree, Astrophysics
Bachelor of Science and Master of Science, Physics and Data Science
Project Associate
Head, Astronomy Research Group
