About

Pranjal Sahu is a Ph.D. in Computer Science with a specialization in Computer Vision and Medical Imaging. He has extensive experience in Deep Learning applications in the Medical Imaging domain, with his Ph.D. thesis focusing on utilizing synthetic data in training Deep Models. Pranjal has worked on several challenging problems such as volume segmentation, volume classification, and image/projection denoising, using his expertise in Computer Vision, Machine Learning, and Medical Imaging. Prior to his Ph.D., Pranjal worked as a software developer in companies like Oyo Rooms and HT Media. He has a strong technical background in PyTorch, C++, Tensorflow, Keras, Python, OpenCV, Matlab, Scipy, Numpy, and Numba. Pranjal's homepage and Github profile showcase his prior experience and publications. Currently, Pranjal is working as a Research Assistant at Stony Brook University, where he is involved in Deep Learning in Medical Imaging. He is working on projects such as Scatter correction in Breast Tomosynthesis using Deep Learning, Digital Breast Tomosynthesis projection denoising using Deep learning, and 3D Lung Nodule Classification in CT Using Multisection CNN. Pranjal's past experience includes working as an Image Analytics Intern at Siemens, where he worked on improving Siemens Lung CAD and Pathological Lung CT segmentation. He improved the recall of Tumor voxels in lung segmentation mask from 0.56 to 0.87 without requiring a full annotated segmentation mask for lung volume. His work was published in the Journal of Biomedical and Health Informatics (JBHI). Pranjal has a Doctor of Philosophy (Ph.D.) degree from Stony Brook University and a Bachelor of Technology (B.Tech.) degree in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur. He has a diverse skill set that includes Computer Vision, Keras, OpenCV, Deep Learning, Python, ML, Tensorflow, Pytorch, Software Engineer, and infra. With 4.70 years of relevant experience, Pranjal Sahu is a skilled and accomplished researcher in the field of Computer Vision and Medical Imaging.