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Prospective students

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IITB UG/DD/MTech students

To seek a project in our research group, you need to be good at linear algebra, probability, differential calculus, machine learning, basic deep learning, python, numpy, and pytorch (or TensorFlow+Keras). MTP and DDP students are expected to make original contributions to tech or the lab to get a good grade. BTP/RnD/SRE students are expected to understand research papers well enough to coach others. If you are ready to work hard, you will get lots of mentorship.

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Potential PhD students

During PhD studies, students (research scholars) are expected to move towards independent exploration by reading others' research, understanding advanced mathematics through self-study, concieving new ideas, trying those ideas, and communicating their findings to the world. They also get paid a stipend and get to live in a well equipped campus. After a PhD, they can join academics, big companies, or a startup. If this sounds like fun, then here is how to apply.

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Interns

THERE ARE NO INTERNSHIPS due to the Covid situation. Please check out mastAI ki paatSHALA instead.
We have very few intern spots compared to the number of applications. Please forgive me if I do not respond to each query. Preference will be given to IITB students due to their familiarity with the system. See the section on the left for IIT Bombay student for the background required. It will be best if you apply after meeting those requirements. Write to medal.iitb.job at gmail, and be specific about what you know. We also expect that you would have a specific interest in one of our projects.

Current PhD Students

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Deepak is working on many things, including deep learning on graphs, semi-supervised learning, weakly supervised learning, and color normalization for medical images.

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Abhijeet is working on detecting various subtypes of cancer and multiple instance learning.

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Nikhil is working on detecting various mutations in cancer using customized deep learning pipelines. 

Current Masters and Dual Degree Students

Anil Pawar

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Weakly supervised lesion localization in histology.

Avineil Jain

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Semi-supervised lesion localization in radiology.

Harsvardhan Tibrewal

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Initialization of neural networks for radiology using unlabeled data.

Nitesh Kaushal

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Compression of histology images and extraction of generic features for histology in an unsupervised manner.

Ravishankar Reddy

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Improved classification of radiology images using data augmentation.

Shrey Gadia

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Graph convolutional neural networks.

Vidyasagar Reddy

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Visualization of CNN features.