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Hi there 👋

I am Soumya, a Masters student at Stanford studying Computer Science. Previously, I completed my Bachelors in Computer Science and Engineering from IIT Bombay. I have spent time at Apple and Google Research working on problems in Natural Language Processing. I am also interested in Machine Learning Systems.

Visit my website to know more.

Soumya Chatterjee's Projects

automated-dispensary icon automated-dispensary

Locates medicines kept on a table and passes required ones to the user. Uses a MobileNet CNN classifier to recognize medicines, an Android app to accept voice commands and a Arduino to control the CNC.

cell-segmentation icon cell-segmentation

Course Project for Medical Image Computing(CS 736), Spring 2019, IIT Bombay. Cell Segmentation using U-Net. PyTorch implementation.

chat_app icon chat_app

Django Channels Tutorial to make an asynchronous chat application (Sockets)

cryodrgn icon cryodrgn

Neural networks for cryo-EM reconstruction

cs251-battleship icon cs251-battleship

Course Project for Software Systems Lab. Battleship Game was implemented using Django and django-channels for the backend and React, HTML, and JS for the frontend.

faq-bot-for-freshmen icon faq-bot-for-freshmen

WnCC Seasons Of Code 2018 project. Designed a chatbot using RasaNLU for answering queries of newly joined students at IIT Bombay

fgsm-keras icon fgsm-keras

Implemention of Fast Gradient Sign Method for generating adversarial examples in Keras

flexflow icon flexflow

A distributed deep learning framework that supports flexible parallelization strategies.

hyperbolic-label-emb-for-hmc icon hyperbolic-label-emb-for-hmc

Code for the paper Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (EACL '21)

infoseek icon infoseek

Code for our AAAI 2021 paper "Model-agnostic Fits for Understanding Information Seeking Patterns in Humans"

instrumental-music-translation icon instrumental-music-translation

Course Project for Automatic Speech Recognition. Implemented music translation from one instrument to another using two approaches, different LSTM encoder-decoder based architectures and the other using a WaveNet autoencoder heavily based on a Facebook AI research paper and codebase

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