Anmol Goel

I am a CS undergrad completing my bachelors at Guru Gobind Singh Indraprastha University, Delhi. My interests span across deep learning, NLP and computer vision.

I interned at Defence Research Development Organisation, New Delhi in Summer 2018 - developing a machine learning powered web application for use in the Solid State Physics Laboratory .

I worked as a Data Science Intern at ZoAi from Jan-April 2019 where I built predictive analysis dashboards, developed forecasting algorithms for restaurants and anomaly detection.

I am currently working in the machine learning team at Wellowise where I will be developing algorithms for biomedical data and collaborating with the bioinformatics team.

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Research/Projects

I'm interested in natural language processing, deep learning, generative adversarial networks, optimization, statistics, computational mathematics and image processing. My work is mostly focused on the fusion of deep learning, web technologies and healthcare among other things.

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This Paper Does Not Exist!

These papers have been generated by an AI!, May 2019

Fine-tuned the 345M version of GPT-2 model on 30k papers from arxiv to generate completely new reseach titles and abstracts. The model learned the semantic and contextual meanings of words really well and the generated samples are very human-like.

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Heart Disease Prediction

Predictive analysis for a healthy life, Nov 2018

Developed a PWA which predicts the probability of getting a coronary heart disease in the future using patient's medical data as input.

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Automated spoken voice to cursive handwritten text conversion

Inspired by the movie Her, Feb 2019

A CLI tool which converts spoken language to beautiful handwritten cursive letters.

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Genetic algorithm for textual evolution

Survival of the fittest!, Dec 2018

Developed a web application which takes user input and tries to evolve a random string of text.

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Offline Image Classification

Classify images completely offline, Jan 2019

A PWA which can work completely offline for predicting common objects using native camera.


Inspired by Jon