Rohan Sikand (rosikand)

rosikand

Geek Repo

Company:Stanford University

Location:Stanford, CA | NJ

Home Page:https://rosikand.github.io/

Twitter:@rosikand

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Rohan Sikand's repositories

awesome-templates

🖨 Nice templates for documents in LaTeX and more

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pw

Jekyll Minima theme. Previewing design changes for PW.

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Mignot-Lab-Research

💤 Code regarding my research in Dr Mignot's lab at Stanford School of Medicine

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algo-trading

📈 Some things regarding algorithmic trading.

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covid-cough-test

😷 This is an experiment regarding the classification of cough audio samples as either COVID-19 positive or negative.

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BIG-bench

Beyond the Imitation Game collaborative benchmark for enormous language models

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musical-instruments-classification

🎵 In this project, I experiment with different machine learning algorithms to classify audio samples of musical instruments.

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ACMLab-Project

🛰 This was a project I worked on with several team members as part of the end of Autumn quarter project for Stanford's ACMLab club.

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waterQualityML

💧 Inland water systems are essential to our environment because they are vital ecosystems that are bio-diverse. Thus, finding innovative ways to monitor water quality is vital. In this repository, I present various machine learning algorithms that takes in multispectral remote sensing data from the AquaSat data set as input to predict optically active water quality parameters as desired output.

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Effuse

🫁 Pleural effusion is a common lung disorder which can be dissected into two main categories: transudates and exudates. Initially, pleural effusion is diagnosed using X-rays or CT scans. Once effusion can be spotted on one of these scans, a pleural fluid tap-an invasive procedure-is coducted to identify the nature of the effusion--either transudate or exudate. Before this procedure, and ultrasound is used to mark where the fluid is located. These sonograms can be used to identify the nature of pleural effusion without the invasive procedure. However, it is hard for a radiologist-human-to differentiate between the two accurately. Thus, a effuse was made: a machine learning algorithm for differential diagnosis in transudate and exudate pleural effusion.

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