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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sambid Wasti</title><link>https://sambidwasti.com/</link><description>Recent content on Sambid Wasti</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Sat, 25 Jun 2022 18:35:46 +0530</lastBuildDate><atom:link href="https://sambidwasti.com/index.xml" rel="self" type="application/rss+xml"/><item><title>Image Gallery</title><link>https://sambidwasti.com/gallery/gallery/</link><pubDate>Sat, 25 Jun 2022 18:35:46 +0530</pubDate><guid>https://sambidwasti.com/gallery/gallery/</guid><description/></item><item><title>Kaggle Titanic</title><link>https://sambidwasti.com/projects/kag-titanic/</link><pubDate>Sat, 25 Jun 2022 18:35:46 +0530</pubDate><guid>https://sambidwasti.com/projects/kag-titanic/</guid><description>Overview: This is the kaggle competition used to learn machine learning. In this competition, we are given a database with information about the passengers on board. We have a test set that has information about if the passengers survived or not. Then we have a different dataset where we do not know if they survived or not. We have to determine if they survived or not depending on the other information.</description></item><item><title>Principle Component Analysis (PCA)</title><link>https://sambidwasti.com/projects/pca/</link><pubDate>Sat, 25 Jun 2022 18:35:46 +0530</pubDate><guid>https://sambidwasti.com/projects/pca/</guid><description>Overview: I noticed randomm articles that try to explain PCA and use python packages that has PCA to look at data. Most of the time, it is refrenced as dimension reduction but it does not address the beauty behind the mathematics of this process. I have attempted to document a simple example here, which i have also explained in a section in my Phd. dessertation.
Principle Component Analysis (PCA) PCA is an powerful tool that uses eigen vectors and eigen values to find the correlated variables and collapse them to reduce the dimensions.</description></item><item><title/><link>https://sambidwasti.com/exp/main_experience/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sambidwasti.com/exp/main_experience/</guid><description>Research Experience Postdoc Researcher 2020 - present* Catholic University of America, Washington, DC NASA Goddard Space Flight Center, Greenbelt, MD Center for Research and Exploration in Space Science &amp; Technology II , Greenbelt, MD Postdoc researcher at CUA, working at NASA Goddard under CRESST2 cooperative agreement.
I am part of the team to develop ComPair telescope, a prototype of AMEGO. AMEGO is a midex mission concept. Additional information about AMEGO. ComPair (and AMEGO) uses four subsystem to achieve its scientific and instrument goals.</description></item><item><title/><link>https://sambidwasti.com/exp/main_experience_test/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sambidwasti.com/exp/main_experience_test/</guid><description>Research Experience Postdoc Researcher 2020 - present* 1. Catholic University of America, *Washington, DC* 2. NASA Goddard Space Flight Center, *Greenbelt, MD* 3. Center for Research and Exploration in Space Science &amp; Technology II , *Greenbelt, MD* *Postdoc researcher at CUA, working at NASA Goddard under CRESST2 cooperative agreement.* I am part of the team to develop ComPair telescope, a prototype of AMEGO. AMEGO is a midex mission concept. Additional information about [&lt;u&gt;AMEGO&lt;/u&gt;](https://asd.</description></item></channel></rss>