<feed xmlns="http://www.w3.org/2005/Atom"> <id>https://v3rac1ty.github.io/</id><title>Rishi Vemulapalli</title><subtitle>This website is a collection of my personal projects.</subtitle> <updated>2026-01-07T15:54:42-06:00</updated> <author> <name>Rishi Vemulapalli</name> <uri>https://v3rac1ty.github.io/</uri> </author><link rel="self" type="application/atom+xml" href="https://v3rac1ty.github.io/feed.xml"/><link rel="alternate" type="text/html" hreflang="en" href="https://v3rac1ty.github.io/"/> <generator uri="https://jekyllrb.com/" version="4.4.1">Jekyll</generator> <rights> © 2026 Rishi Vemulapalli </rights> <icon>/assets/img/favicons/favicon.ico</icon> <logo>/assets/img/favicons/favicon-96x96.png</logo> <entry><title>Formula 1 ML Analysis</title><link href="https://v3rac1ty.github.io/posts/F1_ML_Analysis/" rel="alternate" type="text/html" title="Formula 1 ML Analysis" /><published>2025-12-08T00:00:00-06:00</published> <updated>2026-01-07T15:54:23-06:00</updated> <id>https://v3rac1ty.github.io/posts/F1_ML_Analysis/</id> <content type="text/html" src="https://v3rac1ty.github.io/posts/F1_ML_Analysis/" /> <author> <name>Rishi Vemulapalli</name> </author> <category term="Applications" /> <summary>Overview This project analyzes the Formula 1 Pit Stop Dataset from Kaggle to understand how pit stop strategy influences performance across a full Formula 1 season. The dataset provides detailed race strategy information including driver and team data, race details, and pit stop metrics. The analysis combines exploratory data analysis, statistical testing, and predictive modeling to quantify t...</summary> </entry> <entry><title>Kalman Filter</title><link href="https://v3rac1ty.github.io/posts/Kalman_Filter/" rel="alternate" type="text/html" title="Kalman Filter" /><published>2025-02-14T00:00:00-06:00</published> <updated>2025-03-17T20:30:29-05:00</updated> <id>https://v3rac1ty.github.io/posts/Kalman_Filter/</id> <content type="text/html" src="https://v3rac1ty.github.io/posts/Kalman_Filter/" /> <author> <name>Rishi Vemulapalli</name> </author> <category term="Robotics" /> <summary>A Kalman filter is an optimal estimation algorithm used to estimate the state of a system when the state cannot be measured directly but can be inferred from indirect and noisy measurements. This document explains the theory behind Kalman filtering and details our implementation for motion tracking. Theory of Kalman Filtering Basic Concept Kalman filtering works in two steps: Prediction: ...</summary> </entry> <entry><title>Stock Market Tracker</title><link href="https://v3rac1ty.github.io/posts/Stock_Market_App/" rel="alternate" type="text/html" title="Stock Market Tracker" /><published>2024-12-24T00:00:00-06:00</published> <updated>2025-01-06T14:18:26-06:00</updated> <id>https://v3rac1ty.github.io/posts/Stock_Market_App/</id> <content type="text/html" src="https://v3rac1ty.github.io/posts/Stock_Market_App/" /> <author> <name>Rishi Vemulapalli</name> </author> <category term="Applications" /> <summary>Investing in the stock market can be very rewarding and challenging. Effective tracking and analysis are important for making informed decisions. To help with this process, I am developing a C++ application designed to monitor real-time stock data, visualize performance metrics, and provide insightful data analytics through comprehensive graphs. This project shows my expertise in web scraping, ...</summary> </entry> <entry><title>Pure Pursuit</title><link href="https://v3rac1ty.github.io/posts/Pure_Pursuit/" rel="alternate" type="text/html" title="Pure Pursuit" /><published>2024-10-17T00:00:00-05:00</published> <updated>2025-01-06T14:25:17-06:00</updated> <id>https://v3rac1ty.github.io/posts/Pure_Pursuit/</id> <content type="text/html" src="https://v3rac1ty.github.io/posts/Pure_Pursuit/" /> <author> <name>Rishi Vemulapalli</name> </author> <category term="Robotics" /> <summary>The Pure Pursuit algorithm is a popular and efficient path-following algorithm used in autonomous robotics. It enables the robot to follow a desired path by continuously steering toward a lookahead point, which is calculated based on a fixed lookahead distance from the robot’s current position. This allows the robot to follow a smooth curve and navigate through predefined paths, making it ideal...</summary> </entry> <entry><title>Odometry</title><link href="https://v3rac1ty.github.io/posts/Odometry/" rel="alternate" type="text/html" title="Odometry" /><published>2024-10-16T00:00:00-05:00</published> <updated>2024-10-16T00:00:00-05:00</updated> <id>https://v3rac1ty.github.io/posts/Odometry/</id> <content type="text/html" src="https://v3rac1ty.github.io/posts/Odometry/" /> <author> <name>Rishi Vemulapalli</name> </author> <category term="Robotics" /> <summary>Odometry is a method used by robots to track their position and orientation relative to a starting point by using data from sensors such as encoders or tracking wheels. This technique is essential for autonomous navigation, as it allows a robot to estimate its location accurately in real time, without relying on external reference points such as GPS. In Illini VEX Robotics, we implemented an o...</summary> </entry> </feed>
