Hey there! I'm

Samir Bhattarai

Computer Engineering and Mathematics

I'm a computer engineering student at the University of Southern Mississippi with a passion for crafting amazing software and hardware experiences. I am currently studying computer engineering with a mathematics minor to deepen my understanding of how to build sleek and efficient computer software and machines to make our daily experiences better.

While I'm not busy coding you'll often find me soaking up inspiration from the world of technology. Whether it's exploring new robotics innovations, machine learning models from hugging face, or just learning to play guitar a bit better, I love bringing creativity into work.

S. Bhattarai

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Featured Projects

Explore my key projects showcasing innovative solutions across robotics, web development, data science, and IoT technologies.

Smart Home SCADA System (2025)

Developed a SCADA-based smart home system by integrating Ignition 8.1, and for seamless data acquisition from ESP32 connected to various sensors enabling centralized control and real-time home monitoring.

Created a web-based dashboard using Ignition Perspective Designer to provide an intuitive interface for real-time data visualization and remote device management. The system utilizes TCP/IP and IoTprotocols for robust communication between sensors and the central monitoring platform.

IEEE Southeast Con(2025)

Built an autonomous robot using Raspberry Pi, Arduino, Ardu Cam for the Hardware Competition at IEEE SoutheastCon 2025. The robot is capable of detecting game pieces and avoiding obstacles in real-time by dynamically adjusting its path.

SoutheastCon is the annual IEEE Region 3 Technical, Professional, and Student Conference. SoutheastCon brings together Computer Scientists, Electrical, and Computer Engineering professionals, faculty and students to share the latest information through technical sessions, tutorials, and exhibits.

Bagh-Chal: Digital Strategy Game Platform (2025)

Bagh-Chal is a full-stack web application that digitizes the ancient Nepali strategy board game "Bagh-Chal" (बाघचाल). This traditional game pits four tigers against twenty goats in an asymmetric strategy battle where tigers hunt goats while goats attempt to trap all tigers.

Built with Next.js 14, TypeScript, and Socket.IO for real-time multiplayer functionality. Features include AI opponents with configurable difficulty levels using minimax algorithm, online matchmaking, room creation, and comprehensive user management with authentication and statistics tracking.

The project successfully bridges traditional gaming culture with modern web technology, implementing complex game logic with two-phase gameplay, asymmetric strategy mechanics, and sophisticated AI development using minimax with alpha-beta pruning.

Next.jsTypeScriptSocket.IOMongoDBAI/Minimax

Infolaya(2025)

Zero Code Data Analysis and Visualization

Infolaya is an innovative web application designed to simplify data analysis and visualization for users without programming expertise. The platform allows users to upload datasets in various formats, including CSV, Excel, Text, and JSON, and provides a user-friendly interface for data exploration and visualization. Users can easily create interactive charts, graphs, and dashboards to gain insights from their data without writing a single line of code.

The application supports a wide range of data visualization options, including bar charts, line graphs, scatter plots, and heatmaps. Users can customize their visualizations with various filters and parameters to focus on specific aspects of their data. Infolaya aims to empower users to make data-driven decisions by providing an intuitive platform for data analysis and visualization.

Vision Bot (2024)

Vision Bot is an autonomous robot utilizing the ESP32 camera module and the YOLOv9 object detection model. The robot is capable of detecting and avoiding obstacles in real-time by dynamically adjusting its path.

The autonomous object detection and avoidance robot integrates an ESP32 camera module to capture real-time video streams, enabling it to detect obstacles in its environment. By utilizing the YOLOv9 object detection model, the robot can accurately identify objects within its surroundings, which facilitates precise path planning. Sophisticated algorithms are being implemented for dynamic obstacle avoidance, allowing the robot to navigate autonomously in complex, changing environments.