Hi there!
I design intelligent robotic systems that combine embedded control, perception, and AI to solve real-world engineering problems. With 3+ years of hands-on experience, 30+ project builds, and a strong foundation in robotics, machine vision, and autonomous systems.
Built, Not Borrowed. Self-taught journey from data science to Robotics
A versatile technology professional specializing in Robotics, data analysis, machine learning, and System Design. My work sits at the intersection of software, hardware, and AI, where I build practical solutions for robot navigation, control, and perception.
Autonomous navigation & system design
Perception & image understanding
PCB, sensors, and real-time control
Decision-making & learning systems
Exploring reliable, adaptive, and intelligent robotic systems for real-world environments.
Perception, control, and data-driven intelligence working together so robots can sense, decide, and act reliably.
My research interest lies in autonomous robotic systems - specifically in how perception, control, and data-driven intelligence combine to let robots operate reliably in unstructured, real-world environments. My background spans embedded systems and PCB design, control theory through PID-based control on an autonomous F1 formula car, sensor fusion for navigation, and applied machine learning - including computer vision pipelines for image understanding and classical ML for predictive modeling.
I am particularly drawn to problems at the intersection of robotics and AI: real-time perception under noisy sensor conditions, path-planning and navigation in dynamic environments, and building data pipelines that let robotic systems learn from and adapt to their surroundings.
Going forward, I want to pursue graduate research in autonomous robotics and intelligent control, with a focus on making robotic systems more adaptive, robust, and capable of operating with minimal human supervision. I am especially interested in labs working on autonomous navigation, robotic perception, human-robot interaction, or embedded AI for real-time robotic control.
A practical toolkit spanning robotics, embedded systems, perception, AI, and control engineering.
Core languages I use to build, automate, and analyze.
Dashboards, reports, and decision-making visuals.
Hardware, control, and embedded technologies for intelligent systems.
Libraries for data preparation, machine learning, and computer vision.
Environment, version control, and shipping tools.
Interfaces, app deployment, and cloud technologies.
My journey through robotics, embedded systems, and intelligent engineering.
Building intelligent automation systems, embedded prototypes, and AI-assisted robotics solutions for practical engineering challenges. Focused on sensor integration, autonomous logic, perception pipelines, and end-to-end development using Python, C++, and embedded tools.
Worked across robotics, embedded design, and analytical problem-solving to create intelligent systems that combine sensing, decision-making, and automation. Developed practical projects that bridge real-time control with data-driven model development.
Pursuing a computer science degree with the cgpa of 3.45/4.0 which emphasis on intelligent systems, machine learning, software engineering, and applied robotics. Active in projects that align with autonomy, perception, and embedded intelligence.
Completed higher secondary education with a specialization in Computer Science, building the foundation for my career in data analysis and software development.
Explore my diverse portfolio of Robotics, data analytics, machine learning, and development projects across multiple technologies.
Developed an automated exploratory data analysis tool to eliminate repetitive manual data exploration tasks. Built using Python, Streamlit, and Pandas with intelligent statistical analysis features. Reduced dataset review time by 80% with fast, consistent insight generation.
Designed an executive dashboard to provide strategic business insights for C-suite decision making. Implemented using Power BI with advanced modeling and summary views. Improved executive decision speed by 40% with real-time performance indicators.
Built an intent-classification chatbot using a self-labeled dataset and a TF-IDF + traditional ML classifier pipeline. Designed a custom campus graph and implemented A* pathfinding for accurate shortest-path routing to key campus locations. Integrated intent recognition with route computation for natural-language navigation queries; built and deployed 3 interface versions (CLI, Flask, Streamlit)
Built a network monitoring tool using Nmap for real-time data collection, stored via a SQLAlchemy-managed database. Designed and implemented a custom Domain-Specific Language (DSL) from scratch including a lexer and parser for querying stored network data
Analyzed cardiovascular health data to identify risk factors and develop predictive models for early intervention. Utilized Python and Pandas for data manipulation and statistical analysis. Presented findings to healthcare professionals for informed decision-making.
Implemented PID control for precise speed regulation in the F1 Formula Car. Utilized advanced electronics and embedded systems to achieve optimal performance.
Identified at-risk customers to improve retention strategies and reduce business losses. Applied machine learning with Python for predictive modeling and customer segmentation. Helped surface churn drivers for early intervention.
Designed custom PCBs for various electronic projects. Implemented circuit designs and performed thorough testing to ensure functionality. Created professional-looking printed circuit boards that meet the required specifications.
Developed an autonomous robot capable of navigating and performing tasks in dynamic environments. Utilized sensor fusion and machine Control algorithms to enhance decision-making capabilities.
Engineered a high-performance computational system to demonstrate advanced C++ architecture principles. Developed optimized algorithms, custom data structures, and memory-aware processing. Improved overall runtime efficiency through careful optimization.
Built a multi-stage satellite imagery pipeline combining classic DIP enhancement (CLAHE, adaptive gamma correction, denoising) with deep learning-based segmentation and detection.
Automated large-scale web data extraction to eliminate manual collection work. Built using Python, Selenium, and BeautifulSoup with support for JavaScript-rendered pages. Increased data collection efficiency through automation.
Ready to discuss your next project? I'd love to help you achieve your goals. Reach out via email, phone, or fill out the contact form below. Let's make something great together!