Research & Projects

Naturallangprocessors-poster.pdf

Project 1

Translated Navigation Instructions for Robot Navigation

This project was conducted during my undergraduate studies at IIT Gandhinagar as part of a natural language processing course. The goal was to bridge human language and robotic execution by converting navigation instructions into actionable commands for autonomous systems.

Objectives

The project aimed to build an NLP pipeline that could accurately parse and translate complex human instructions into robot-compatible language. Major challenges included dealing with unstructured language, ensuring contextual accuracy, and optimizing performance on large datasets

Results

The solution achieved 82% accuracy on a gigabyte-scale dataset by implementing a "learning in chunks" strategy, which outperformed baseline models. This reinforced my interest in NLP applications and gave me valuable hands-on experience in data preprocessing, tokenization, and model training.

Project 2

Advanced Multimedia and Web Database Systems: Human Motion Video Analytics

This project was completed as part of an advanced database systems course at Arizona State University. The goal was to design a multimedia video retrieval system leveraging computer vision and deep learning techniques. I worked independently with guidance from course instructors to apply theory in a practical and scalable system.

Objectives

The primary aim was to implement a video analytics system that could classify and retrieve human motion activities from a large video dataset. Key objectives included accurate feature extraction using deep learning (R3D-18), clustering for organization, and dimensionality reduction for performance optimization. Considerations included system accuracy, search speed, and similarity ranking quality.

Results

Successfully built a prototype that achieved 77% retrieval accuracy using Locality Sensitive Hashing. Classification of 51 motion types was achieved through k-NN and clustering with K-means. Advanced reduction techniques like PCA, SVD, and LDA improved efficiency, confirming the system’s potential for real-world video search applications.

Project 3

Parameter Optimization using Machine Learning in Additive Manufacturing | Skills Gained: ANN

Conducted at IIT Gandhinagar, this project explored the use of artificial neural networks (ANN) to optimize parameters in Selective Laser Melting (SLM), a key process in metal additive manufacturing. The project blended materials science with machine learning, aligning with my interdisciplinary academic background.

Objectives

The goal was to develop a predictive model that could optimize over five critical process parameters to improve part quality (e.g., density, microstructure). This involved creating an ANN capable of understanding the relationship between input variables and quality outcomes in Powder Bed Fusion techniques.

Results

The ANN model successfully identified optimal parameter ranges, leading to improved prediction accuracy for key quality metrics. The project demonstrated the value of ML in industrial applications and sparked my deeper interest in intelligent manufacturing systems.

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