Tool Calling Experiment
Experimental framework for enhancing LLM capabilities through dynamic tool integration, exploring function calling patterns, tool selection strategies, and error recovery mechanisms.
A collection of data science and machine learning projects showcasing my expertise in AI, analytics, and software development.
Experimental framework for enhancing LLM capabilities through dynamic tool integration, exploring function calling patterns, tool selection strategies, and error recovery mechanisms.
A distributed multi-agent system where specialized AI agents collaborate, communicate, and coordinate to solve complex tasks that exceed the capabilities of individual agents.
A comprehensive analytics dashboard that integrates multiple machine learning models for real-time monitoring, performance comparison, ensemble predictions, and continuous learning across diverse data sources.
An intelligent learning analytics system that uses GraphRAG to track student learning journeys, identify knowledge gaps, and provide personalized recommendations based on concept relationships and learning pathways.
A graph-based Retrieval-Augmented Generation system that leverages knowledge graphs to enhance contextual understanding and improve LLM responses through structured relationship mapping.
A framework for discovering causal relationships from observational data and constructing causal graphs that enable counterfactual reasoning and intervention analysis for robust decision-making.
Designed and implemented an intelligent platform for automated assessment of learning outcomes. Features include adaptive testing, performance analytics, and personalized feedback systems.
Published in ACM Computing Surveys (2026), this comprehensive survey reconceptualizes explainability as a reflexive, system-level property spanning the entire ML lifecycle, introducing a cognitively grounded taxonomy and lifecycle-centric architecture for XAI.
Built a comprehensive framework for making machine learning models interpretable and transparent. Implemented various XAI techniques including SHAP, LIME, and feature importance analysis.
Developed an automated machine learning platform that streamlines the model development process. Features include automated feature engineering, hyperparameter optimization, and model selection algorithms.
Created a sophisticated stock market prediction system using time series analysis and deep learning models. Achieved 15% improvement in prediction accuracy compared to traditional methods.
Developed an NLP-based system that analyzes text to predict personality traits using machine learning algorithms. Processed large datasets and achieved 85% accuracy in personality classification.
Created an innovative virtual reality-based training simulator for medical ultrasound procedures. Combines computer vision and haptic feedback for realistic training experience.