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M.Sc. Applied Statistics student focusing on AI, RAG, and Statistical Modeling with Python.
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Highly motivated M.Sc. Applied Statistics student with a focus on Artificial Intelligence and process automation. Skilled in developing multi-agent AI systems, Retrieval-Augmented Generation (RAG), and statistical modeling. Experienced in using Python to automate complex data workflows and build decision-support tools. Passionate about applying AI-driven solutions to enhance operational efficiency and data-driven decision-making.
Veer Narmad South Gujarat University
Master of Science · Applied Statistics
August 1, 2024 – June 30, 2026
Veer Narmad South Gujarat University
Bachelor of Commerce · Accounting & Auditing
August 1, 2021 – June 30, 2024
StatAgents: Design and Evaluation of a Retrieval Oriented Statistical Intelligence System
January 1, 2025 – June 1, 2026
Developed an AI-driven statistical intelligence system using Retrieval-Augmented Generation (RAG) combined with Graph-based retrieval (GraphRAG) to automate statistical query answering. Designed integrated pipeline combining retrieval, reasoning, and generation, reducing manual effort in solving complex statistical queries. Engineered a complete data processing and knowledge indexing pipeline, performing document preprocessing, cleaning, and transformation, applying recursive text splitting and chunking strategies for context preservation, and generating semantic embeddings for efficient retrieval. Implemented FAISS-based vector search for high-performance semantic retrieval, converting documents and queries into embeddings for similarity-based search, and enabling fast and scalable retrieval across large datasets. Built advanced GraphRAG architecture for contextual and relational understanding, implementing Local GraphRAG to retrieve directly connected statistical concepts and Global GraphRAG to capture broader multi-concept relationships, and enabling multi-hop reasoning for complex analytical queries. Designed intelligent query routing and retrieval mechanism, transforming user queries into embeddings and graph-aware representations, and combining vector search (FAISS) with graph-based retrieval for improved accuracy. Developed prompt augmentation layer for context integration, combining retrieved vector results and graph-based insights before LLM processing, and improving response quality and reducing hallucination. Integrated Large Language Models (LLMs) for response generation, generating structured, explainable, and step-by-step statistical solutions, and enabling natural language interaction for analytical problem-solving. Developed multi-agent architecture for workflow automation, designing specialized agents for handling different statistical tasks, and enabling scalable, modular, and plug-and-play system allowing easy addition of new agents without modifying core architecture. Automated statistical reasoning and decision-support system, combining theoretical knowledge with computational outputs, and delivering consistent and reliable analytical explanations. Conducted comparative evaluation of retrieval methods, comparing FAISS, Local GraphRAG, and Global GraphRAG performance, and identifying optimal retrieval strategy based on accuracy and relevance. Designed system for scalability and real-time query handling, ensuring smooth data flow between retrieval, graph, and generation layers, and building flexible architecture for future expansion and integration. Improved reliability of AI outputs in domain-specific applications, reducing hallucination by grounding responses in vector and graph-based knowledge, and developing a robust AI system for statistical learning and decision-making.
View ProjectOne Day National Seminar on “Indian Statistical Knowledge System”
Veer Narmad South Gujarat University (PM-USHA)
January 1, 2026 – Present
Two Days National Workshop on “Time Series Analysis Using Open-Source Software”
Veer Narmad South Gujarat University (PM-USHA)
January 1, 2026 – Present
National Service Scheme (NSS) Certificate
Veer Narmad South Gujarat University
January 1, 2025 – Present
Statistical Exploration through Machine Learning Techniques with Python
Unknown
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic background in Applied Statistics and the 'StatAgents' project align well with a data-driven and analytical culture. The project's focus on innovation (RAG, GraphRAG, multi-agent systems) and problem-solving indicates a proactive and curious individual. However, the lack of diverse project experience outside of a single academic project and no professional work experience limits the assessment of broader cultural adaptability and experience in different team dynamics. The certifications are primarily academic workshops, further emphasizing a strong academic focus.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills and a proactive mindset, as evidenced by the comprehensive 'StatAgents' project. The project description highlights an ability to design complex systems, evaluate different approaches, and focus on practical outcomes like reducing manual effort and improving reliability. The mention of 'Good Team Player' and 'Communication' in skills suggests an understanding of collaborative work environments. However, without professional experience, the operational fit in a fast-paced industry setting is yet to be fully validated.