• Assisting in teaching core AI topics including Agentic AI, machine learning, and decision-making models.
• Guiding students on implementing AI solutions using Python, R, and SQL, with emphasis on model evaluation and deployment.
• Mentoring students on AI-driven projects, from conceptual design to debugging and optimization.
• Supporting students in analyzing algorithm complexity, dynamic programming, and graph-based problem-solving.
• Assisting in debugging and optimizing Python/R implementations of advanced algorithms.
• Helping design and grade assignments focused on data structures, search/sort strategies, and optimization techniques.
• Maintained and enhanced ChatSSRN, a GenAI-powered research assistant leveraging HPC and NLP to efficiently index and retrieve research abstracts.
• Integrated 2024–2025 datasets, improving model accuracy, freshness, and relevance of academic literature recommendations.
• Supported circulation desk operations by checking out and returning materials, managing user accounts, and maintaining accurate digital records in the library system.
• Gained experience in data entry, information organization, and process accuracy while assisting patrons and ensuring smooth workflow.
• Developed skills in data organization, information retrieval, and maintaining structured systems that support efficient information management.
• Collaborated with team members to ensure smooth daily operations, strengthening teamwork, adaptability, and process efficiency.
• Maintained reliability through effective time management while meeting strict service deadlines and handling tasks under pressure.
• Delivered customer-focused service by addressing needs quickly, applying strong communication skills, and practicing user-centric problem-solving.
• Developed Python scripts for data analysis, automating workflows and reducing manual effort by 30%, ensuring timely and accurate project deliverables.
• Applied NLP techniques and data science methods (topic modeling, sentiment analysis) to analyze unstructured text data, improving insight extraction efficiency by 40%.
• Built and deployed a machine learning model using Streamlit, creating an interactive web app that enabled non-technical stakeholders to access real-time recommendations.
• Worked on two machine learning projects: Red Wine Quality Prediction using SVM, achieving ~85% accuracy on a dataset of 4,800+ samples, and Cardiac Arrest Analysis using Clustering, uncovering 3–4 distinct patient risk groups from 1,500+ records.
• Applied data cleaning, feature engineering, and evaluation metrics to improve model reliability by 15-20%.
• Strengthened skills in classification, clustering, and data-driven analysis across diverse datasets.
