• Led a dynamic project called 'Data Valuation in the Aggregate Economy' leveraging advanced machine learning techniques and textual analysis to measure the value of data in the aggregate economy, utilizing the comprehensive O*Net database.

• Conducted comprehensive data analysis tasks, including data cleaning, preprocessing, and feature engineering, to prepare the dataset for advanced machine learning algorithms, utilizing Python programming.

• Contributed to the visualization of data trends and insights through graphical representations, facilitating deeper understanding and aiding in the effective communication of findings.

• Identified temporal trends in data relevance for specific job titles through temporal analysis, providing valuable insights into the evolving landscape of data-related occupations over time.

• Proactively managed project risks through effective data validation, collaboration on algorithmic complexities, and strategic task prioritization, optimizing time efficiency and project outcomes.

Skills: The libraries used are Pandas, Matplotlib, Scikit-learn, NLTK (Natural Language Toolkit).

• Assisting in teaching graduate-level course on Knowledge Discovery in Databases.

• Addressing questions and offering in-depth explanations.

• Collaborate closely to design and grade assignments and projects using Python.

• Actively involved in extracurricular research discussions with professors and Ph.D. students, deepening

knowledge in text mining, BERT, language models, and tuning beyond the standard curriculum.

• Skills: The libraries worked Pandas, NumPy, Matplotlib, Keras, TensorFlow, NLTK, scikit-learn.