Sheikh Sharfuddin Mim

- Contribute to research on privacy-preserving federated learning, with use cases in finance and healthcare, and to AI/ML projects. - Prepare and validate datasets with Python and SQL, run data quality checks, and apply preprocessing steps. - Implement ML workflows in Python and evaluate model results with error metrics and statistical methods, and prepare scientific papers using LaTeX. - Mentor students on data preparation, data modeling, and ML workflows; review their projects; and give feedback.

- Queried and prepared research datasets with SQL and Python, and applied statistical methods to check them. - Cleaned and standardized raw data and used exploratory analysis to find missing values, outliers, and inconsistencies before the data went into models. - Built machine learning models, evaluated results, and traced problems in the data that affected them. - Documented the datasets, preparation steps, and findings, and presented them in visualizations for the team.

- Consolidated sales and logistics data in Excel with lookup functions and PivotTables and checked it for duplicates and mismatched records before each report. - Produced recurring reports on revenue, profitability, and product performance for management, including period-over-period comparisons of sales trends.

- Wrote SQL for recurring reports on production, inventory, order, and quality data used by operations teams and management. - Supported ETL process: extracted data from source systems, applied transformation and validation rules, and loaded it into central reporting database. - Tracked KPIs against targets, analyzed actual vs. target variances, and flagged deviations and trends to operations teams. - Built reports with Excel (PivotTables, PivotCharts, lookup, and cond. formatting) for weekly KPI review.