Iman Tabari

Productivity Management: • Used logistic regression to classify projects based on costs and earnings, enabling data-driven decisions. • Developed multilinear regression model to predict total productivity, achieving 7% improvement. Empowered organization to optimize productivity confidently.

• Conducted quantitative risk analysis on maintenance data using the KNN algorithm to predict failure consequences and optimize maintenance planning, resulting in a 12% reduction in failures. • Developed a multilinear regression model to enhance equipment availability by 5%, identifying key factors influencing availability in maintenance data analysis. • Utilized design of experiments and statistical models like ANOVA to optimize production specifications, achieving an 11% improvement in product quality.

Multiple coursework such as Coding Fundamentals, Application Development, Automation, Data Preparation, Machine Learning, Deep Learning and big data, Decision making and statistics, Business and Finance fundamentals, Financial Markets. Thesis: Forecasting Temperature Anomalies: A Time- Series Modeling and Analysis Approach for 3- Month Ahead Prediction

Thesis: I introduced two Evolutionary models to solve a Multi-Objective problem for scheduling in a flexible flow-shops environment with buffer constraints and sequence-dependent setup times.