Engineering Data

Vahid Azizi

US Green Card Holder

Overview

Programming

Python

R

SQL

Matlab

GAMS

Education

Ph.D. Industrial Engineering (GPA 3.84)

Iowa State University (2017-2020)

Minor Ph.D. Statistic (GPA 3.84)

Iowa State University (2018-2020)

M.S. Industrial Engineering (GPA 3.82)

K. N. Toosi University (2011-2013)

B.S. Industrial Engineering (GPA 3.61)

Payame Noor University (2006-2010)

Projects

Abnormality Detection in Manufacturing Process with Data Analytics

• Applied unsupervised learning methods to eliminate noisy data points

• Developed supervised learning methods to predict and monitor CNC machine’s tool load in order to detect abnormalities

Corn root’s diseases prediction

• Applied feature engineering, data augmentation and data visualization techniques on corn root images

• Constructed deep convolutional neural networks using transfer learning in Keras and fastai Google Analytics Customer Revenue Prediction

• Applied feature engineering methods and used GBM, XGBoost, LGBM and Cat Boost algorithms to predict customer revenue (900000 observations), coded in Python EXPERIENCES

Principal Financial Group

Data and Operations Research Scientist Intern May 2020 - Present

• Redesigned available pipelines using big time-series datasets

• Modernized pipeline using AWS cloud9 for coding and S3 to load and store the data

• Developed ETL using Airflow to schedule and monitor product’s workflow

• Applied machine learning techniques in scikit-learn package and ensemble methods to improve the prediction results in pipelines

• Strengthened pipelines by adding following regression and classification models: Stepwise, Lasso, Ridge, ElasticNet, Gradient Boosting Machine, Random Forest, Naïve Bayes, KNN, Bayesian Factorization Machines, Hierarchical Linear Model

• Designed unit tests for all modules of pipelines

• Delivered visualized results using Plotly package Iowa State University

Research Assistant 2017-2020

• Developed and analyzed advanced supervised and unsupervised machine learning techniques in various industrial projects

• Developed deterministic and stochastic optimization models for complex supply chain and forward/reverse logistics systems

Skills and Awards

• Computer Programming Languages: Python (Airflow, Scikit-learn, TensorFlow, Keras, fastai, OpenCV, Pandas, Numpy, Plotly, Matplotlib, Seaborn), R including Shiny, SQL, MATLAB

• Cloud Services: AWS Cloud9, AWS S3, Snowflake, Matillion

• Version control and collaboration: GitHub

• Engineering Software Packages: JMP, Minitab, GAMS, CPLEX, Lingo, Octave, Gusek

• Application Software Packages: LaTeX, Microsoft Office

• Well versed in statistical analysis, predictive modeling, clustering, and deep learning

• Harold and Shirley Reihman Graduate Scholar Award

• Research Excellence Award

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LinkedIn Profile

Google Scholar Profile

Industrial

Engineering

Operations

Research

Statistics

Optimization

Data Science

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