This Sr. Machine Learning Engineer has experience in modeling, transforming, interpreting and analyzing data to drive successful solutions.
- Proficient knowledge in Machine learning, SQL, programming and analysis. Experienced at creating predictive models, using different data modeling, and analyzing data mining algorithms to deliver insights.
- Developed several logistic regression models with K-fold cross-validation and regularization (Lasso and Ridge) for predicting patient critical factors such as mortality, inpatient duration, and hospital re-admission.
- Implemented a POC for building a recommender system using model-based and KNN-based collaborative filtering model for recommendation system
- Programming library & languages: Python (Flask, Skid Learn, Pandas, Numpy, NLTK, Tensorflow, Pytorch, Keras, Surprise), R, PySpark, Git
- Cloud Solutions: Microsoft Azure (Databricks, Data Factory, LogicApp, Power BI, ADLS), AWS (EC2, ECS)
- Virtualization: Kubernetes, Docker container
- Machine learning Algorithm: Regression (Linear and Logistic), Classification (Decision Tree, GBT, SVM), Clustering (K-Mean, Hierarchical), Deep Learning (RNN, CNN, Transformers), Time Series(ARIMA model)
- Statistical Method: Hypothesis Testing, Test of Randomness, significance
- University of Texas at Arlington, M.S. Computer Science
- University of Tabriz, M.S. Mechanical Engineering
- AmirKabir University, B.S. Mechanical Engineering