Proficient in python and popular libraries:
• NumPy & SciPy
Experiences with C++ / JAVA / MATLAB / C#
Familiar with MySQL, parallelism
Regression and classification
• Support Vector Machines
• Neural Networks
• Naïve Bayes
• K-Nearest Neighbors
• Random Forest
• Gaussian Mixture Model
• Hierarchical Clustering
Time series analysis
Familiar with Natural Language Processing
MS, COMPUTER SIENCE
Virginia Tech, GPA: 4.00/4.00
Augu 2019 – present Blacksburg, VA
Thesis: Application of Machine Learning in
Ph.D., PETROLEUM ENGINEERING
Texas Tech University, GPA: 4.00/4.00
Aug 2015 – Aug 2019 Lubbock, TX
Thesis: Software & Modeling Development
for Wax Deposition Phenomenon
Virginia Tech Research Assistant
Aug 2019 – present Blacksburg, VA
• Developed a state-of-the-art ensemble clustering-regression model to group data into clusters using GMM to mitigate the class confusion problem and then use ANN for class label prediction. validation.
• Applied deep learning, rigid regression and random forest for subsurface temperature prediction using geological setting.
• Currently performing time-series analysis for hydrocarbon production rate and crude oil price estimation using classic time series models in addition to LSTM Recurrent Neural Networks. Texas Tech University Ph.D. Candidate & Researcher Aug 2015– Aug 2019 Lubbock, TX
• Developed two software packages for oil & gas industry containing 21,000 lines of code in C++ and Python coupled with a C# GUI. OpenMp parallelism techniques have been applied to enhance the performance (by nearly 5 times). Advanced numerical methods were programmed (e.g., Dorman Prince, Levenberg-Marquardt optimization algorithm, etc.).
• Applied Self-Organizing Map (SOM) for clustering and developed sets of Generic-Algorithm-Based Correlations for CO2 solubility estimation in deep aquifers for sequestration applications.
• Developed a simulation tool to read through real-time capacitance censor data in pipe and fit time-series models to predict various characteristics of the flow regime.
Azad University Machine Learning Researcher
Set 2012 – Aug 2014 Tehran, Iran
• Collaborated in a research project to estimate compressibility factor in gas systems. A LS-SVM model was developed which resulted in higher accuracy in compare to all other physics-based methods.
• The developed model in the previous study was then used to predict Frictional Pressure Loss in Inclined Annuli in a separate research effort and resulted in a state-of-the-art performance (R2 = 0.95, ARD = -1.68%)
RELEVANT COURSES AND CERTIFICATIONS
University courses: Data Analytics (CS5525) - Advanced Machine Learning (CS5824) - Data Structure and Algorithms (CS3114) Coursera: Practical Time Series Analysis - Sequence, Time Series and Prediction - Java Programming: Arrays, Lists, and Structured Data - Solving Problems with Software
DataCamp: Time Series Analysis in Python - Deep Neural Networks with PyTorch – Pandas Foundations - Feature Engineering for Machine Learning in Python
Published and presented five papers in peer-reviewed journals and conference proceedings
US Permanent resident