Abbas Goudarzi, Ph.D. ( Certified Data Scientist Physics Professional)

+1-940-***-**** ️ ad0v8q@r.postjobfree.com Linkedin.com Scholar.google.com Github.com PROFESSIONAL SUMMARY

● Experienced Computational Physicist and Certified Data Scientist with a strong track record in data-driven approaches, including scientific research, advanced data analysis, machine learning, data modeling, and experience working with supercomputers.

● Over 12 years of professional expertise in implementing computational projects using Matlab and Python across various unstructured and structured datasets.

● Proficient in using Data Science tools like SQL and Oracle databases, as well as Python Libraries and packages for Machine Learning and Natural Language Processing.

● Competent in various IDEs, such as PyCharm, Spyder, Jupyter Notebook, and Visual Studio Code.

● Skilled in version control using Git and platforms like GitHub for collaborative data science projects.

● Skilled in leadership, communication, teamwork, and independent problem-solving. Adept at root cause analysis and adeptly implementing tailored solutions for both technical and non-technical audiences. Core Skills and Knowledge

● Software: JDFTx, Solid Works, PowerBI, Tableau, Matlab, Zemax, RapidMiner

● Languages: Python, Matlab, Octave

● Python Libraries: Pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly, Scikit-Learn, TensorFlow, Tableau, NLTK

(Natural Language Toolkit), spaCy, Gensim, Stanford NLP.

● Python IDE: Jupyter Notebook, PyCharm, Spyder,

● Operating systems: Windows, Unix/Linux

● Databases: SQL

● Machine Learning; Supervised learning methods such as binary and multiclass classification using SVM, XGboost, random forest, and KNN, as well as linear regression,

● Data Mining: Performing Unsupervised Learning methods such as Kmeans clustering, as well as LDA and LSA

● Data Visualization: Creating interactive visualzitions using Tableau, PowerBI and Python packages libraries.

● Data Engeenring: Transforming unstructured categorical and numerical time series data assets into organized and structured datasets for subsequent analysis and visualization, utilizing Python data frames and libraries. WORK EXPERIENCE

Computational Researcher (Center for Nolinear Science, UNT, Denton, TX) Aug 2017 - Sep 2023

● Created mathematical and computational models to simulate nonlinear physcis phenomenon using varies types of data.

● Developed data preprocessing pipelines using Scipy and pandas to handle noise reduction, outlier detection, and missing data imputation for IMU time series data.

● Crafted Python scripts to preprocess, clean, and format raw IMU time series data using the pandas API.

● Implemented algorithms for feature extraction from time series data collected using sklearn.

● Impelmeted supervised and unsupervised machine learning models, including Linear Regression, Logistic Regression, and Multiclass classification, utilizing Python libraries such as Scikit-learn and Statsmodels for different classification use cases like fraud detection, job advertisement analysis.

● Performed hypothesis testing, regression analysis, and statistical significance tests to draw insights from data.

● Conducted data visualization for each axis of gyroscope and accelerometer time series data using Seaborn and Matplotlib.

● Scraped online resources, job advertisement, using Scrapy.

● Leveraged NLP libraries such as for in-depth analysis of the textual datasets.

● Generated visualizations to represent the extracted knowledge from data science job advertisements using Tableau, Seaborn, Matplotlib, and Plotly.

Technical Environment: Python, Numpy, Scipy, pandas, Tableau, Seaborn, Matplotlib, Plotly, Scrapy, NLTK, SpaCY, Django, PL/SQL, Linux, HPC, JDFTx, Scikit-learn, Statsmodels, API, PyCharm, Spyder, Jupyter Notebook, Git, Github. Graduate Researcher (Department of Physics, UNT, Denton, TX) Jan 2013 - Aug 2022

● Developed a computational model in Python to demonstrate photonic spiking neurons, serving as a fundamental unit for neuromorphic computing platforms, utilizing Numpy and Scipy.

● Utilized Logistic Regression from Scikit-Learn to construct a binary classification model, fine-tuning photonic metamaterials and quantum nanorod structural parameters for superior materials and geometry.

● Created a computational model in Python and Matlab to compute the efficiency of harvesting plasmonic hot electrons from thin-film metals.

● Formulated a numerical model in Python to determine time constants of defects when trapping hot electrons at the interface of silicon oxide and gold, leading to a published journal paper.

● Executed Python and Matlab programming alongside JDFTx software on a High-Performance Computing (HPC) cluster operating Linux (Red Hat).

● Generated presentations that effectively communicate technical information to a diverse audience.

● Stayed up to date with the latest advancements in nonlinear science, machine learning, deep learning, and HPC to incorporate innovative techniques into research projects. Technical Environment: Python, Matlab, Numpy, Scipy, pandas, Tableau, Seaborn, Plotly, Linux, HPC, JDFTx, Scikit-learn, Statsmodels, PyCharm, Spyder, Jupyter Notebook, Git, Github. Software Support Specialist (System Group, Tehran, Iran) Nov 2008- Oct 2012

● Acquired expertise in System Group stock, sale, and accounting software based on SQL Server.

● Experienced in managing and maintaining sensitive big databases, creating tables, and writing queries with SQL.

● Identified hardware and software solutions, diagnosing, troubleshooting technical issues, and offered valuable feedback to software developers to solve issues by introducing a new service packs.

● Installed and configured software and its service packs, on-site or remote (TeamViewer), to meet user needs and supported the roll-out of new applications to increase operational efficiency.

● Optimized, tested existing solutions, and implemented new using PL/SQL and SQL.

● Leveraged Power BI dashboards and Matplotlib to craft compelling visualizations from diverse datasets.

● Used Matlab to perform data manipulation on MDF files. Technical Environment: SQL, SQL Server, Oracle, Microsoft Office, TeamViewer, Git, Matlab, NumPy, pandas, asammdf, Matplotlib, Power BI, Linux.

CERTIFICATIONS

The Data Science Course: Complete Data Science Bootcamp(Udemy, 2023), NLP - Natural Language Processing with Python

(Udemy, 2023), A Complete Guide on TensorFlow 2.0 using Keras AP (Udemy, 2023), OOP Python 3: Deep Dive (Udemy, 2023), Machine Learning (Coursera, 2019), Neural Networks and Deep Learning (Coursera, 2020). EDUCATION

● Ph.D. in Physics, University of North Texas, Denton, TX, USA Aug. 2022

● M.S. in Applied Physics, Amirkabir University of Technology, Tehran, Iran Jun. 2008

● B.S. in Applied Physics, Chamran University, Ahvaz, Iran Jun. 2003 SELECTED PEER-REVIEW PUBLICATIONS

● A. Goudarzi, S. Behpour, R. Sundararaman, O. N Garcia, Y. Rostovtsev, Trap dynamics of hot electrons in metal-insulator-metal plasmonic structures for ultra-fast optoelectronics, Journal of Applied Physics 131, 194501 (2022)

● A Bafekry, CV Nguyen, A. Goudarzi, M Ghergherehchi, M Shafieirad, Investigation of strain and doping on the electronic properties of single layers of C6N6 and C6N8: a first-principles study, RSC Advances 10 (46), 277**-***** (2020)

● Behpour, S., Vispute, D. S., Goudarzi, A., & Hawamdeh, S. (2019). "Employer’s Perspective on Data Science: An Analysis of Job Requirements-Learning Objectives." Presented at ALISE International Conference Proceedings.

● Behpour, S., O’Connor, B. C., Goudarzi, A., & Hawamdeh, S. M. (2018). "Fostering Scholarly Creativity: Modeling Functional Browsing through the Lens of Complexity." Presented at the International Conference on Knowledge Management (ICKM) Proceedings.

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