Experience

  1. Data Scientist

    Arizona State University

    Responsibilities include:

    • Consulted faculty on GenAI, computer vision, and NLP projects.
    • Collaborated with faculty and researchers to integrate AI tools.
    • Instructed workshops on geAI, LLMs, NLP, classic ML, network analysis.
    • Led 10+ projects using classic ML and modern DL
    • Mentored 20+ students in data science projects and conference presentations.
    • Presented research findings at 6 conferences on AI, digital humanities.
    • Co-organized and served as a judge in various data science hackathons.

  2. Research and Teaching Associate

    Arizona State University

    Responsibilities include:

    • Conducted advanced research using both NHST and predictive modeling.
    • Collected and large-scale data and performed advanced regression analysis.
    • Instructed courses in statistics, research methods, and R and Python
    • Mentored students on thesis design, data analysis, and academic writing.
    • Presented research findings at academic conferences
    • Published peer-reviewed articles using ML, advanced regression, qual methods
    • Received first place award for lab experiments on climate change

Education

  1. PhD Political Science

    Arizona State University
    GPA: 3.74/4.0

  2. MS Data Science

    University of Arizona

    GPA: 4/4.0

    Courses included:

    • INFO 502 Data Ethics
    • INFO 523 Data Mining/Discovery
    • INFO 526 Data Analysis and Visualization
    • INFO 511 Foundations of Data Science
    • INFO 555 Applied NLP
    • INFO 557 Neural Networks

  3. BA European Studies and Economic Sciences

    Qafqaz University

    GPA: 99/100

    Courses included:

    • Microeconomics
    • Macroeconomics
    • International Economics
    • Informatics 1
    • Informatics 2
    • Research Methods
Skills & Hobbies
Technical Skills
Python
Data Science
SQL
Hobbies
Hiking
Cats
Photography
Awards
Neural Networks and Deep Learning
Coursera ∙ November 2023
I studied the foundational concept of neural networks and deep learning. By the end, I was familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
Blockchain Fundamentals
edX ∙ July 2023

Learned:

  • Synthesize your own blockchain solutions
  • Gain an in-depth understanding of the specific mechanics of Bitcoin
  • Understand Bitcoin’s real-life applications and learn how to attack and destroy Bitcoin, Ethereum, smart contracts and Dapps, and alternatives to Bitcoin’s Proof-of-Work consensus algorithm
Object-Oriented Programming in R
datacamp ∙ January 2023
Object-oriented programming (OOP) lets you specify relationships between functions and the objects that they can act on, helping you manage complexity in your code. This is an intermediate level course, providing an introduction to OOP, using the S3 and R6 systems. S3 is a great day-to-day R programming tool that simplifies some of the functions that you write. R6 is especially useful for industry-specific analyses, working with web APIs, and building GUIs.
See certificate
Languages