Institute for Data Science and Big Data: General Additive Models(GAMs)

Winter 2025

Institute for Data Science and Big Data : Workshop on R and Overleaf: Political Analysis Using American Politics Data

Winter 2025

Institute for Data Science and Big Data *: AI in Political Science (Transfer Learning)

1- R for GAM

1-Data Wrangling with R

Introduction to Transfer Learning/Computer Vision

Winter Institute for Data Science : Non-linear Models

Winter 2024

2- Teaching Experience

Institute for Data Science and Big Data* – American University

• Instructor: Large Language Models for Social Science: Foundations and Applications (Spring 2026)

• Instructor: Nonlinear Methods for Social Science: ​ Why We (Often) Must Move Beyond Linearity (Spring 2026)

• Instructor: Non-linear Methods and General Additive Models (GAMs) (Winter 2024)

• Instructor: AI & Transfer Learning in Political Science (Winter 2024)

• Instructor: Workshop on R and Overleaf: Political Analysis Using American Politics Data (Winter 2024)

School of Public Affairs – American University (2022–2025)

• Teaching Assistant & Guest Lecturer: Congress and Legislative Behavior

• Teaching Assistant & Guest Lecturer: Political Conflict (Two Sections)

• Teaching Assistant & Guest Lecturer: Elections and Voting Behavior

• Teaching Assistant: Winter Institute of Data Science (Graduate Course)

• Teaching Assistant: Applied Political Data Science (Graduate Course – Two Sections)

• Teaching Assistant & Lab Proctor: Introduction to Quantitative Political Research (Two Sections)

• Teaching Assistant: Winter Institute of Data Science (Graduate Course)

College of Arts & Sciences – American University

• Teaching Assistant: DATA-613 Data Science Course (Graduate Course – Four Sections)

• Teaching Assistant: STAT-614 Statistical Methods (Graduate Course)

• Teaching Assistant: Statistical Reasoning (Two Sections)

Ferdowsi University (2015)

• Fundamentals of Physics (TA)

Tutor (2005–2017)

• Mathematics & Statistics

Winter 2025

Nonlinear Methods for Social Science: ​ Why We (Often) Must Move Beyond Linearity

Large Language Models for Social Science: Foundations and Applications

Useful Books :

1- American University students have free access to O'Reilly Online Learning. Use it!

https://www.oreilly.com/library/view/natural-language-processing/9781098136789/

https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/

https://jalammar.github.io/illustrated-transformer/

2- These are free and a great start for ML

An Introduction to Statistical Learning with Applications in R, by G. James, D. Witten, T. Hastie, and R. Tibshirani. 2 nd edition, 2021

An Introduction to Statistical Learning with Applications in Python, by G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, 2023

https://www.statlearning.com/

3- Original paper: "Attention Is All You Need" (Vaswani et al., 2017)

https://arxiv.org/abs/1706.03762

Useful links and classes:

Cursor ai

https://cursor.com/

What is a transformer model?

https://www.ibm.com/think/topics/transformer-model

Stanford Online

Stanford CS230 | Autumn 2025 by Andrew Ng

https://www.youtube.com/watch?v=Ozb1AR_F5MU

Stanford CS230 | Autumn 2025 By Kian Katanforoosh

https://www.youtube.com/watch?v=_NLHFoVNlbg&list=PLoROMvodv4rNRRGdS0rBbXOUGA0wjdh1X

Institute for Data Science and Big Data - American University, School of Public Affairs

Spring 2026

1- Code & notes

Institute for Data Science and Big Data* - American University, School of Public Affairs

*This is an intensive course offered to both undergraduate and graduate students during the late winter and early spring of each year in the School of Public Affairs. The course is organized into modules covering different methods with several instructors, rather than functioning as a full-semester class. I was not the instructor of record for the overall course; instead, I served as the instructor for the mentioned modules.