AI & Society College

About Rawan Abdelaal 

Rawan Abdelaal is a PhD student in curriculum and instruction at the University at Albany. 

She holds a bachelor’s degree in biotechnology from the City College of New York and a master’s degree in curriculum development and instructional technology from UAlbany. 

Her research explores the relationship between artificial intelligence (AI) and computational thinking (CT) at the intersection of science education. 
 

About Rawan Abdelaal's Dissertation

With the rapid advancement of AI and its ubiquitous presence in daily life, she seeks to investigate and develop methods that promote ethical, critical and effective uses of AI in K–12 classrooms, empowering educators and students to engage thoughtfully and responsibly with intelligent technologies. 

About Karan Bhasin

Karan Bhasin is a PhD candidate in Quantitative Economics and Econometrics at the University at Albany, SUNY.

His research lies at the intersection of monetary economics and applied econometrics, with a focus on how economic agents process information and update their expectations — insights that are critical for understanding inflation dynamics, fiscal policy, and macroeconomic stability.

Karan’s work has been featured in The Economist and The Wall Street Journal and has also appeared in leading policy platforms such as the Brookings Institution, VoxEU and Econofact, reflecting its broad relevance to both academic and policy audiences.

He has held research and policy roles at the International Monetary Fund, World Bank, Empire State Development (New York State), and MyGov, Government of India, bringing a global and applied perspective to his academic work. 

Currently, Karan is exploring the use of natural language processing (NLP) techniques to identify and quantify policy shocks, leveraging textual data to uncover latent signals in economic policymaking.
 

About Karan Bhasin's Dissertation

This thesis investigates the behavior of professional forecasters using data from the Blue-Chip Economic Indicators, applying a Bayesian learning framework that incorporates forecaster inattention. We address a central challenge in macroeconomic policy evaluation: identifying unanticipated monetary policy shocks. Unlike the natural sciences, macroeconomics cannot rely on controlled experiments to establish causality. 

To overcome this limitation, we deploy Large Language Models (LLMs) to systematically extract unanticipated shocks from textual data. By conditioning LLMs on specific sources and temporal contexts, we constrain their information set and improve the precision of causal inference. 

This methodology offers a robust tool for policymakers and researchers to evaluate the effects of monetary interventions and to better understand how professional forecasters respond to economic developments. Moreover, we introduce a novel identification strategy for forecaster inattention that utilizes both individual and consensus forecast revisions. 

Our analysis reveals that zero forecast revisions tend to overstate inattention, and that inattention is horizon-specific, driven in part by the regularity of statistical data releases. Persistent forecast disagreement is attributed to heterogeneous interpretations of news, although periods of club-convergence emerge during large economic shocks.

About Anastasios Karnazes

Anastasios Karnazes is a PhD candidate in English Studies at UAlbany, researching the material transformation from book technologies to artificial intelligence (AI) systems and its effects on aesthetic production. 

He is the author of Rainbow Sonnets 20, published by The Song Cave, and founder of Theaphora, an experimental book and game publisher. His work has been covered in Artforum, ARTnews, Spike Magazine, and Forbes.
 

About Anastasios Karnazes' Dissertation

This dissertation positions artificial intelligence as the exhaustion point of book logic — the moment when Enlightenment systems of knowledge production, replication, and distribution culminate in computational infrastructure and become fully legible as a unified financial, political, and aesthetic project. 

The work traces how information compression functions transformed through the printing press, encyclopedia, digital storage, networked infrastructure, and cloud computing to the memory systems undergirding AI development, arguing that this transformation refracts into dual domains: the collapse of financial systems into speculative bubbles and the aesthetic-cultural reconfigurations that respond to this trajectory. 

Drawing on both historical analysis of dissemination infrastructures and documentation of experimental work in publishing and text editor development, the dissertation demonstrates the prefiguring relationship between literary production mechanisms and computational intelligence systems, positioning AI not as revolutionary break but as the terminal form through which the book's historical trajectory becomes visible.

The College encourages undergraduate students interested in artificial intelligence to explore the following courses:

• UUNI 118: Introduction to Artificial Intelligence
• CINF 135: Concepts of Artificial Intelligence

Visit the Schedule of Classes to explore upcoming course offerings.

Microcredentials are a collection of courses and certificates that help learners build in-demand skills and competencies.

• AI Plus Fundamentals: This for-credit program includes undergraduate courses, which can stack towards a degree.
• Artificial Intelligence for Business: This for-credit program includes graduate courses, which can stack towards a degree. 

Visit the Professional and Continuing Education (PaCE) website to learn more about microcredentials. 

Google and SUNY have partnered to give SUNY students, faculty and staff free access to Google AI learning opportunities.  

Visit the Free Google AI Certificates at SUNY webpage for more information.