Student Research
Research Focus
My research explores the intersections of artificial intelligence and human experience, with particular attention to the mutual shaping of technology, society, and culture.
This work engages three key areas:
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Natural Language Processing (NLP): Investigating computational models for understanding and generating human language, including large language models, text analysis, and human-AI communication.
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Human-AI Interaction: Studying how people engage with AI systems, with emphasis on evaluation, interpretability, and user experience design.
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Critical AI Studies: Examining the broader implications of AI technologies through interdisciplinary methods that combine technical inquiry with insights from ethics, philosophy, and the social sciences.
Student Involvement
Muhlenberg students interested in these topics are welcome to reach out. While programming skills (especially in Python) are useful, I value:
- Curiosity about the societal roles of AI
- Interest in interdisciplinary approaches
- Willingness to engage with both technical and theoretical perspectives
Students can get involved through independent studies during the academic year or through summer research opportunities. If you’d like to discuss a possible project or collaboration, please don’t hesitate to contact me.
Current Projects
- Nightshade/Glaze VLM Evaluation Harness: An evaluation harness for testing how vision-language models perceive and respond to images protected by anti-AI-training tools such as Nightshade and Glaze.
- The Grammar of Belief: Testing whether language models can separate what someone believes from what is true, in English and in Turkish.
- Noisy Stars: Rating Reliability and the Superhost Premium: Whether the Airbnb Superhost rating threshold can reliably tell listings apart, and what the badge is actually worth.
- Raising Reachy: Framing, Values, and Responsibility in LLM-Powered Robot Interaction: Studying how undergraduates “raise” an LLM-powered Reachy Mini robot framed as a learner, and the values and behaviors they pass on to it.
- Seasonal Changes in AI: From AI-Springs to AI-Winter: A corpus of news, papers and videos on AI’s booms and busts, now the starting point for a study of how “the AI winter” has been named and dated.