Research
My research sits at the intersection of creative writing, critical AI studies, and digital humanities. I am less interested in artificial intelligence as a set of tools or models than as a cultural, economic, and aesthetic force that is reshaping what we mean by creativity, labor, and the purpose of creative writing itself. Large language models do not simply “assist” writers; they reorganize how we imagine authorship, attention, and value—and my work asks what it means to write, teach, and live as these systems become ordinary.
Supported by institutions including NVIDIA and the University of California, Irvine Center for Asian Studies, I approach AI not as a neutral technology but as an infrastructure built on human effort: annotation labor, data collection, editorial judgment, and the histories of language that models repackage as “new.” My research traces how these hidden forms of work shape what AI can say and who gets to speak through it.
Through both critical analysis and creative practice, I examine how LLMs pressure long-standing assumptions in creative writing:
- What counts as “original” when models remix vast archives of text?
- How do we understand voice when it can be simulated, shared, or scaled?
- What does it mean to be a writer when composing, editing, prompting, and curating all become forms of authorship?
Rather than centering the models themselves, my methodology foregrounds human authorship, documentation, and responsibility. I often work with custom models trained on my own poetry and prose—not as products to be optimized, but as experimental sites where questions of consent, provenance, copyright, and creative agency can be made visible. These experiments allow me to follow the full arc of a piece of writing: from first draft, to training data, to model behavior, to final work, and to the legal and ethical frameworks that surround it.
This work directly engages with urgent debates around intellectual property, creative labor, and cultural power in the age of AI. I am especially concerned with how AI systems can either concentrate influence in the hands of a few platforms or, alternatively, be used to build more equitable, transparent, and community-centered creative practices. By combining rigorous critical frameworks with experimental writing, my research offers concrete ways for writers, educators, and policymakers to think about AI beyond hype—attending to the histories, workers, and institutions that make these systems possible.
Alongside scholarly publications, I am developing public-facing essays that translate complex questions about AI ethics, copyright, and creative labor into accessible, narrative-driven writing. These pieces treat AI as a lived condition rather than a distant technology, situating it in classrooms, dorm rooms, literary spaces, and everyday platforms where people already encounter machine-generated language.
Through projects completed with The Digital Scholarship Center at the University of Cincinnati and Yale University Press, I continue to bridge technological innovation with humanistic inquiry. Across these collaborations, I insist that AI should be something we learn to read critically and write with thoughtfully, not something that replaces or diminishes human creativity.
Current Research Projects
Research Projects My current research centers on two interconnected projects that approach AI and generative text through creative practice, critical writing, and questions of authorship, labor, and care.
Human Error Is the Point An essay collection on AI, generative systems, and the work of writing This essay collection brings together creative nonfiction and public-facing criticism that examines AI and generative language models from the ground up—from classrooms and writing workshops to dorm rooms where data is labeled late at night, to offices where everyday writing is quietly automated. Moving between memoir, reportage, and close reading, the essays ask how AI reshapes our understanding of effort, boredom, authorship, and collaboration. Rather than treating “AI in writing” as a novelty or purely technical problem, the collection argues that machine-generated text reveals what writing has always been: a social practice shaped by unequal access to time, money, and attention. Across these essays, error is not a failure to be corrected, but a critical site where ethics, creativity, and human judgment remain visible.
Our Sun Bear Paws A from-scratch manuscript trained only on my poetry dataset This creative manuscript explores what it means to write with a machine when the machine is trained exclusively on one writer’s work—and nothing else. Built from scratch using only my own poetry dataset, the project stages an evolving dialogue between human and model across multiple generations of training, drafting, and revision. Rather than aiming for seamless or “convincing” AI poetry, the manuscript traces the shifts that occur as a poetic voice is returned, distorted, echoed, and misremembered by a system designed to learn from it. Poems are arranged to reflect model evolution over time, foregrounding drift, limitation, repetition, and surprise as aesthetic and ethical forces. The project asks how authorship changes when writing becomes iterative, relational, and partially machinic—and what kinds of care, responsibility, and refusal are required when a writer builds the system that writes back.