Jon Chun has undergraduate and graduate degrees in computer science and electrical engineering from UC Berkeley and UT Austin. He has done postgraduate fellowships and NSF research in gene therapy, electronic medical records, and semiconductors at the University of Iowa Medical School, MIT and SEMATECH. After working in large organizations including national labs, finance, and insurance, he co-founded startups in Japan, Brazil and Silicon Valley. He co-authored patents at his last startup to build the world’s largest privacy/anonymity service and designed the first web-based VPN linux appliance. Immediately prior to coming to Kenyon, he was a Fortune 500 director of development for the world’s largest computer security company, entrepreneur in residence at UC Berkeley and judged startup competitions at Berkeley Engineering Graduate School and OSU. 

In 2020 he and Katherine Elkins published "Can GPT-3 pass a Writer’s Turing Test?" in the Journal of Cultural Analytics; one of the first critical reviews of generative AI with 60 academic citations currently. Last year they published "What the Rise of AI Means for Narrative Studies: A Response to ‘Why Computers Will Never Read (or Write) Literature’ by Angus Fletcher." in Narrative. He gave the first presentation on GPT-2 at the Narrative Conference in 2020 and this March is presenting “Augmenting Narrative Generation with Visual Imagery Using Integrated Prompt Engineering (ChatGPT, DALL-E 2)” at Narrative 2023. His 2023 article "Exploring the Black Box: Narrative XAI (eXplainable AI)" will appear in the International Journal of Digital Humanities (IJDH) 2023 Special Issue on "Reproducibility and Explainability in Digital Humanities."

Areas of Expertise

Human-centered CS, AI/ML, Narrative NLP, eXplainable AI, AI Fairness-Accuracy-Transparency-Explainability, HealthTech, FinTech, Cybersecurity, Startups, SciComp


1995 — Master of Science from University of Texas at Austin

1989 — Bachelor of Science from Univ. of California Berkeley

Courses Recently Taught

Centered on the big questions emerging from the rise of big data and AI, this course offers an interdisciplinary, humanities-centered introduction to programming and data analysis. As part of the new data humanities movement, our focus is on telling the stories we find in data, exploring how to count what counts and critically quantifying issues of bias and representation. With hands-on projects like analyzing Netflix data and exploring the Twitterverse, we also build the foundation for topics covered more fully in intermediate courses: natural language processing, social network models, and machine learning and artificial intelligence. No prerequisite.

Cultural analytics is the study of culture using diverse sources and data-driven methods. We analyze language from texts to tweets and social networks from film to the Twitterverse. In this project-based course, students code ways to explore phenomena like the social networks in "Game of Thrones" and the classification of tweets as Trump or Trudeau. They apply what they have learned for a final project of their choice. Students new to coding should contact the instructor for information on how to complete a self-paced mini coding course before the start of the semester. This course does not count toward the completion of any diversification requirement. No prerequisite. Offered every other year.

This course is an interdisciplinary, humanities-centered coding course that explores the philosophical and ethical questions raised by AI. Ethical questions include issues of bias, fairness and transparency, as well as AI-human value alignment. We explore AI as a mirror of both our best and worst natures: how it can surveil, disemploy and police, but also play games, write text, create images and compose music. Prerequisite: any IPHS course.

The Individual Study is to enable students to explore a pedagogically valuable topic in computing applied to the sciences that is not part of a regularly offered SCMP course. A student who wishes to propose an individual study course must first find a SCMP faculty member willing to supervise the course. The student and faculty member then craft a course syllabus that describes in detail the expected coursework and how a grade will be assigned. The amount of credit to be assigned to the IS course should be determined with respect to the amount of effort expected in a regular Kenyon class. The syllabus must be approved by the director of the SCMP program. In the case of a small group IS, a single syllabus may be submitted and all students must follow the same syllabus. Because students must enroll for individual studies by the end of the seventh class day of each semester, they should begin discussion of the proposed individual study preferably the semester before, so that there is time to devise the proposal and seek departmental approval before the registrar’s deadline. This interdisciplinary course does not count toward the completion of any diversification requirement. Permission of the instructor and program director required. No prerequisite. \n