Affiliated Departments & Programs

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 national labs and large organizations, from FinTech and HealthTech to InsurTech, he did startups in Japan, Brazil and Silicon Valley. He co-founded the world’s largest privacy/anonymity website backed by In-Q-Tel. He then pivoted the startup to enterprise network security as CEO and co-authored web-based VPN Linux appliance patents. Prior to Kenyon he sold his startup to the world's largest computer security company and became a Fortune 500 director of development, successfully rebranding and relaunching their VPN product. He was an entrepreneur in residence at UC Berkeley and judged startup competitions at Berkeley Engineering Graduate School and OSU. 

In 2016, he co-founded the world’s first human-centered AI curriculum and Colab at Kenyon College. He has mentored over 300 original student research projects in ML/AI downloaded 60k times worldwide by leading institutions like MIT, Stanford, CMU, Oxford and the Chinese Academy of Social Sciences. He is lead investigator for the Modern Language Association participation in the NIST US AI Safety Institute representing over 25 thousand scholars in literature, linguistics and languages worldwide. He is co-principal investigator for one of only three nationwide IBM-Notre Dame Tech Ethics Lab grants on AI decision-making for criminal recidivism. He co-published and presented some of the first interdisciplinary AI research at leading conferences and papers including Narrative, MLA, Cultural Analytics, the International Journal of Digital Humanities and the Journal of Humanities and Arts Computing. He has also published on medical informatics, gene therapy, as well as in traditional CS/AI venues like ICML, Frontiers in CS, and ArXiv. 

Areas of Expertise

Research in human-centered AI, AI agents, affective computing, narrative, security/privacy, generative AI benchmarking, eXplainable AI (XAI), AI fairness bias transparency explainability (FATE), ethical and compliance auditing, and AI policy/regulation. Domain expertise in HealthTech, FinTech, InsurTech, Security, and Entrepreneurship.

Education

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