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

This course equips students with computational methods spanning the humanities, social sciences, and data science. Through Python programming, data visualization, and modeling, students analyze everything from literary texts to social networks. The course examines how digital tools transform our understanding of human behavior and society while tackling crucial questions about AI, surveillance, automation, and transhumanism. By combining quantitative methods with critical analysis, the course prepares students to both understand and shape our increasingly algorithmic world. This course serves as the gateway course in the IPHS AI curriculum. We recommend that students without prior data science or programming experience take this course before enrolling in more advanced AI courses. \n\n

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 explores artificial intelligence through both technical implementation and humanistic inquiry. Building on the programming foundations from IPHS 200, students learn to build and critically evaluate AI systems, from classical machine learning approaches to cutting-edge deep neural networks and large language models. Through hands-on projects, students create AI systems that generate music, analyze text, classify images, and more. The course pairs technical training with readings from philosophy, ethics, and critical theory to examine fundamental questions about creativity, intelligence, and what it means to be human in an age of artificial minds. The course emphasizes both technical competency and critical thinking, preparing students to be thoughtful practitioners and critics in our AI-driven future. Prerequisite: prior programming experience (such as IPHS 200); students will be implementing machine learning models and working with industry-standard AI tools.

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