Teaching

  • As an instructor, I have taught students reporting, multimedia production, and ethical storytelling while fostering critical thinking about the role of journalism in a digital environment. My approach encourages students to engage with media critically and creatively as both producers and analysts.

  • I have also taught students media analysis alongside computational and analytical skills, with a focus on social platforms and digital data. Building on my primary research methodology, my instruction extends to advanced approaches such as machine learning and deep learning in applied contexts.

  • I’ve continued to grow as an instructor through SOC398T Supervised Teaching in Sociology and teaching excellence modules offered by the Center for Teaching and Learning (CTL) at UT Austin. These experiences have allowed me to engage with faculty and peers in the social science disciplines about the challenges of teaching in a rapidly changing academic environment shaped by generative AI and evolving university policies, while also fostering a reflective approach that helps me continually evaluate what and how I teach in order to refine my pedagogy.

The University of Texas at Austin, Austin, TX (Lab Instructor, Teaching Assistant, Secondary faculty Mentor, 2022-2025)

  • Discussion Lab Instructor

    J302F Digital Media Storytelling (Fall 2024, Spring 2025): This course introduces students to the evolving field of digital journalism, focusing on both traditional reporting skills and emerging storytelling tools. It emphasizes hands-on practice with interviewing, writing, fact-checking, and multimedia production while exploring how technology, data, and audience engagement shape modern journalism.

Note: I led two labs per week with 20 students in each, guiding them through audio and video storytelling projects such as podcast production and creating three-minute video stories. I also facilitated weekly analyses of multimedia news outlets, as well as news coverage of the 2024 U.S. presidential election, leading lab discussions that culminated in group presentation slides and class presentations.

  • Teaching Assistant

    J336F Social Media Journalism (Fall 2022, Spring 2023): This course focuses on developing professional skills in social media for journalism, emphasizing strategies to engage audiences, drive traffic, and strengthen personal and organizational presence. Through hands-on projects and topic-based modules, students learn to cover breaking news, apply crowdsourcing, measure campaign success, and build effective social media strategies.

    J330M True Crime Podcasts (Fall 2025): This course examines the true crime podcast genre, focusing on its rise in popularity, storytelling techniques, and cultural impact. Students explore ethical challenges, analyze how top podcasts handle sensitive topics, and study the structures and strategies that make stories compelling. By the end, participants gain the tools to critically evaluate and ethically engage with true crime media.

  • Secondary Faculty Mentor

    Bridging Disciplines Program - Digital Arts & Media (Spring 2024): The Digital Arts & Media BDP is designed to guide students toward careers in this dynamic field by providing a unique mix of courses from fine arts, radio-television-film, computer sciences, humanities, and engineering. The BDP provides a framework for students to explore and create work that pushes the boundaries of traditional disciplines and media.

Note: As a secondary faculty member with Dr. Dhiraj Murthy, I mentored one undergraduate student in research data collection, analysis, and presentation, focusing on a computational comparative study of Instagram and TikTok vape brand posts. Through tasks such as building bibliographies, creating research posters, and developing digital visualizations, the student gained valuable skills in social media analysis and natural language processing, which directly supported their growth and eventual career as a data scientist.

Yonsei University (Teaching Assistant, 2021-2022)

  • Teaching Assistant

    Deep Learning using Python - Basic & Advanced Course (2021-2022): This course, led by the Center for Digital Social Science at Yonsei University, covers the fundamentals of deep learning, including neural network mechanisms, feedforward networks for regression and classification, recurrent neural networks, and an introduction to convolutional neural networks (CNNs). Advanced topics include deeper CNN models with pre-training and transfer learning, LSTMs and seq2seq models, as well as Transformer and BERT architectures. Students are expected to have basic proficiency in Python programming.