This seminar will introduce the latest empirical methods in generative AI and causal inference using text, empowering doctoral students to explore and investigate novel and high-impact business and computational social science research. The first half of the seminar will concentrate on the techniques, potential applications, and economics of generative AI and large language models. Topics covered will include Transformer, BERT, the GPT family, VAEs, GANs, Diffusion Model, Human-AI collaboration, etc. The second half will focus on causal inference techniques using text as controls, mediators, and treatment. Students will be required to propose a new idea based on the seminar's content. Previous iterations of the seminar have included Interpretable ML and Bias in ML (2017), Generative AI (2019), and Neural Language Models and Economics of AI (2020). The seminar is engineered to foster innovative ideas for students across a diverse range of academic disciplines.

Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.