Zihao Li has been a TTLF Fellow at Stanford Law School since 2023. He is also a Lecturer (Assistant Professor) at CREATe Centre, School of Law, University of Glasgow, UK. Meanwhile, he has been invited as a Guest Lecturer to Trinity College Dublin, Ireland. Zihao is qualified in both Computer Science (B.Eng.) and Law (LL.B., LL.M., Ph.D.). With his interdisciplinary background, his research interests concentrate on the intersection of law, data and information technology. Recently, his research mainly includes data protection law, AI regulation, algorithmic pricing, blockchain and law, and Internet and intellectual property. Zihao’s research has been published in top-tier interdisciplinary academic journals, conference proceedings and books, including Nature Machine Intelligence, Computer Law and Security Review, IEEE Communications Magazine, IEEE International Conference on Communications, and European Data Protection Law Review. His research has been cited around the world. He has also been invited to speak at several prestigious universities, including the University of Cambridge, UK, Imperial College London, UK, and King’s College London (KCL), UK. Zihao has been awarded the Modern Law Review (MLR) Scholarship operated by the LSE. Additionally, Zihao is a co-founder of the Scottish Law and Innovation Network (SCOTLIN), and the founding president of the Intellectual Property Society at the University of Glasgow.

At Stanford Law School, Zihao principally investigates the regulation of Generative AI and Large Language Models, as well as data privacy issues in algorithmic pricing within the EU and US. Taking a comparative approach, his projects aim to explore how the respective AI regulations and data privacy laws of the EU and US should address the issues posed by emerging AI and machine learning technologies. The project particularly analyzes the new EU regulatory paradigm, including the AI Act, Digital Service Act (DSA) and Digital Market Act (DMA), as supplementary mechanisms to the EU data protection law, in order to draw lessons for US AI regulation and data privacy law, and vice versa.