Prof. Chloe Ki recognized among the World's Top 2% Scientists for 2026 by Stanford University and Elsevier

[Eight Years of Dedication, a Moment of Recognition] 

I’m honoured to be included in the the World’s Top 2% Scientists 2026, based on single-year citation impact. 🏆

Looking back on eight years in research, what stands out most is not the number of papers published, but the people, conversations, and questions that have shaped the journey. Research has taught me that meaningful work rarely happens in isolation; It grows through shared curiosity, different perspectives, constructive challenges, and a willingness to keep learning.

To my collaborators, mentors, and students: Thank you for challenging my thinking, sharing your insights, and helping turn early ideas into research that we could develop together. I’m equally grateful to the researchers who have read, discussed, and cited our work, allowing our ideas to become part of a wider academic conversation.

Looking ahead, I’m excited to continue exploring how AI is reshaping fashion retail, services, and marketing, particularly how consumers respond to the growing roles of agentic AI and physical AI in the fashion industry. Beyond advancing academic understanding, I hope our research can offer meaningful, practical insights to help fashion businesses navigate this evolving landscape.

And perhaps there is an interesting contrast here: While much of my research examines how AI can transform the way we work, shop, and interact, the research journey itself remains profoundly human. Behind every paper are countless hours of hard work, thoughtful discussions, exchanged ideas, constructive disagreements, and the collective effort that rarely appears in the final publication. Once again, I’m truly grateful to everyone who has been part of this journey over the past eight years.

View the full list and data source at:

Ioannidis, John P.A. (2026), “August 2026 data-update for "Updated science-wide author databases of standardized citation indicators"”, Elsevier Data Repository, V9, https://doi.org/10.17632/btchxktzyw.9