Biography: Emine Yilmaz is a Professor and an ELLIS Fellow at University College London, Department of Computer Science. At UCL she is one of the faculty members affiliated with the UCL Centre for Artificial Intelligence, where she leads the Web Intelligence Group. She also works as an Amazon Scholar for Amazon, where she works with the Alexa team and is a co-founder of Humanloop, a UCL spinout company.

Emine’s research interests lie in the fields of information retrieval and natural language processing. Her research in these areas is mainly guided by principles from machine learning, statistics and information theory. She has received several awards for her research, including the Karen Sparck Jones Award, a Google Faculty Research Award and a Bloomberg Data Science Research Award.

Emine has served in various senior roles, including co-editor-in-chief for the Information Retrieval Journal, a member of the editorial board for the AI Journal, an elected member of the executive committee for ACM SIGIR, and PC Chair for various conferences. She currently serves as an Associate Editor for ACM Transactions on Information Systems and is a member of the Steering Committee for ACM SIGIR Asia-Pacific.

MultimodalAI'25 Keynote Title: Using Large Language Models for Evaluation: Opportunities and Limitations.

MultimodalAI'25 Keynote Abstract: Large Language Models (LLMs) have shown significant promise as tools for automated evaluation across diverse domains. While using LLMs for evaluation come with significant advantages potentially alleviating the reliance on costly and subjective human assessments, the adoption of LLM-based evaluation is not without challenges. In this talk we discuss about the transformative potential and the inherent constraints of using LLMs for evaluation tasks. In particular, we describe some of the challenges that come with LLM-based evaluation, such as biases and variability in judgment. We further discuss how LLMs can augment traditional evaluation practices while acknowledging the need for cautious and informed integration.