EECS Special Seminar: Tijana Zrnic, "AI-Assisted Approaches to Data Collection and Inference"

Abstract:
Recent breakthroughs in AI offer tremendous potential to reduce the costs of data collection. For example, there is a growing interest in leveraging large language models (LLMs) as efficient substitutes for human judgment in tasks such as model evaluation and survey research. However, AI systems are not without flaws—generative language models often lack factual accuracy, and predictive models remain vulnerable to subtle perturbations. These issues are particularly concerning when critical decisions, such as scientific discoveries or policy choices, rely on AI-generated outputs. In this talk, I will present recent and ongoing work on AI-assisted approaches to data collection and inference. Rather than treating AI as a replacement for data collection, our methods leverage AI to strategically guide data collection and improve the power of subsequent inferences, all the while retaining provable validity guarantees. I will demonstrate the benefits of this methodology through examples from computational social science and more.
 

Bio:
Tijana Zrnic is a Ram and Vijay Shriram Postdoctoral Fellow at Stanford University, affiliated with Stanford Data Science and the Department of Statistics. Tijana obtained her PhD in Electrical Engineering and Computer Sciences at UC Berkeley and a BEng in Electrical and Computer Engineering at the University of Novi Sad in Serbia. Her research establishes foundations to ensure data-driven technologies have a positive impact; she has worked on topics such as AI-assisted statistical inference, performative prediction, and mitigating selection bias.