Ruixuan Sun

Computer Science Ph.D. candidate @ University of Minnesota

GroupLens Research

Department of Computer Science & Engineering

U of Minnesota

Hi! This is Ruixuan(Sophia), a 5th-year Computer Science Ph.D. student working with Dr. Joseph Konstan at University of Minnesota. I earned my Bachalor’s Degree of Computer Science from Georgia Institute of Technology in 2019. After graduation, I’ve worked as a Software Engineer at Yelp for two years before starting my Ph.D. journey at UMN. During my PhD, I have the privilege to intern with Dolby Lab, Amazon, and Google.

My research interests include: Human-centered AI, Social Computing, Recommender Systems, and Applied Machine Learning. In particular, I’m passionate about user interest modeling, behavior understanding, beyond-accuracy evaluation and optimization. I have expertise in mixed-method study design and extensive experience in running large-scale online field experiments powered by A/B tests. In general, I love training models and conducting user evaluation to understand how we can better serve human beings with AI-powered technology!

Please see my CV for more detailed experience.

news

Apr 8, 2026 Our work “Beyond Exposure Diversity: Debiasing News Consumption With Topic-Locality Calibration and Personalized Preview Nudges” has been accepted to SIGIR 2026!
Apr 2, 2026 Our work “Productively Wrong: How Auditing Open Profiles Enhances Interest Awareness and Reflection in Movie Recommenders” has been accepted to UMAP 2026. Looking forward to present it in Gothenburg, Sweden in June!
Feb 1, 2026 Excited to serve as RecSys 26’ Demos and Research & Practice Notes chair. Please consider submitting your work this summer and looking forward to meeting with everyone in Minneapolis this September!
Apr 28, 2025 Excited to present our paper “Multi-Prompting Scenario-based Movie Recommendation with Large Language Models: Real User Case Study” at CHI 2025!
Mar 24, 2025 Excited to present our long paper “Why They Come And Go: A Case Study of Productive Flyby Users and Their Rating Integrity Challenge in Movie Recommenders” at CHIIR 2025!