Rate My Professor Kristen Vaccaro

Kristen Vaccaro is an Associate Professor in the Computer Science department at the University of California, San Diego. She earned her PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2020, with a dissertation titled Configuration and Control in Social Media Systems advised by Karrie Karahalios. She also completed nondegree graduate work in Computational Social Science at George Mason University from 2011 to 2013 and a BA in Physics from Reed College in 2009. Vaccaro's research centers on designing the algorithmic experience of machine learning systems to enhance user agency, control, understanding, justice, and policy implications, particularly in social computing applications like news feed curation and content moderation. Her work investigates placebo effects of control settings, contestability in content moderation, and user attitudes toward algorithmic opacity.

Prior to UCSD, Vaccaro was a Research Assistant at the University of Illinois from 2013 to 2020, a Research Intern at Adobe Research in 2015, and a Software Engineer at MITRE Corporation from 2009 to 2013. She has published influential papers at premier venues such as CHI and CSCW, including the Best Paper Award-winning I always assumed that I wasn’t really that close to [her]: Reasoning About Invisible Algorithms in the News Feed (CHI 2015, 1091 citations), The Illusion of Control: Placebo Effects of Control Settings (CHI 2018), Contestability For Content Moderation (CSCW 2021), and “At the End of the Day Facebook Does What It Wants”: How Users Experience Contesting Algorithmic Content Moderation (CSCW 2020). Vaccaro received the inaugural Google Trust & Safety Research Award in 2024 and the 2019 Graduate Student Outstanding Ambassador Award from the University of Illinois. She serves on program committees for conferences like FAccT and DIS, reviews for CHI and CSCW, and holds roles on UCSD CSE's Undergraduate Committee and Executive Committee of Seventh College. Vaccaro teaches courses including Human-Centered AI, Antisocial Computing, and User Experiences of Machine Learning, and mentors PhD students such as Lu Sun and Tianyi Shan.