Hi! I’m a second-year computer science PhD student at the University of Washington advised by Dan Weld and Zoran Popović. My research interests are in human-AI interaction and explainable AI. More specifically, I am interested in how methods of transparency can be used to help users of intelligent systems understand when and when not to trust those systems.

Email: radensky at cs dot washington dot edu

CV | LinkedIn | Semantic Scholar | Google Scholar

Currently

• Investigating the role of local and global explanations in recommender systems

Publications

• PUMICE: A Multi-Modal Agent that Learns Concepts and Conditionals from Natural Language and Demonstrations
Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell and Brad A. Myers (UIST 2019)

• The Story in the Notebook: Exploratory Data Science using a Literate Programming Tool
Mary Beth Kery, Marissa Radensky, Mahima Arya, Bonnie E. John, and Brad A. Myers (CHI 2018)

Posters and Workshops

• Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations
Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell and Brad A. Myers (IPA 2020 Workshop)

• A Multi-Modal Approach to Concept Learning in Task Oriented Conversational Agents
Toby Jia-Jun Li, Marissa Radensky, Tom M. Mitchell and Brad A. Myers (CHI 2019 Workshop)

• How End Users Express Conditionals in Programming by Demonstration for Mobile Apps
Marissa Radensky, Toby Jia-Jun Li, and Brad A. Myers (VL/HCC 2018 Poster)