Entropy-Bounded Computational Geometry Made Easier and Sensitive to Sortedness
David Eppstein, Michael T. Goodrich, Abraham M. Illickan, Claire A. To
The 37th Canadian Conference on Computational Geometry (CCCG 2025)
We study instance optimality–algorithms that achieve the performance of the best correct algorithm on every input, with respect to a measure. By leveraging input structure and sortedness, we design simple algorithms for classic computational geometry problems, including maxima set, convex hull, and visibility, and prove new bounds under a complexity measure sensitive to both properties.
Paper | Poster | Slides (Complete)
Investigating the Capabilities of Generative AI in Solving Data Structures, Algorithms, and Computability Problems
Nero Li, Shahar Broner, Yubin Kim, Katrina Mizuo, Elijah Sauder, Claire To, Albert Wang, Ofek Gila, Michael Shindler
The 56th Technical Symposium on Computer Science Education (SIGCSE TS 2025)
Our study evaluates the ability of generative AI models to solve advanced problems in data structures, algorithms, and computability. By testing 165 free-response questions across 16 theoretical computer science topics, we analyze the strengths and limitations of these models, highlighting their potential for education and automated tutoring.
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Common Strategy Patterns of Persuasion in a Mission Critical and Time Sensitive Task
Claire To, Setareh Nasihati Gilani, David Traum
The 27th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2023 - MariLogue)
Our research examines persuasion strategies in high-stakes, time-sensitive disaster relief interactions, analyzing how dialogue structure, speech acts, and urgency impact outcomes. By identifying patterns in communication, we explore how situational factors influence decision-making and persuasion effectiveness in crisis scenarios.
Paper | Poster | Slides