Intelligent Systems to Support Large-Scale Collective Creative Idea Generation. - Pao Siangliulue (Creativity & Cognition, 2015)

Providing Timely Examples Improves the Quantity and Quality of Generated Ideas. - Pao Siangliulue; Joel Chan; Krzysztof Z. Gajos; Steven P. Dow (Creativity & Cognition, 2015)

Toward Collaborative Ideation at Scale: Leveraging Ideas from Others to Generate More Creative and Diverse Ideas. - Pao Siangliulue; Kenneth C. Arnold; Krzysztof Z. Gajos; Steven P. Dow (CSCW, 2015)

Supporting Collaborative Innovation at Scale. - Pao Siangliulue (UIST (Adjunct Volume), 2015)

Large-Scale Collaborative Innovation: Challenges, Visions and Approaches. - Pao Siangliulue; Joel Chan; Kenneth C. Arnold; Bernd Huber; Steven P. Dow; Krzysztof Z. Gajos (AAAI Spring Symposia, 2016)

IdeaHound: Self-sustainable Idea Generation in Creative Online Communities. - Pao Siangliulue; Joel Chan; Bernd Huber; Steven Dow; Krzysztof Z. Gajos (CSCW Companion, 2016)

IdeaHound: Improving Large-scale Collaborative Ideation with Crowd-Powered Real-time Semantic Modeling. - Pao Siangliulue; Joel Chan; Steven P. Dow; Krzysztof Z. Gajos (UIST, 2016)

Semantically Far Inspirations Considered Harmful?: Accounting for Cognitive States in Collaborative Ideation. - Joel Chan; Pao Siangliulue; Denisa Qori McDonald; Ruixue Liu; Reza Moradinezhad; Safa Aman; Erin Treacy Solovey; Krzysztof Z. Gajos; Steven P. Dow (Creativity & Cognition, 2017)

Critter: Augmenting Creative Work with Dynamic Checklists, Automated Quality Assurance, and Contextual Reviewer Feedback. - Aditya Bharadwaj; Pao Siangliulue; Adam Marcus 002; Kurt Luther (CHI, 2019)

MixTAPE: Mixed-initiative Team Action Plan Creation Through Semi-structured Notes, Automatic Task Generation, and Task Classification. - Sajjadur Rahman; Pao Siangliulue; Adam Marcus 002 (Proc. ACM Hum. Comput. Interact., 2020)

Ask Me or Tell Me? Enhancing the Effectiveness of Crowdsourced Design Feedback. - Fritz Lekschas; Spyridon Ampanavos; Pao Siangliulue; Hanspeter Pfister; Krzysztof Z. Gajos (CHI, 2021)

The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces. - Kyle Lo; Joseph Chee Chang; Andrew Head; Jonathan Bragg; Amy X. Zhang; Cassidy Trier; Chloe Anastasiades; Tal August; Russell Authur; Danielle Bragg; Erin Bransom; Isabel Cachola; et al. (CoRR, 2023)

Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks. - Zejiang Shen 001; Tal August; Pao Siangliulue; Kyle Lo; Jonathan Bragg; Jeff Hammerbacher; Doug Downey; Joseph Chee Chang; David A. Sontag (CoRR, 2023)

A Design Space for Intelligent and Interactive Writing Assistants. - Mina Lee 002; Katy Ilonka Gero; John Joon Young Chung; Simon Buckingham Shum; Vipul Raheja; Hua Shen 005; Subhashini Venugopalan; Thiemo Wambsganss; David Zhou; Emad A. Alghamdi; Tal August; Avinash Bhat; et al. (CHI, 2024)

PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers. - Yoonjoo Lee; Hyeonsu B. Kang; Matt Latzke; Juho Kim 001; Jonathan Bragg; Joseph Chee Chang; Pao Siangliulue (CHI, 2024)

ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models. - Benjamin Newman; Yoonjoo Lee; Aakanksha Naik; Pao Siangliulue; Raymond Fok; Juho Kim 001; Daniel S. Weld; Joseph Chee Chang; Kyle Lo (EMNLP, 2024)

Let's Get to the Point: LLM-Supported Planning, Drafting, and Revising of Research-Paper Blog Posts. - Marissa Radensky; Daniel S. Weld; Joseph Chee Chang; Pao Siangliulue; Jonathan Bragg (CoRR, 2024)

Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination. - Marissa Radensky; Simra Shahid; Raymond Fok; Pao Siangliulue; Tom Hope; Daniel S. Weld (CoRR, 2024)

Cocoa: Co-Planning and Co-Execution with AI Agents. - K. J. Kevin Feng; Kevin Pu; Matt Latzke; Tal August; Pao Siangliulue; Jonathan Bragg; Daniel S. Weld; Amy X. Zhang; Joseph Chee Chang (CoRR, 2024)

The Semantic Reader Project. - Kyle Lo; Joseph Chee Chang; Andrew Head; Jonathan Bragg; Amy X. Zhang; Cassidy Trier; Chloe Anastasiades; Tal August; Russell Authur; Danielle Bragg; Erin Bransom; Isabel Cachola; et al. (Commun. ACM, 2024)

CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation. - Peter Jansen 001; Oyvind Tafjord; Marissa Radensky; Pao Siangliulue; Tom Hope; Bhavana Dalvi Mishra; Bodhisattwa Prasad Majumder; Daniel S. Weld; Peter Clark (ACL (Findings), 2025)

IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback. - Kevin Pu; K. J. Kevin Feng; Tovi Grossman; Tom Hope; Bhavana Dalvi Mishra; Matt Latzke; Jonathan Bragg; Joseph Chee Chang; Pao Siangliulue (CHI, 2025)

Facets, Taxonomies, and Syntheses: Navigating Structured Representations in LLM-Assisted Literature Review. - Raymond Fok; Joseph Chee Chang; Marissa Radensky; Pao Siangliulue; Jonathan Bragg; Amy X. Zhang; Daniel S. Weld (CoRR, 2025)

Literature-Grounded Novelty Assessment of Scientific Ideas. - Simra Shahid; Marissa Radensky; Raymond Fok; Pao Siangliulue; Daniel S. Weld; Tom Hope (CoRR, 2025)