Scholar
Elena L. Glassman
Google Scholar ID: C_r8d0AAAAAJ
Harvard University
Human-computer interaction
human-AI interaction
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Citations & Impact
All-time
Citations
3,370
H-index
28
i10-index
42
Publications
20
Co-authors
92
list available
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Publications
10 items
MagicCopy: Bring my data along with me beyond boundaries of apps
2026
Cited
0
How Notations Evolve: A Historical Analysis with Implications for Supporting User-Defined Abstractions
2026
Cited
0
Interface Design to Support Legal Reading and Writing: Insights from Interviews with Legal Experts
2025
Cited
0
Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop
2025
Cited
0
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
2025
Cited
0
The Impact of Revealing Large Language Model Stochasticity on Trust, Reliability, and Anthropomorphization
2025
Cited
0
CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writing
2025
Cited
0
Supporting Co-Adaptive Machine Teaching through Human Concept Learning and Cognitive Theories
arXiv.org · 2024
Cited
3
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Resume (English only)
Academic Achievements
Published multiple papers at top-tier venues including CHI, UIST, DIS, and ICML
Two CHI'25 papers awarded Best Paper: 'Supporting Co-Adaptive Machine Teaching...' and 'Creative Writers’ Attitudes on Writing as Training Data...'
Multiple CHI'24 papers received Honorable Mentions, including works on LLM sensemaking and ChainForge
CHI'24 paper 'DynaVis' received Best Paper Award
Developed ChainForge (chainforge.ai), an open-source visual toolkit for prompt engineering and LLM hypothesis testing
Proposed novel interaction techniques like AI-resilient text rendering, with pre-prints and open-source implementations
Background
Faculty at the John A. Paulson School of Engineering & Applied Sciences (SEAS), Harvard University
Leads The Variation Lab, focusing on augmenting human intelligence with variation
Research centers on AI-resilient interfaces that help users cope with AI errors, inappropriate outputs, or misaligned suggestions
Targets open-ended, context- and preference-heavy tasks such as ideation, search, sensemaking, and large-scale reading/writing of text and code
Derives design principles from cognitive science, even when they contradict conventional usability guidelines
Cultivates a vibrant, supportive research community including undergraduates, master's students, PhDs, postdocs, and collaborators
Co-authors
92 total
Priyan Vaithilingam
Apple
Tianyi Zhang
Assistant Professor of Computer Science, Purdue University
Co-author 3
Bjoern Hartmann
Associate Professor of EECS, University of California, Berkeley
Ian Arawjo
Université de Montréal
Co-author 6
Andrew Head
Assistant Professor @ University of Pennsylvania
Gustavo Soares
Researcher, Microsoft
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