Lena Hartmann
Research Scientist, Networked Intelligence
Designs graph retrieval layers and provenance-rich interfaces for scientific search.
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ETH Zurich
Zurich, CH. Institution profiles help you understand where researchers, outputs, topic strength and operational trust gather in one place.
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18labs
142researchers
10works
4topics
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Drag to move the map, use wheel or controls to zoom.Selected institution: ETH Zurich
institutionsignal 104 adjacent
ETH Zurich
Zurich, CH
Researchers
3 connected scholar(s)
Outputs
10 representative work(s)
Dataset Provenance Across Paper Graphs: Linking Results, Repositories and Reuse
We trace dataset reuse across scholarly graphs to improve auditability, discovery and replication planning.
Graph Neighborhoods for Scholarly Search and Topic Expansion
We precompute neighborhoods over works, authors, communities and institutions to speed exploratory search.
Probabilistic Claim Resolution for Author Entities in Scholarly Graphs
This work formalizes a claim workflow that separates platform users, researcher profiles and author entities, improving identity resolution without degrading product usability.
Reproducible Paper-to-Code Linking for Research Infrastructure
We align code repositories, datasets and papers with provenance-aware linking that survives version churn and renamed assets.
Moderation Ledger Design for Research Platforms
We propose ledger-based moderation records that improve accountability, appeals and policy learning in research products.
Graph Memory for Literature Agents: Persistent Neighborhoods for Scientific Search
We show that persistent graph neighborhoods and operator-facing retrieval memory improve literature navigation across papers, people and opportunities.
A Signal Layer for Science: Structured Reviews, Graph Context and Faster Research Triage
We present a graph-native product architecture for scholarly sensemaking where work pages, topic maps and researcher trust signals reduce time-to-understanding for new literature.
Calibrated Review Rubrics for AI Science Communities
We evaluate rubric design for structured reviews, moderation queues and reviewer calibration in technical communities.
Sparse Embeddings for Scholarly Discovery with Human-Readable Signals
We compare dense and sparse retrieval strategies for scholarly search when operators need interpretable reasons and controllable ranking.
Collaboration Intent Signals in Research Labs
We extract actionable collaboration intent from follows, saves, reviews and topic overlap without collapsing trust into a single opaque metric.