Lena Hartmann
Research Scientist, Networked Intelligence
Designs graph retrieval layers and provenance-rich interfaces for scientific search.
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Zurich, CH. Institution profiles help you understand where researchers, outputs, topic strength and operational trust gather in one place.
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Zurich, CH
Researchers
Research Scientist, Networked Intelligence
Designs graph retrieval layers and provenance-rich interfaces for scientific search.
Open to collaboratePI, Machine Intelligence Lab
Builds graph-native systems for research evaluation and discovery.
Open to collaborateResearch Software Engineer
Links papers, repositories and datasets with reproducible provenance infrastructure.
Open to collaborateOutputs
We trace dataset reuse across scholarly graphs to improve auditability, discovery and replication planning.
We precompute neighborhoods over works, authors, communities and institutions to speed exploratory search.
This work formalizes a claim workflow that separates platform users, researcher profiles and author entities, improving identity resolution without degrading product usability.
We align code repositories, datasets and papers with provenance-aware linking that survives version churn and renamed assets.
We propose ledger-based moderation records that improve accountability, appeals and policy learning in research products.
We show that persistent graph neighborhoods and operator-facing retrieval memory improve literature navigation across papers, people and opportunities.
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.
We evaluate rubric design for structured reviews, moderation queues and reviewer calibration in technical communities.
We compare dense and sparse retrieval strategies for scholarly search when operators need interpretable reasons and controllable ranking.
We extract actionable collaboration intent from follows, saves, reviews and topic overlap without collapsing trust into a single opaque metric.