Hanae Morita
Senior Lecturer
Works on review quality, moderation design and institutional trust systems for online science.
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University College London
London, UK. Institution profiles help you understand where researchers, outputs, topic strength and operational trust gather in one place.
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16labs
119researchers
10works
4topics
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University College London
London, UK
Researchers
3 connected scholar(s)
Outputs
10 representative work(s)
Multimodal Benchmark Curation for Biology LLMs
We curate paper, code and dataset benchmarks for biology-focused language models under provenance and reproducibility constraints.
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.
Verified Expert Routing with Institutional Signals in Research Networks
This paper combines institution verification, topical expertise and trust snapshots to route methodological questions to the right specialists.
Open-Weight Biology Assistants for Dataset-Centric Literature Triage
We study open-weight assistants that combine paper summaries, dataset cards and experiment context for biology teams.
Moderation Ledger Design for Research Platforms
We propose ledger-based moderation records that improve accountability, appeals and policy learning in research products.
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.
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.
Trust-Aware Feed Ranking for Scholarly Collaboration Networks
We study how follow edges, review quality, graph proximity and freshness can be blended into an explainable feed optimized for high-signal research discovery.
Topic-Aware Opportunity Matching for Research Careers
We use graph signals and topical expertise to connect researchers with fellowships, residencies and collaboration openings.