Ni Lao
Ph.D. Carnegie Mellon University 2012
Facts
7 properties- Instance of
- human
- Occupation
- researcher
- Educated at
- Carnegie Mellon University[3]
- Doctoral advisor
- William W. Cohen[4]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en Ni Lao
🔖 Identifiers · 6
- ORCID iD
- 0000-0002-4034-7784
- OpenAlex ID
- A5087022523as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- Google Knowledge Graph ID
- /g/11lkg8cxql
- Google Scholar author ID
- hxEoGAMAAAAJ
- MR Author ID
- 1045131
- Mathematics Genealogy Project ID
- 205368
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 13
Efficient inference and learning in a large knowledge base,
Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages,
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation,
Learning Relational Features with Backward Random Walks,
Memory Augmented Policy Optimization for Program Synthesis with Generalization,
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells,
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision,
Never-ending learning
+5 more
← Authored by 13
Efficient inference and learning in a large knowledge base,
Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages,
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation,
Learning Relational Features with Backward Random Walks,
Memory Augmented Policy Optimization for Program Synthesis with Generalization,
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells,
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision,
Never-ending learning
+5 more
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