PeopleofSuperintelligence

Researcher

Yue Pan

Yue Pan is an AI researcher whose IBM work connected machine learning with semantic-web, search and clinical systems. He coauthored a 2004 model for finding topical named entities, then contributed to SOR and Semplore for ontology storage and Web-of-Data search. His work also covered semantic queries of clinical documents, MapReduce-based detection of adverse drug events and active linked data for the Web of Things. In 2013, he was a member of IBM's Watson team when it received AAAI's Feigenbaum Prize for real-time factual question-answering.

Badges

Timeline

  1. 2004

    Focused named entity recognition

    In 2004, he coauthors a machine-learning approach to focused named entity recognition while affiliated with IBM. The model selects topical entities in documents for uses such as summarization and search ranking.

  2. 2007

    SOR for ontology search

    In 2007, he coauthors SOR (Scalable Ontology Repository), an IBM system for ontology storage, reasoning and search. It stores ontologies in relational databases, performs inference and supports SPARQL queries.

  3. 2008

    Semantic access for objects

    In 2008, he coauthors a system that maps object-oriented models to relational schemas and OWL ontologies. The mapping lets applications store objects in relational databases and access them through the Semantic Web.

  4. 2009

    Semplore searches Web data

    In 2009, he coauthors Semplore, an information-retrieval system for searching the Web of Data. It combines keyword search with structured queries and supports faceted search over text and structured data.

  5. 2009

    iSMART queries clinical records

    In 2009, he coauthors iSMART, a prototype for ontology-based queries of Clinical Document Architecture records. It extracts RDF triples from clinical documents, applies SNOMED CT ontology reasoning and uses an RDF query engine for retrieval.

  6. 2010

    MapReduce drug-safety signals

    In 2010, he coauthors a MapReduce-based algorithm for detecting adverse drug event signals in FDA reports. The study tests it with the Proportional Reporting Ratio method.

  7. 2012

    Active linked data

    In 2012, he coauthors an active-linked-data framework for the Web of Things. It extends linked data with models for data, tasks, context, computational organization, knowledge, presentation and interaction to support human-to-human and human-to-machine collaboration.

  8. 2013

    The Feigenbaum Prize

    In 2013, he is a member of IBM's Watson team when it receives the Feigenbaum Prize for combining symbolic knowledge representation, natural language understanding and statistical machine learning for real-time factual question-answering.

  1. 23% of people shaping AI who left a university for a company later returned to a universityIncludes IBM. We estimate that 468 of the 2,011 people shaping AI who left a university for a company (23%) later returned to a university after a median of 5 years, and 163 of 829 (20%) made the reverse round trip.
  2. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes IBM. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.
  3. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes IBM. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  4. People shaping AI work at a median of 3 organizations over a career: 3 for university starters and institute and lab starters, versus 2 for company startersIncludes IBM. Across 6,853 people shaping AI with a dated work role, we estimate a median of 3 parent organizations over a career (mean 3.6), comparing role groups, starting sectors and starting decades from the 1950s to 2020–2026.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Focused named entity recognition using machine learning - IBM Research d3ccyth396mz21.cloudfront.net. Checked 22 October 2026.
  2. SOR: A practical system for ontology storage, reasoning and search for VLDB 2007 - IBM Research research.ibm.com. Checked 22 October 2026.
  3. A system to enable relational persistence and Semantic Web style access simultaneously for objects for ISWC 2008 - IBM Research research.ibm.com. Checked 22 October 2026.
  4. Semplore: A scalable IR approach to search the Web of Data for Journal of Web Semantics - IBM Research research.ibm.com. Checked 22 October 2026.
  5. iSMART: Ontology-based Semantic Query of CDA Documents - PMC pmc.ncbi.nlm.nih.gov. Checked 22 October 2026.
  6. High-Performance Signal Detection for Adverse Drug Events using MapReduce Paradigm - PMC pmc.ncbi.nlm.nih.gov. Checked 22 October 2026.
  7. Active linked data for human centric semantic web of things for GreenCom-iThings-CPSCom 2012 - IBM Research research.ibm.com. Checked 22 October 2026.
  8. The AAAI Feigenbaum Prize - AAAI aaai.org. Checked 22 October 2026.