World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
19427
World Ranking
8141
National Ranking
3485

Overview

Matthew Richardson is affiliated with Microsoft in the United States and conducts research primarily within the field of Computer Science. Their work spans several specialized areas, including Artificial Intelligence, Psychiatry and Mental Health, Clinical Psychology, Social Psychology, and Information Systems.

The main topics covered in Richardson's research include:

  • Schizophrenia research and treatment
  • Mindfulness and Compassion Interventions
  • Mental Health Treatment and Access
  • Bayesian Modeling and Causal Inference
  • Data Mining Algorithms and Applications
  • Logic, Reasoning, and Knowledge
  • Topic Modeling

Richardson has contributed to multiple publication venues, with a total of six papers published in Zenodo (CERN European Organization for Nuclear Research). Other venues include BMC Psychology, Proceedings of the AAAI Conference on Artificial Intelligence, arXiv (Cornell University), and Cambridge University Press eBooks.

Some notable recent papers authored or coauthored by Richardson are:

  • Home practice in mindfulness-based interventions for psychosis groups: a systematic review and qualitative study, 2022, BMC Psychology
  • Counting-MLNs: Learning Relational Structure for Decision Making, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • AmelHap Metadata, 2022, Zenodo (CERN European Organization for Nuclear Research)
  • DataDescriptor sample metadata, 2023, Zenodo (CERN European Organization for Nuclear Research)
  • KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers, 2021, arXiv (Cornell University)

Frequent collaborators in Richardson's research network include:

  • Melanie Parejo
  • Andrea Talenti
  • Alain Vignal
  • Mark Barnett
  • David Wragg

The breadth of Richardson's work demonstrates an integration of computational methods with mental health applications and advanced data modeling techniques. Their contributions in relational structure learning, metadata publication, and mental health intervention research reflect interdisciplinary engagement across computer science and psychology.

Best Publications

  • Markov logic networks

    Matthew Richardson;Pedro Domingos

  • Mining the network value of customers

    Pedro Domingos;Matt Richardson

  • Mining knowledge-sharing sites for viral marketing

    Matthew Richardson;Pedro Domingos

  • Trust management for the semantic web

    Matthew Richardson;Rakesh Agrawal;Pedro Domingos

  • Predicting clicks: estimating the click-through rate for new ads

    Matthew Richardson;Ewa Dominowska;Robert Ragno

  • MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text

    Matthew Richardson;Christopher J.C. Burges;Erin Renshaw

  • The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank

    Matthew Richardson;Pedro Domingos

  • DyNet: The Dynamic Neural Network Toolkit

    Graham Neubig;Chris Dyer;Yoav Goldberg;Austin Matthews

  • The Value of Semantic Parse Labeling for Knowledge Base Question Answering

    Wen-tau Yih;Matthew Richardson;Christopher Meek;Ming-Wei Chang

  • Yes, there is a correlation: - from social networks to personal behavior on the web

    Parag Singla;Matthew Richardson

  • Dynamic client interaction for search

    Matthew R. Richardson;Robert J. Ragno

  • RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

    Bailin Wang;Richard Shin;Xiaodong Liu;Oleksandr Polozov

  • Beyond PageRank: machine learning for static ranking

    Matthew Richardson;Amit Prakash;Eric Brill

  • Systems and methods that enable search engines to present relevant snippets

    Silviu-Petru Cucerzan;Matthew R. Richardson

  • Estimating expected performance of advertisements

    Matthew R. Richardson;Robert J. Ragno;Ewa Dominowska

  • Do Deep Convolutional Nets Really Need to be Deep and Convolutional

    Gregor Urban;Krzysztof J. Geras;Samira Ebrahimi Kahou;Özlem Aslan

  • Unifying Logical and Statistical AI

    Pedro Domingos;Daniel Lowd;Stanley Kok;Aniruddh Nath

  • Using popularity data for ranking

    Matthew R. Richardson;Eric D. Brill;Robert J. Ragno;Robert L. Rounthwaite

  • Structure-Grounded Pretraining for Text-to-SQL

    Xiang Deng;Ahmed Hassan Awadallah;Christopher Meek;Oleksandr Polozov

  • A world-wide distributed system using Java and the Internet

    K.M. Chandy;A. Rifkin;P.A.G. Sivilotti;J. Mandelson

  • The Alchemy System for Statistical Relational AI: User Manual

    Stanley Kok;Parag Singla;Matthew Richardson;Pedro Domingos

Frequent Co-Authors

Pedro Domingos
Pedro Domingos University of Washington
Ryen W. White
Ryen W. White Microsoft (United States)
Eric Brill
Eric Brill Microsoft (United States)
Janet A. Englund
Janet A. Englund Seattle Children's Hospital
Jay Shendure
Jay Shendure University of Washington
Robert L. Rounthwaite
Robert L. Rounthwaite Microsoft (United States)
Trevor Bedford
Trevor Bedford Fred Hutchinson Cancer Research Center
Rich Caruana
Rich Caruana Microsoft (United States)
Abdel-rahman Mohamed
Abdel-rahman Mohamed Facebook (United States)
Helen Y. Chu
Helen Y. Chu University of Washington

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