World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
11186
World Ranking
8635
National Ranking
3701

Overview

Philip J. Guo is affiliated with the University of California, San Diego in the United States. Their research spans multiple subfields within computer science, with a focus on areas such as computer vision and pattern recognition, information systems, electrical and electronic engineering, computer science applications, and artificial intelligence.

The primary topics Philip J. Guo investigates include data visualization and analytics, big data and business intelligence, ethics and social impacts of AI, online learning and analytics, machine learning and data classification, spreadsheets and end-user computing, and multimedia communication and technology.

Their recent scholarly output includes publications in various reputable venues, demonstrating a breadth of collaborative and interdisciplinary research. Notable papers by Philip J. Guo comprise:

  • "Six Opportunities for Scientists and Engineers to Learn Programming Using AI Tools Such as ChatGPT," 2023, Computing in Science & Engineering

Other recent articles featuring their work or close collaboration have appeared in venues such as:

  • Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
  • Designing Interactive Systems Conference
  • Proceedings of the ACM on Programming Languages
  • arXiv (Cornell University)

Frequent coauthors include:

  • Sean Kross
  • Ian Drosos
  • Sam Lau
  • Deborah Nolan
  • Joseph E. Gonzalez

Philip J. Guo's work reflects a significant engagement with computer science topics as well as interdisciplinary themes connecting to social sciences, as indicated by their publication record spanning these fields. Their collaborative network and publication venues suggest active participation in computer science education research and computational methodologies.

Best Publications

  • How video production affects student engagement: an empirical study of MOOC videos

    Philip J. Guo;Juho Kim;Rob Rubin

  • The Daikon system for dynamic detection of likely invariants

    Michael D. Ernst;Jeff H. Perkins;Philip J. Guo;Stephen McCamant

  • Automatic creation of SQL Injection and cross-site scripting attacks

    Adam Kieyzun;Philip J. Guo;Karthick Jayaraman;Michael D. Ernst

  • Online python tutor: embeddable web-based program visualization for cs education

    Philip J. Guo

  • Two studies of opportunistic programming: interleaving web foraging, learning, and writing code

    Joel Brandt;Philip J. Guo;Joel Lewenstein;Mira Dontcheva

  • Understanding in-video dropouts and interaction peaks inonline lecture videos

    Juho Kim;Philip J. Guo;Daniel T. Seaton;Piotr Mitros

  • Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows

    Philip J. Guo;Thomas Zimmermann;Nachiappan Nagappan;Brendan Murphy

  • HAMPI: a solver for string constraints

    Adam Kiezun;Vijay Ganesh;Philip J. Guo;Pieter Hooimeijer

  • OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale

    Elena L. Glassman;Jeremy Scott;Rishabh Singh;Philip J. Guo

  • Characterizing and predicting which bugs get reopened

    Thomas Zimmermann;Nachiappan Nagappan;Philip J. Guo;Brendan Murphy

  • Data-driven interaction techniques for improving navigation of educational videos

    Juho Kim;Philip J. Guo;Carrie J. Cai;Shang-Wen (Daniel) Li

  • "Not my bug!" and other reasons for software bug report reassignments

    Philip J. Guo;Thomas Zimmermann;Nachiappan Nagappan;Brendan Murphy

  • Crowdsourcing step-by-step information extraction to enhance existing how-to videos

    Juho Kim;Phu Tran Nguyen;Sarah Weir;Philip J. Guo

  • Inference and enforcement of data structure consistency specifications

    Brian Demsky;Michael D. Ernst;Philip J. Guo;Stephen McCamant

  • Paradise unplugged: identifying barriers for female participation on stack overflow

    Denae Ford;Justin Smith;Philip J. Guo;Chris Parnin

  • Non-Native English Speakers Learning Computer Programming: Barriers, Desires, and Design Opportunities

    Philip J. Guo

  • Proactive wrangling: mixed-initiative end-user programming of data transformation scripts

    Philip J. Guo;Sean Kandel;Joseph M. Hellerstein;Jeffrey Heer

  • Opportunistic programming: how rapid ideation and prototyping occur in practice

    Joel Brandt;Philip J. Guo;Joel Lewenstein;Scott R. Klemmer

  • Codeopticon: Real-Time, One-To-Many Human Tutoring for Computer Programming

    Philip J. Guo

  • Wrex: A Unified Programming-by-Example Interaction for Synthesizing Readable Code for Data Scientists

    Ian Drosos;Titus Barik;Philip J. Guo;Robert DeLine

  • Crowdsourcing step-by-step information extraction to enhance existing how-to videos

    Phu Tran Nguyen;Sarah Weir;Philip J. Guo;Robert C. Miller

Frequent Co-Authors

Michael D. Ernst
Michael D. Ernst University of Washington
Scott R. Klemmer
Scott R. Klemmer University of California, San Diego
Krzysztof Z. Gajos
Krzysztof Z. Gajos Harvard University
Rishabh Singh
Rishabh Singh Google (United States)
Dawson Engler
Dawson Engler Stanford University
Thomas Zimmermann
Thomas Zimmermann Microsoft (United States)
Nachiappan Nagappan
Nachiappan Nagappan Facebook (United States)
Brendan Murphy
Brendan Murphy Microsoft (United States)
James D. Hollan
James D. Hollan University of California, San Diego

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