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Jun Yang

Knut Schmidt Nielsen Distinguished Professor of Computer Science
Computer Science
Box 90129, Durham, NC 27708-0129
D308 LSRC, Durham, NC 27708

Overview


Jun's primary research interest lies in the area of databases and data-intensive computing. One of his current passions is computational journalism, the idea of leveraging computing to help preserve and advance journalism, especially in the public interest.

For Jun's publications, please see the publications page of his website. (The "Selected Publications" section below is NOT actively curated by himself.)

Current Appointments & Affiliations


Knut Schmidt Nielsen Distinguished Professor of Computer Science · 2025 - Present Computer Science, Trinity College of Arts & Sciences
Professor of Computer Science · 2014 - Present Computer Science, Trinity College of Arts & Sciences
Associate of the Duke Initiative for Science & Society · 2020 - Present Duke Science & Society, University Initiatives & Academic Support Units

In the News


Published January 21, 2022
Computational Science Is For Everyone
Published April 23, 2020
Five New Bass Professors Named for Excellence in Teaching and Research
Published September 29, 2016
Fact-Checking Senate Campaign Ads Just Got Easier

View All News

Recent Publications


CrypQ: A Database Benchmark Based on Dynamic, Ever-Evolving Ethereum Data

Conference Lecture Notes in Computer Science · January 1, 2026 Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of the database systems. Those that use synthetic data gen ... Full text Cite

PAR2QO: Parametric Penalty-Aware Robust Query Optimization

Conference Proceedings of the VLDB Endowment · January 1, 2025 Parametric Query Optimization (PQO) is an important problem in database systems, yet existing approaches suffer from high training costs, sensitivity to estimation errors, and vulnerability to severe performance regressions. This paper introduces PAR2QO (P ... Full text Cite

Hint-QPT: Hints for Robust Query Performance Tuning

Conference Proceedings of the VLDB Endowment · January 1, 2025 Query optimizers rely heavily on selectivity estimates to choose efficient execution plans, but inaccuracies in these estimates often result in poor query performance. We introduce Hint-QPT (Hints for Robust Query Performance Tuning), an interactive tool d ... Full text Cite
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Recent Grants


III: Medium: Responsive Optimization for Algorithmic Decision Systems

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2024 - 2027

III: Medium: Ask the Experts: Generating Question-Answer Pairs for Addressing Information Deficits about Vaccines

ResearchCo-Principal Investigator · Awarded by National Science Foundation · 2022 - 2026

IIS: Small: Helping Novices Learn and Debug Relational Queries

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2020 - 2024

View All Grants

Education, Training & Certifications


Stanford University · 2001 Ph.D.
University of California, Berkeley · 1995 B.A.

External Links


Personal Website