Shriram Krishnamurthi Source Confirmed
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor
John Brown University
faculty
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Biography and Research Information
OverviewAI-generated summary
Shriram Krishnamurthi is a Professor at John Brown University whose work encompasses both theoretical foundations and practical applications of computer science. His research spans diverse areas, from software engineering to educational innovations in computer science. Recent work explores student use of large language models for program generation, as well as a grounded conceptual model for ownership types in Rust. Krishnamurthi has also investigated misconceptions in the understanding of Linear Temporal Logic (LTL). Furthermore, he is actively involved in integrating computer science into secondary education, including curriculum development for K-12 and integrated data science programs. His broad interests include teaching and learning programming, software testing and debugging, logic and type systems, and parallel computing.
Metrics
- h-index: 51
- Publications: 340
- Citations: 8,138
Selected Publications
- Accepted Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL (2025) DOI
- Reproduction Package for Article `Identifying and Correcting Programming Language Behavior Misconceptions' (2024) DOI
- Privacy-Respecting Type Error Telemetry at Scale (2024) DOI
- Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale (2023) DOI
- Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale (2023) DOI
- Artifact: Privacy-Respecting Type Error Telemetry at Scale (2023) DOI
- Artifact: Privacy-Respecting Type Error Telemetry at Scale (2023) DOI
- Conceptual Mutation Testing for Student Programming Misconceptions (2023) DOI
- Artifact for "A Core Calculus for Documents" (2023) DOI
- Artifact for "A Core Calculus for Documents" (2023) DOI
- What Happens When Students Switch (Functional) Languages (Experience Report) (2023) DOI
- Applying Cognitive Principles to Model-Finding Output: The Positive Value of Negative Information (artifact) (2022) DOI
- Accepted Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL (2022) DOI
- Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL (2022) DOI
- Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL (2022) DOI
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