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David Shriver

Research Interests

I work to make machine learning systems safer and more reliable, especially in applications where failures can have serious consequences, such as autonomous vehicles or safety-critical systems. My research develops practical tools and techniques for analyzing, verifying, and fixing neural networks, ensuring failures are caught and remediated before they can have a significant impact.

Education

Ph.D., Computer Science

•
Dec 2022
•
University of Virginia

Advised by: Matt Dwyer and Sebastian Elbaum

Thesis: Increasing the Applicability of Verification Tools for Neural Networks

M.S., Computer Science

•
May 2018
•
University of Nebraska-Lincoln

Advised by: Sebastian Elbaum

Thesis: Assessing the Quality and Stability of Recommender Systems

B.S., Computer Engineering

•
May 2016
•
University of Nebraska-Lincoln

Positions Held

Machine Learning Research Scientist

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Software Engineering Institute (SEI)

January 2023 — February 2026

Leads teams of researchers, engineers, and software developers on projects in the Secure AI Lab on topics related to identifying, understanding, and defending against AI model vulnerabilities.

Research Assistant

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Department of Computer Science, University of Virginia

August 2018 — December 2022

Performed research on techniques for increasing the applicability of state-of-the art formal verification tools for neural networks to improve the safety and correctness of systems with high costs of failure, such as autonomous vehicles.

Research Intern

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Langley Research Center, NASA

June 2021 — August 2021

Proposed and led a short-term project to develop a method for transforming temporal behavioral properties of neural networks to local robustness properties to enable application of a plethora of available tools and investigated the feasibility of this transformation in the context of neural networks trained for aircraft collision avoidance.

Research Assistant

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Department of Computer Science and Engineering, University of Nebraska-Lincoln

March 2014 — July 2018

Developed approaches for assessing the quality and stability of recommender systems.

Publications

Compositional Neural Network Verification via Assume-Guarantee Reasoning



Neural Information Processing Systems (NeurIPS)
—
2025

[Paper] [Code]
Hai Duong, David Shriver, ThanhVu Nguyen, Matthew B. Dwyer. 2025. Compositional Neural Network Verification via Assume-Guarantee Reasoning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS). https://openreview.net/forum?id=WbpXT0WL9S

Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing



International Conference on Learning Representations (ICLR)
—
2025

[Paper] [Code]
Keltin Grimes, Marco Christiani, David Shriver, Marissa Connor. 2025. Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing. In The Thirteenth International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=RzUvkI3p1D

Deeper Notions of Correctness in Image-Based DNNs: Lifting Properties from Pixel to Entities



Foundations of Software Engineering (FSE)
—
2023

[Paper]
Felipe Toledo, David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2023. Deeper Notions of Correctness in Image-Based DNNs: Lifting Properties from Pixel to Entities. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023). Association for Computing Machinery, New York, NY, USA, 2122–2126. https://doi.org/10.1145/3611643.3613079

DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles



IEEE Transactions on Software Engineering (TSE)
—
2023

[Paper] [Artifact]
Meriel von Stein, David Shriver, Sebastian Elbaum. 2023. DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles. In IEEE Transactions on Software Engineering, vol. 49, no. 10, pp. 4496-4509. https://doi.org/10.1109/TSE.2023.3301443

Increasing the Applicability of Verification Tools for Neural Networks



University of Virginia
—
2022

[Paper]
David Shriver. Increasing the Applicability of Verification Tools for Neural Networks. PhD Thesis. University of Virginia. 2022.

Distribution Models for Falsification and Verification of DNNs



International Conference on Automated Software Engineering (ASE)
—
2021

[Paper] [Artifact]
Felipe Toledo, David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2021. Distribution Models for Falsification and Verification of DNNs. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). 317-329. https://doi.org/10.1109/ASE51524.2021.9678590

DNNV: A Framework for Deep Neural Network Verification



Computer Aided Verification
—
2021

[Paper] [Tool] [Video]
David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2021. DNNV: A Framework for Deep Neural Network Verification. In Computer Aided Verification. 137--150. https://doi.org/10.1007/978-3-030-81685-8_6

Reducing DNN Properties to Enable Falsification with Adversarial Attacks



International Conference on Software Engineering (ICSE)
—
2021

[Paper] [Artifact] [Tool] [Video]
David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2021. Reducing DNN Properties to Enable Falsification with Adversarial Attacks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). 275-287. https://doi.org/10.1109/ICSE43902.2021.00036

Systematic Generation of Diverse Benchmarks for DNN Verification



Computer Aided Verification (CAV)
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2020

[Paper]
Dong Xu, David Shriver, Matthew B. Dwyer, Sebastian Elbaum. 2020. Systematic Generation of Diverse Benchmarks for DNN Verification. In Computer Aided Verification - 32nd International Conference, CAV 2020, Los Angeles, CA, USA, July 21-24, 2020, Proceedings, Part I. 97-121. https://doi.org/10.1007/978-3-030-53288-8_5

Poster: Differencing Neural Networks



University of Virginia CS Research Symposium
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2019

[Poster]
David Shriver, Sebastian Elbaum, Matthew B. Dwyer. 2019. Differencing Neural Networks. In the 2019 University of Virginia Computer Science Research Symposium.

Refactoring Neural Networks for Verification



arXiv preprint arXiv:1908.08026
—
2019

[Paper]
David Shriver, Dong Xu, Sebastian Elbaum, Matthew B. Dwyer. 2019. Refactoring Neural Networks for Verification. arXiv:1908.08026. https://arxiv.org/abs/1908.08026

Evaluating Recommender System Stability with Influence-Guided Fuzzing



AAAI Conference on Artificial Intelligence (AAAI)
—
2019

[Paper] [Poster]
David Shriver, Sebastian Elbaum, Matthew B. Dwyer, David S. Rosenblum. 2019. Evaluating Recommender System Stability with Influence-Guided Fuzzing. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. 4934-4942. https://doi.org/10.1609/aaai.v33i01.33014934

Toward the development of richer properties for recommender systems



International Conference on Software Engineering (ICSE): Companion Proceedings
—
2018

[Paper] [Poster]
David Shriver. 2018. Toward the development of richer properties for recommender systems. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings, ICSE 2018, Gothenburg, Sweden, May 27 - June 03, 2018. 173-174. https://doi.acm.org/10.1145/3183440.3195082

Assessing the Quality and Stability of Recommender Systems



University of Nebraska-Lincoln
—
2018

[Paper]
David Shriver. Assessing the Quality and Stability of Recommender Systems. MS Thesis, University of Nebraska-Lincoln, 2018.

At the End of Synthesis: Narrowing Program Candidates



International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track (ICSE NIER)
—
2017

[Paper]
David Shriver, Sebastian G. Elbaum, Kathryn T. Stolee. 2017. At the End of Synthesis: Narrowing Program Candidates. In 39th IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track, ICSE-NIER 2017, Buenos Aires, Argentina, May 20-28, 2017. 19-22. https://doi.org/10.1109/ICSE-NIER.2017.7

Tools and Artifacts

dnnf

:

Implements a reduction to enable the application of falsification tools, such as adversarial attacks, to a more general set of behavioral properties of neural networks.

https://github.com/dlshriver/dnnf

[Github] [Documentation] [Video] [Docker]

dnnv-benchmarks

:

A large collection of DNN verification benchmarks, specified in DNNP and ONNX for use with DNNV, DNNF and their supported verifiers and falsifiers.

https://github.com/dlshriver/dnnv-benchmarks

[Github]

dnnv

:

Introduces a standard network and property specification formats and implements network simplifications and property reductions, facilitating verifier execution, comparison, and artifact re-use.

https://github.com/dlshriver/dnnv

[Github] [Documentation] [Video] [Docker]

Awards and Honors

• Software Engineering Institute AJ Award for Leading and Advancing (Team award), June 2025
• ACM SIGSOFT Outstanding Doctoral Dissertation, March 2023
• John A. Stankovic Outstanding Graduate Research, May 2022
• University of Nebraska-Lincoln, Highest Distinction, May 2016
• University of Nebraska-Lincoln, Computer Engineering Outstanding Undergraduate Senior, May 2016

Service

• ICSE 2026: Program Committee Member
• ICSE 2025: Program Committee Member
• TOSEM 2024: Reviewer
• TOSEM 2023: Reviewer
• ASE 2022: Committee Member in Program Committee within Artifact Evaluation-track
• ISSTA 2021: Committee Member in Artifact Evaluation Committee within the Artifact Evaluation-track
• ISSTA 2021: Co-reviewer
• ICSE 2020: Co-reviewer