Samuel Mulder
Orcid: 0000-0001-9514-7061Affiliations:
- Auburn University, Department of Computer Science and Software Engineering, Auburn, AL, USA
- Sandia National Laboratories, Albuquerque, NM, USA
- University of Missouri-Rolla, Department of Computer Science, Rolla, MO, USA (PhD)
According to our database1,
Samuel Mulder authored at least 16 papers
between 2003 and 2026.
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Bibliography
2026
Frontiers Virtual Real., 2026
2025
How Different Tokenization Algorithms Impact LLMs and Transformer Models for Binary Code Analysis.
CoRR, November, 2025
From Bytes to Types: Enhancing Type Prediction in Executables With Transformer-Based Binary Analysis Tool SeeType.
IEEE Access, 2025
Large Language Models as Visualization Agents for Immersive Binary Reverse Engineering.
Proceedings of the IEEE Working Conference on Software Visualization, 2025
Analyzing Competitive Coevolution across Families of N-Player Games through Tree Search.
Proceedings of the 18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2025
Triaging Firmware Updates for Critical Systems to Defend Against Software Supply Chain Threats.
Proceedings of the Annual Computer Security Applications Conference, 2025
2024
CoRR, 2024
A Cognitive Approach to Improving Binary Reverse Engineering with Immersive Virtual Reality.
Proceedings of the IEEE Working Conference on Software Visualization, 2024
Proceedings of the IEEE Conference on Games, 2024
2017
Improving performance of CDCL SAT solvers by automated design of variable selection heuristics.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017
2014
Proceedings of the IEEE 38th Annual Computer Software and Applications Conference, 2014
2011
Adaptive Rule-Based Malware Detection Employing Learning Classifier Systems: A Proof of Concept.
Proceedings of the Workshop Proceedings of the 35th Annual IEEE International Computer Software and Applications Conference, 2011
2009
Proceedings of the International Joint Conference on Neural Networks, 2009
2003
Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks.
Neural Networks, 2003
Using adaptive resonance theory and local optimization to divide and conquer large scale traveling salesman problems.
Proceedings of the International Joint Conference on Neural Networks, 2003