Projects per year
Personal profile
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Dive into the research topics where David Jonathan Miller is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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EAGER: Clustering, Auto-Encoding, and Generative Modelling of 3D Object Representations for Manufacturing
Miller, D. J. (CoPI) & Kesidis, G. (PI)
5/15/24 → 4/30/26
Project: Research project
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SBIR Phase I: Post-training deep neural networks certification against backdoor data poisoning attacks
Li, X. (PI), Miller, D. J. (CoPI) & Kesidis, G. (CoPI)
4/1/22 → 6/30/23
Project: Research project
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TWC: Medium: Collaborative: Towards Securing Coupled Financial and Power Systems in the Next Generation Smart Grid
Kesidis, G. (PI) & Miller, D. J. (CoPI)
9/1/12 → 8/31/17
Project: Research project
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NetSE Small: Unsupervised flow-based clustering
Kesidis, G. (PI) & Miller, D. J. (CoPI)
9/1/09 → 8/31/13
Project: Research project
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NRT: Collaborative research: Testing and Benchmarking Methodologies for Future Network Security Mechanisms
Kesidis, G. (PI), Miller, D. J. (CoPI) & Liu, P. (CoPI)
9/1/03 → 8/31/08
Project: Research project
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BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers: A Comprehensive Study
Li, X., Miller, D. J., Xiang, Z. & Kesidis, G., 2024, In: IEEE Transactions on Knowledge and Data Engineering. 36, 8, p. 3697-3711 15 p.Research output: Contribution to journal › Article › peer-review
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A BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers
Li, X., Miller, D. J., Xiang, Z. & Kesidis, G., 2023, Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023. Comminiello, D. & Scarpiniti, M. (eds.). IEEE Computer Society, (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; vol. 2023-September).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open Access -
Anomaly-Detection Defense Against Test-Time Evasion Attacks on Robust DNNs
Tao, Y., Xiang, Z., Miller, D. J. & Kesidis, G., Jan 1 2023, Handbook of Dynamic Data Driven Applications Systems: Volume 2. Springer International Publishing, Vol. 2. p. 795-805 11 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter
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Anomaly detection of adversarial examples using class-conditional generative adversarial networks
Wang, H., Miller, D. J. & Kesidis, G., Jan 2023, In: Computers and Security. 124, 102956.Research output: Contribution to journal › Article › peer-review
Open Access9 Scopus citations -
Optimization of ReaxFF Reactive Force Field Parameters for Cu/Si/O Systems via Neural Network Inversion with Application to Copper Oxide Interaction with Silicon
Roshan, K. A., Talkhoncheh, M. K., Sengul, M. Y., Miller, D. J. & van Duin, A. C. T., Oct 19 2023, In: Journal of Physical Chemistry C. 127, 41, p. 20445-20458 14 p.Research output: Contribution to journal › Article › peer-review
2 Scopus citations