Jordan Milton Malof

Research Interests

Application of advanced machine learning and computer vision techniques to real-world problems

Bio

I work with domain experts across different fields to solve challenging real-world problems through the application or development of advanced signal processing, computer vision, machine learning (especially deep learning) methods to real-world problems.  Recently, my work has spanned topics such as remote sensing, energy systems, and materials science.   My work has recently been featured in premiere machine learning conferences (e.g., NeurIps, ICLR) and computer vision conferences (e.g., WACV).

Education

• Ph.D. Duke University, 2015

Positions

• Adjunct Assistant Professor in the Department of Electrical and Computer Engineering

Awards, Honors, and Distinctions

• Bass Connections Award for Outstanding Leadership. Duke University. 2022

Courses Taught

• ENERGY 795T: Bass Connections Energy & Environment Research Team
• ENERGY 395T: Bass Connections Energy & Environment Research Team
• ECE 493: Projects in Electrical and Computer Engineering
• ECE 292: Projects in Electrical and Computer Engineering

Publications

• Yaras C, Kassaw K, Huang B, Bradbury K, Malof JM. Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024 Jan 1;17:1988–98.
• Spell GP, Ren S, Collins LM, Malof JM. Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling. 2022.
• Calhoun ZD, Lahrichi S, Ren S, Malof JM, Bradbury K. Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications. Remote Sensing. 2022 Nov 1;14(21).
• Khatib O, Ren S, Malof J, Padilla WJ. Learning the Physics of All-Dielectric Metamaterials with Deep Lorentz Neural Networks. Advanced Optical Materials. 2022 Jul 1;10(13).
• Ren S, Malof J, Fetter R, Beach R, Rineer J, Bradbury K. Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning. ISPRS International Journal of Geo-Information. 2022 Apr 1;11(4).
• Ren S, Mahendra A, Khatib O, Deng Y, Padilla WJ, Malof JM. Inverse deep learning methods and benchmarks for artificial electromagnetic material design. Nanoscale. 2022 Mar;14(10):3958–69.
• Huang B, Yang J, Streltsov A, Bradbury K, Collins LM, Malof JM. GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022 Jan 1;15:4956–70.
• Xu Y, Huang B, Luo X, Bradbury K, Malof JM. SIMPL: Generating Synthetic Overhead Imagery to Address Custom Zero-Shot and Few-Shot Detection Problems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022 Jan 1;15:4386–96.
• Deng Y, Soltani M, Ren S, Padilla W, Khatib O, Tarokh V, et al. Data from: Benchmarking deep learning architectures for artificial electromagnetic material problems. 2021.
• Khatib O, Ren S, Malof J, Padilla WJ. Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review. Advanced Functional Materials. 2021 Aug 1;31(31).
• Xu Y, Huang B, Luo X, Bradbury K, Malof JM. SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems. 2021.
• Yaras C, Kassaw K, Huang B, Bradbury K, Malof JM. Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. 2021.
• Deng Y, Ren S, Fan K, Malof JM, Padilla WJ. Neural-adjoint method for the inverse design of all-dielectric metasurfaces. Optics express. 2021 Mar;29(5):7526–34.
• Streltsov A, Malof JM, Huang B, Bradbury K. Estimating residential building energy consumption using overhead imagery. Applied Energy. 2020 Dec 15;280.
• Kong F, Huang B, Bradbury K, Malof JM. The synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation. In: Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. 2020. p. 1803–12.
• Kong F, Huang B, Bradbury K, Malof JM. The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation. 2020.
• Ren S, Padilla WJ, Malof J. Benchmarking deep inverse models over time, and the neural-adjoint method. In: Advances in Neural Information Processing Systems. 2020.
• Nadell CC, Huang B, Malof JM, Padilla WJ. Deep learning for accelerated all-dielectric metasurface design. Optics express. 2019 Sep;27(20):27523–35.
• Malof JM, Reichman D, Karem A, Frigui H, Ho KC, Wilson JN, et al. A large-scale multi-institutional evaluation of advanced discrimination algorithms for buried threat detection in ground penetrating radar. IEEE Transactions on Geoscience and Remote Sensing. 2019 Sep 1;57(9):6929–45.
• Kong F, Chen C, Huang B, Collins LM, Bradbury K, Malof JM. Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: Preliminary results. In: International Geoscience and Remote Sensing Symposium (IGARSS). 2019. p. 3903–6.
• Lin K, Huang B, Collins LM, Bradbury K, Malof JM. A simple rotational equivariance loss for generic convolutional segmentation networks: Preliminary results. In: International Geoscience and Remote Sensing Symposium (IGARSS). 2019. p. 3876–9.
• Hu W, Bradbury K, Malof JM, Li B, Huang B, Streltsov A, et al. What you get is not always what you see: pitfalls in solar array assessment using overhead imagery. 2019.
• Huang B, Lu K, Audebert N, Khalel A, Tarabalka Y, Malof J, et al. Large-scale semantic classification: Outcome of the first year of inria aerial image labeling benchmark. In: International Geoscience and Remote Sensing Symposium (IGARSS). 2018. p. 6947–50.
• Malof JM, Reichman D, Karem A, Frigui H, Ho DKC, Wilson JN, et al. A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar. 2018.
• Camilo J, Wang R, Collins LM, Bradbury K, Malof JM. Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery. 2018.
• Reichman D, Collins LM, Malof JM. Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar. In: 2017 9th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2017 - Proceedings. 2017.
• Malof JM, Bradbury K, Collins LM, Newell RG. Automatic detection of solar photovoltaic arrays in high resolution aerial imagery. Applied Energy. 2016 Dec 1;183:229–40.
• Malof JM, Morton KD, Collins LM, Torrione PA. A Probabilistic Model for Designing Multimodality Landmine Detection Systems to Improve Rates of Advance. IEEE Transactions on Geoscience and Remote Sensing. 2016 Sep 1;54(9):5258–70.
• Malof JM, Bradbury K, Collins LM, Newell RG. Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery. 2016.
• Malof JM, Morton KD, Collins LM, Torrione PA. A queuing model for designing multi-modality buried target detection systems: Preliminary results. In: Proceedings of SPIE - The International Society for Optical Engineering. 2015.
• Malof JM, Hou R, Collins LM, Bradbury K, Newell R. Automatic solar photovoltaic panel detection in satellite imagery. In: 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015. 2015. p. 1428–31.