Ashley S. Dale

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Hello World!

I am a Eric and Wendy Schmidt AI in Science Postdoctoral Fellow at the University of Toronto in the Department of Materials Science & Engineering. I research explainable and trustworthy AI for materials science.

My work has applications in spintronics, black-box model exploration, Monte-Carlo simulation, data domain gap analysis, and physics education.

selected publications

  1. SPIE
    Mind the (domain) gap: metrics for the differences in synthetic and real data distributions
    Ashley S Dale, William R Reindl, Edwin Sanchez, and 2 more authors
    In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 2024
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  2. IEEE
    Direct Adversarial Latent Estimation to Evaluate Decision Boundary Complexity in Black Box Models
    Ashley S Dale, and Lauren Christopher
    IEEE Transactions on Artificial Intelligence, 2024
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  3. IEEE
    All patched up: effective integration of real and synthetic features into a single image for object detection
    Ashley S. Dale, Lauren Christopher, William Reindl, and 5 more authors
    In 2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2023
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  4. JPMAT
    Direct observation of the magnetic anisotropy of an Fe (II) spin crossover molecular thin film
    Ashley S. Dale, Saeed Yazdani, Thilini K Ekanayaka, and 6 more authors
    Journal of Physics: Materials, 2023
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  5. JPCL
    Quantitative study of the energy changes in voltage-controlled spin crossover molecular thin films
    Aaron Mosey, Ashley S. Dale, Guanhua Hao, and 3 more authors
    The Journal of Physical Chemistry Letters, 2020
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  6. JPCM
    Intermolecular interaction and cooperativity in an Fe (II) spin crossover molecular thin film system
    Ashley S. Dale, Guanhua Hao, Rajesh V Chopdekar, and 8 more authors
    Journal of Physics: Condensed Matter, 2022
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  7. AIPR
    CNN-based network has Network Anisotropy-work harder to learn rotated feature than non-rotated feature
    Ashley S. Dale, Mei Qiu, Lauren Christopher, and 2 more authors
    In 2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2022
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