Browsing by Author "Elhamod, Mohannad"

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    Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
    Elhamod, Mohannad; Khurana, Mridul; Manogaran, Harish Babu; Uyeda, Josef C.; Balk, Meghan A.; Dahdul, Wasila; Bakış, Yasin; Bart, Henry L. Jr.; Mabee, Paula M.; Lapp, Hilmar; Balhoff, James P.; Charpentier, Caleb; Carlyn, David; Chao, Wei-Lun; Stewart, Charles V.; Rubenstein, Daniel I.; Berger-Wolf, Tanya; Karpatne, Anuj (ACM, 2023-08-06)
    Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how organisms diversify and evolve. However, the measurement of traits is often a subjective and labor-intensive process, making trait discovery a highly label-scarce problem. We present a novel approach for discovering evolutionary traits directly from images without relying on trait labels. Our proposed approach, Phylo-NN, encodes the image of an organism into a sequence of quantized feature vectors–or codes–where different segments of the sequence capture evolutionary signals at varying ancestry levels in the phylogeny. We demonstrate the effectiveness of our approach in producing biologically meaningful results in a number of downstream tasks including species image generation and species-to-species image translation, using fish species as a target example.
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    Neuro-Visualizer: An Auto-encoder-based Loss Landscape Visualization Method
    Elhamod, Mohannad; Karpatne, Anuj (2023)
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    Understanding The Effects of Incorporating Scientific Knowledge on Neural Network Outputs and Loss Landscapes
    Elhamod, Mohannad (Virginia Tech, 2023-06-06)
    While machine learning (ML) methods have achieved considerable success on several mainstream problems in vision and language modeling, they are still challenged by their lack of interpretable decision-making that is consistent with scientific knowledge, limiting their applicability for scientific discovery applications. Recently, a new field of machine learning that infuses domain knowledge into data-driven ML approaches, termed Knowledge-Guided Machine Learning (KGML), has gained traction to address the challenges of traditional ML. Nonetheless, the inner workings of KGML models and algorithms are still not fully understood, and a better comprehension of its advantages and pitfalls over a suite of scientific applications is yet to be realized. In this thesis, I first tackle the task of understanding the role KGML plays at shaping the outputs of a neural network, including its latent space, and how such influence could be harnessed to achieve desirable properties, including robustness, generalizability beyond training data, and capturing knowledge priors that are of importance to experts. Second, I use and further develop loss landscape visualization tools to better understand ML model optimization at the network parameter level. Such an understanding has proven to be effective at evaluating and diagnosing different model architectures and loss functions in the field of KGML, with potential applications to a broad class of ML problems.