Christine Picard

Permanent URI for this collection

https://hdl.handle.net/1805/30169

Discovering Nature's Powerful Allies in Sustainability and Justice

Professor Christine Picard is a member of the groundbreaking team that established the world's first research center dedicated to insects as feed and food, known as the Center for Environmental Sustainability through Insect Farming (CEIF), funded in by the National Science Foundation, and other industry members. Their mission involves collaborating with industry members to address the pressing needs of this emerging field through multidisciplinary research.

The insect farming industry is still in its nascent stage, and the growth potential is nothing short of remarkable. What adds to the excitement is the availability of advanced technological tools and the remarkably short generation times of insects, which enable Professor Picard and her research partners to make significant advancements in their work at an accelerated pace.

Her specific focus lies in leveraging genetic and genomic tools to enhance our ability to predict breeding outcomes and optimize production efficiency of these insects. Whether they are used as feed ingredients for livestock or as a sustainable protein source for human consumption, insects possess incredible potential. They are highly efficient protein factories, requiring fewer resources compared to traditional protein sources, and they excel at recycling food waste.

These tiny creatures are projected to have a tremendous impact as a climate solution in the future of food production. Their ability to provide sustainable protein, while minimizing resource usage and contributing to waste reduction, positions them as a crucial element in addressing the environmental challenges associated with traditional protein production methods. As part of The Center for Environmental Sustainability through Insect Farming, the research team is dedicated to pioneering research and driving innovation in this field, ensuring a more sustainable and resilient future for the food industry. Professor Picard's translation of insects into sustainable protein sources for humans and animals is another excellent example of how IUPUI's faculty members are TRANSLATING their RESEARCH INTO PRACTICE.

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Browsing Christine Picard by Author "Akata, Zeynep"

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    Classifying the Unknown: Identification of Insects by Deep Open-set Bayesian Learning
    (bioRxiv, 2021-09-17) Badirli, Sarkhan; Picard, Christine J.; Mohler, George; Akata, Zeynep; Dundar, Murat
    Insects represent a large majority of biodiversity on Earth, yet only 20% of the estimated 5.5 million insect species are currently described (1). While describing new species typically requires specific taxonomic expertise to identify morphological characters that distinguish it from other potential species, DNA-based methods have aided in providing additional evidence of separate species (2). Machine learning (ML) is emerging as a potential new approach in identifying new species, given that this analysis may be more sensitive to subtle differences humans may not process. Existing ML algorithms are limited by image repositories that do not include undescribed species. We developed a Bayesian deep learning method for the open-set classification of species. The proposed approach forms a Bayesian hierarchy of species around corresponding genera and uses deep embeddings of images and barcodes together to identify insects at the lowest level of abstraction possible. To demonstrate proof of concept, we used a database of 32,848 insect instances from 1,040 described species split into training and test data. The test data included 243 species not present in the training data. Our results demonstrate that using DNA sequences and images together, insect instances of described species can be classified with 96.66% accuracy while achieving accuracy of 81.39% in identifying genera of insect instances of undescribed species. The proposed deep open-set Bayesian model demonstrates a powerful new approach that can be used for the gargantuan task of identifying new insect species.
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    Classifying the unknown: Insect identification with deep hierarchical Bayesian learning
    (Wiley, 2023) Badirli, Sarkhan; Picard, Christine Johanna; Mohler, George; Richert, Frannie; Akata, Zeynep; Dundar, Murat
    1. Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. 2. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. 3. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). 4. Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.
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    Fine-Grained Zero-Shot Learning with DNA as Side Information
    (NeurIPS 2021, 2021-09-29) Badirli, Sarkhan; Akata, Zeynep; Mohler, George; Picard, Christine J.; Dundar, Murat; Biology, School of Science
    Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes. As manually annotated visual attributes are extremely costly and often impractical to obtain for a large number of classes, in this study we use DNA as side information for the first time for fine-grained zero-shot classification of species. Mitochondrial DNA plays an important role as a genetic marker in evolutionary biology and has been used to achieve near-perfect accuracy in the species classification of living organisms. We implement a simple hierarchical Bayesian model that uses DNA information to establish the hierarchy in the image space and employs local priors to define surrogate classes for unseen ones. On the benchmark CUB dataset, we show that DNA can be equally promising yet in general a more accessible alternative than word vectors as a side information. This is especially important as obtaining robust word representations for fine-grained species names is not a practicable goal when information about these species in free-form text is limited. On a newly compiled fine-grained insect dataset that uses DNA information from over a thousand species, we show that the Bayesian approach outperforms state-of-the-art by a wide margin.