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Autonomous Computing Materials

brain waves

Discovering novel computational, sensing, and data storage/ retrieval technologies through the encoding of spatial-temporal neural activity using DNA-conjugated fluorophore networks, barcoded nanoparticle networks, and phononic 2D/3D materials.

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Graphs: Metabolite Networks

networks

Building knowledge graphs of analyte-analyte relationships by integrating functional annotations from disparate repositories (e.g. metabolomics, transcriptomics, etc.) to drive functional analyses of biomedically-relevant diseases and dysfunctions.

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Deep learning: Cancer Classification

cancer detection

Applying deep neural networks to improve and facilitate the semi-quantitative annotation and classification of cancerous regions in whole slide immuno-histochemistry images of tissues (i.e. thyroid, breast, etc.) from digital pathology.

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NLP/Automation: Wet Lab
Protocols

automation

Utilizing Natural Language Processing to develop machine learning approaches for the semi-automatic conversion of wet laboratory protocols into machine-readable formats to faciliate automation of experimental workflows on robotic platforms.

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Raghu Machiraju

Dr. Raghu Machiraju’s interests include visual analytics, modeling, and machine learning, especially as they apply to topics in biology, medicine, and engineering. Over the years, he has been working increasingly on problems of computational biology and bioinformatics including the discovery of biomarkers and disease subtypes and the modeling and reconstruction of signal transduction networks. Additionally, he has led many projects in the applied AI space and is highly knowledgeable in both computer science and biotechnology. In his spare time, he has led the formation of the start-up company, AbioBot, which seeks to bring smart, cost-efficient automation of laboratory processes to the bioscience and biomedical field.