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Detangling Variegated Data to Drive Scientific Innovation

Description

Today’s high-stakes, time- and cost-intensive scientific studies are carried out with increasingly complex tools. This include instrumentation, software tools, and sample complexity that make it difficult to achieve experimental reproducibility. Along the way, intermediary results are measured in multidimensional, seemingly unstructured, data formats. With such large volumes of variegated inputs and intermediaries that could impact successful outcomes, we turn to a novel platform for real-time analytics to uncover latent, and thus potentially overlooked contributors to unsuccessful experimental results. Identifying and removing these obstacles promptly intercepts continued downstream propagation of these issues and yields the types of successful, meaningful outcomes that drive scientific innovation. We present case studies showing how seemingly unrelated data affecting experimental procedures can point towards unexpected root causes of irreproducibility and errors, and how “unknown unknowns” can be more readily identified.

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Contributors

  • Sridhar Iyengar, Ph.D., CEO, Elemental Machines

    Sridhar Iyengar, a serial entrepreneur, has revolutionized multiple industries. Sridhar’s first company, Agamatrix, developed the first medical device, the iBGStar (a glucometer), to connect to the iPhone, setting the stage for connected health. AgaMatrix established partnerships with Apple, Sanofi, Walgreens, Amazon, and Target. His second company, Misfit, maker of elegant wearable products, was sold to Fossil for $260M. Sridhar’s current company, Elemental Machines, leverages Industry 4.0 technologies such as AI, data science and IoT to accelerate scientific innovation. Sridhar holds over 50 patents and received his Ph.D. from Cambridge University as a Marshall Scholar. Beyond Elemental Machines, Sridhar has been known to run 13.1 miles on occasion and has been spotted on stage behind a wall of drums.

March 10, 2020
Tue 1:00 PM EDT

Duration 1H 0M

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