(Online Meetup): Bayesian Modeling in Biotech: Using PyMC to Analyze Agricultural Data (July 27, 2022)

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Event Description

In this panel discussion we will discuss why Bayesian modeling is such a powerful tool for solving problems in biotechnology. As experiments are often complex it is important to build custom and causal models that accurately represent the structure of the experiment in the statistical model. As important decisions are made based on limited data, quantifying uncertainty at every level becomes important.

With a case-study from measuring effects of crop-types in an agricultural setting from Indigo we will highlight concretely how PyMC was used to optimally model the various intricacies of running large-scale real-world experiments. Specifically, we show how Gaussian Processes can be used to model spatial structure and combined with hierarchical modeling to pool information from multiple experiments.

About the speakers

Thomas Wiecki

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world class team of Bayesian modelers founded PyMC Labs – the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

GitHub: twiecki (Thomas Wiecki) · GitHub
Twitter: https://twitter.com/twiecki
Website: https://twiecki.io/

Bill Engels

Bill Engels is a Data Scientist at PyMC Labs with experience solving problems using Bayesian methods in several different industries. He contributed and helps maintain the Guassian process module in PyMC. He has a Masters in Statistics from Portland State.

GitHub: bwengals (Bill Engels) · GitHub
LinkedIn: https://www.linkedin.com/in/bill-engels-a5239a119/

Louis-Emmanuel (Manu) Martinet

Louis-Emmanuel is a Lead Data Scientist in the Microbial Products department at Indigo Ag. He is responsible for designing analysis pipelines to understand the performance of candidate microbes in field trials across geographies and environmental conditions (e.g., drought, soil quality, …). He joined his current company after going through the Insight Data Science Fellowship program. Prior to that, he spent several years as a Post-doctoral fellow at Boston University, Harvard, and the Massachusetts General Hospital where he analyzed human electrophysiological brain recordings in order to study brain networks during seizures. He received a Ph.D. in Computer Science in 2010 from the Sorbonne University in Paris, France.

GitHub: lemartinet (Louis-Emmanuel Martinet) · GitHub
LinkedIn: https://www.linkedin.com/in/lemartinet


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