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Mapping foundational models to use cases in biotech and pharma

Foundational models hold immense potential with hundreds of valuable downstream applications. However, determining which model suits a specific application can be daunting and often overwhelming for many researchers.

To bridge this gap, this session will guide you through the complexities of selecting and applying the right models to your research needs.

  • Get to know the most promising foundational models for biotech and pharma today.
  • Learn how to categorize these models into various biologically compelling categories.
  • Explore the data and compute requirements for each application.

Webinar resources

Speakers

Daniel Koster

VP of Product, Code Ocean

Daniel Koster

VP of Product, Code Ocean

Daniel Koster

Daniel oversees Code Ocean’s product strategy, internal interfaces, and client-facing activities. Previous achievements include the development of a deep learning platform for plant trait phenotyping and patented software for early detection of disease symptoms in plants. Daniel’s academic contributions include publications in single-molecule biophysics of topoisomerases, bacterial predator-prey dynamics on microfabricated habitats, and deep learning-based fruit detection in commercially relevant scenarios. He has two articles published on the cover of the esteemed journal Nature and over 1,200 citations. Daniel holds a PhD from Delft University of Technology and was a postdoctoral fellow at the Weizmann Institute of Science. Daniel is passionate about scientific innovation and playback theater.

Dror Hilman

Applied AI Architect, Code Ocean

Dror Hilman

Applied AI Architect, Code Ocean

Dror Hilman

Dror is an Applied AI Architect at Code Ocean. His expertise lies in AI, data science, machine learning, algorithms, computer vision, genomics, bioinformatics, and software development. He has a PhD in bioinformatics and molecular biology and over ten years of experience in programming, leading, and innovating research projects for supply chain optimization, crop quality and yield prediction, plant phenotyping, gene identification and discovery, gene-trait forecasting, and natural language processing.