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SigOpt Summit 2021
November 16, 2021, Online
SigOpt Summit 2021
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SigOpt Summit 2021
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Deep Learning for Proteomics and the Future of Medicine
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About the talk

With the advent of massive deep learning systems a world of applications lies ahead. However, how do we configure these systems for tasks that have never been investigated before? We are just scratching the surface when applying these deep learning models to healthcare and there is a world of potential. This talk details the journey of how we utilized deep learning to achieve state-of-the-art results on important protein classification and sorting tasks that were recently published in a peer-reviewed journal. The talk will also include discussion of techniques for adjusting the modeling workflow to meet the constraints of a healthcare use case and the importance of experimentation to achieving these results. Finally, the talk will conclude with a discussion around how this Stanford lab expects to utilize these techniques in future health science research and the impact this could have on the field.

About speaker

Alexander Johansen
PhD candidate in Computer Science at Stanford University

Alexander is a PhD student in Computer Science at Stanford, supervised by Michael P Snyder. He leads a research lab in the exploration of machine learning in Bioinformatics and MedTech at stanford-health.github.io. Alexander has been featured in venues such as ICLR and Nature Biotechnology relying on SigOpt technologies.

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