Accelerate Biomedical and Life Sciences Research and Discovery Processes with the Power of Next-Generation Al

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Wednesday, September 8, 2021 at 8am EDT| 5am PDT| 1pm BST| 2pm CEST AI has opened up an enormous amount of possibilities for biomedical and life sciences organizations to use growing volumes of data to accelerate and improve upon critical processes that can speed time to value and improve patient outcomes. Learn how next generation AI is revolutionizing how these organizations can deliver innovation and value to patients and overcome the constraints of legacy technology, easier and faster than ever before.

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Event Overview:

The amount of data needed and used in biomedical and life sciences R&D processes, including drug discovery and research for cancer and other diseases, is growing at an exponential rate. Artificial intelligence (AI) has opened up an enormous amount of possibilities for scientists and researchers to use this data to accelerate and improve upon critical processes such as discovery and prognosis of diseases, drug design, protein folding, and molecular docking or image analysis for segmentation, registration, clinical decision support, and stratification models.

This session features an interactive discussion between panelists Colin Birkenbihl, research fellow at Fraunhofer SCAI and Arvind Sujeeth, VP of engineering, compilers, at SambaNova Systems. Topics include current data management challenges and the limitations of current AI technology, while also providing a look at how next-generation AI can offer pharma and life sciences organizations a new option for greater performance and efficiency that breaks the barriers and limitations of legacy technology.

Key Learning Objectives:

  • Review current challenges around data including the volume, different types, difficulties managing it, and efficiently utilizing it
  • Learn ways machine learning and neural network techniques can improve performance and

revolutionize research

  • Hear examples of ways companies are leveraging NLP[RP1] and high-resolution computer vision
  • Understand critical benefits and considerations for using AI on biomedical data

Who Should Attend:

  • Life Sciences, Pharmaceutical industry
  • VP/Head/Director of AI/ML
  • CXO, Chief Data Officer
  • Chief Scientist, Research Scientist
  • Dir/Head/VP/CXO Cloud Architect, Cloud Strategy, Cloud Infrastructure
  • Data engineers and data scientists
  • AI/ML Engineer
  • MLOps


Colin Birkenbihl
Research Fellow
Fraunhofer SCAI

Colin Birkenbihl is a research fellow at Fraunhofer SCAI, and is part of the group for artificial intelligence and data science. Colin has worked in the department of bioinformatics at Fraunhofer SCAI in the artificial intelligence and data science group for the past four years and previously worked at the Max Planck Institute for plant breeding research in the department for plant-microbe interaction. Colin has contributed to multiple large European Union projects (AETIONOMY, EPAD, RADAR-AD) and (co)-leads a work package on progression modeling and data science in the Horizon2020 project “TheVirtualBrain-Cloud”. The main focus of his work is utilizing artificial intelligence approaches, data mining, and statistics to analyze time-series patient-level clinical data, mainly in the area of neurodegenerative diseases.

Arvind Sujeeth
VP of Engineering, Compilers
SambaNova Systems

Arvind Sujeeth is VP of engineering, compilers at SambaNova Systems. Arvind leads the compiler organization and is responsible for shaping the company’s software vision. Before joining SambaNova Systems, Arvind was the cofounder and chief technology officer of fintech startup, which used machine learning to increase financial access in emerging markets. Prior to that, he was a research assistant at Stanford University where he developed compilers for parallel and heterogeneous computing platforms. Arvind holds doctorate and master’s degrees in electrical engineering from Stanford University and a bachelor’s degree in computer engineering from the University of Texas at Austin.

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