Abstract: The goal of this session is to demonstrate and learn about building end-to-end AI pipelines for biotechnology and healthcare applications. In addition to being a vital investment and providing competitive advantage opportunities, the spread of data science and data tools requires organizations to recognize the necessity of AI adoption and the associated infrastructure and operational changes. Critical steps need to be taken to turn ad hoc development into a comprehensive execution plan, detailing interdependent objectives of strategy, engineering & architecture, data collection, model development, and end-user usage. Pragmatic technical implementation can have the potential to lower costs, identify more effective treatments, develop new tools and products, transforming areas such as clinical trials, automated medical imaging, biomarkers analysis, and drug discovery. In this session we will explore these underlying goals, technical case studies within major organizations, and R&D initiatives across data types that transitioned from simple analytics to deep learning driven systems. We highly encourage executives and leaders of all backgrounds to join.
Bio: Michael Segala is the CEO and Co-Founder of SFL Scientific, a data science consulting firm helping develop AI solutions for organizations building new products, tools, and services. Michael has years of experience leading projects that apply data science and mathematical modeling to solve complex business problems. For his doctoral thesis, he worked as part of the CERN team that discovered the Higgs Boson, which ultimately led to the Nobel Prize in Physics. After graduating from Brown University, he worked as a data science consultant at Tessella Inc. and a principal data scientist at Compete Inc. and Akamai Technologies. He now leads SFL Scientific’s mission in bringing together data strategy, data science, AI engineering, and emerging technology partners together to solve technical problems across industries.