The Data Standard
Data Science, Machine Learning (ML), and Artificial Intelligence (AI) have without doubt become hot topics across all industries, including healthcare. Companies, large and small, are rushing to stock up on data scientists, but are data scientists alone enough to build a successful data science practice in healthcare? Without a doubt, data scientists are needed to build models. Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks.
In the era of eHealth and personalized medicine, “big data” and “machine learning” are increasingly becoming part of the medical world. Algorithms are capable of supporting diagnostic and therapeutic processes and offer added value for both healthcare professionals and patients.
Meet The Host
VP, Strategy and Business Development at Pandio
Results-oriented executive with a strong track record of performance both as a strategic thinker and operator. Skilled in Corporate Development, Strategic Planning, Mergers and Acquisitions, and Marketing Strategy.
Meet The Guest
Data Science Lead at Relation Therapeutics
Cristian is a biomedical data scientist with expertise in both industry and academia, who previously worked in cell therapy at Adaptimmune. Cristian achieved a first-class undergraduate degree with a class medal from the School of Informatics at The University of Edinburgh. He then received his DPhil (PhD) in Systems Approaches to Biomedical Sciences from the Department of Statistics at The University of Oxford, where he authored several papers of significant importance to the field of computational antibody modeling.