Paul Courtney, Tamr, Inc.

Paul Courtney Paul Courtney leads Tamr's life sciences implementations for customers including GlaxoSmithKline, Novartis and Genentech. Paul has decades of experience in the life sciences space supporting application development and deployment for bioinformatics teams, clinical research data management and front line research support. As Life Science Lead, Paul helps R&D organizations build next generation data engineering platforms for clinical data management.

Sessions

4:30 PM - 5:00 PM

Innovation Spotlight ChangeMaker Presentations

How much can you learn in five minutes? Quite a bit. Hear quick hitting presentations on the latest products, new releases, exciting innovations, and more. Each presenter will speak for 5 minutes and then will be available to answer questions during the reception.

David Wild, Ph.D. — Knowledge Graphs: A Transformative New Approach for Drug Discovery
  • Knowledge Graphs (KGs) are a transformative new way to think about drug discovery data
  • KG-based tools open up powerful new scientific and decision-making capabilities
Paul Courtney — Applications of Machine Learning in Clinical Data Management

Rashan Jibowu — Driving Strategic Decisions with Machine Learning

Leila Pirhaji, Ph.D. — AI-Driven Drug Discovery via Metabolomics
  • Metabolomics, or studying small molecules, are essential for discovering the right therapeutics for the right patients
  • How AI can contribute to characterizing a large number of metabolites, or small molecules, inexpensively and fast
Wayne R. Danter, MD — DeepNEU: Cellular Reprograming Comes of Age–a Machine Learning Platform with Application to Rare Diseases Research
  • Stem cell research will inevitably be transformed by computer technologies. The results of the initial DeepNEU project indicate that currently available stem cell data, computer software and hardware are sufficient to generate basic artificially induced pluripotent stem cells (aiPSC)
  • The application of this computer technology to generate disease specific aiPSCs has the potential to improve 1. disease modeling, 2. rapid prototyping of wet lab experiments, 3. grant application writing and 4. specific biomarker identification in a highly cost-effective manner

Moderator

Speakers


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