David Wild, Ph.D., Data2Discovery Inc

David Wild, Ph.D. David Wild is a practitioner and educator in informatics & computing, data science, pharmaceutical research and emergency management. He is associate professor in the School of Informatics, Computing and Engineering (SICE) at Indiana University. He has directed major programs and research initiatives in data science and cheminformatics including the Integrative Data Science Laboratory (IDSL), the IU Data Science Program, and the IU Cheminformatics PhD Program. He is co-founder and President of Data2Discovery Inc., a company exploring with pharmaceutical customers the huge potential for AI, machine learning, graph and linked data technologies in drug discovery. His research interests include data science for drug discovery and healthcare; cheminformatics; network chemical biology; informatics in disasters and emergency response, and data privacy, ethics and security. He is also active in multiple innovation and consulting initiatives and is an advisor to several startups. David completed a B.Sc. in Computing Science at Aston University, Birmingham, England in 1991, and a Ph.D. in Computational Drug Discovery at Sheffield University, England in 1994. He worked for several years in scientific computing in the pharmaceutical industry, before moving into academia in 2004 to form new academic research and educational programs at Indiana University. He has around 100 research publications and is founding editor of the Journal of Cheminformatics. He has been PI or CoPI on around $4m in funding. He is a certified Emergency Medical Technician (EMT) and is trained in Emergency 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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