Date(s) - 22 Oct 2019
8:15 am - 9:00 pm
Hilton Back Bay Boston
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Pistoia Alliance’s Centre of Excellence (CoE) for Artificial Intelligence/Machine Learning (AI/ML) in Life Sciences and the FAIR Implementation for Life Science industry project are planning a joint workshop in Boston on Tuesday, October 22nd 2019. It will allow our communities to meet, share ideas and agree on action plans.
FAIR Implementation Workshop:
- Common challenges and solution brainstorming for FAIR Implementation by pharma/biotech and vendors
- How these potential solutions might relate to the FAIR Implementation process and the FAIR Toolkit
AI Best Practices Project Team Workshop:
- Should we consider an AI Best Practice Toolkit?
- What best work practices in AI/ML offer the highest value?
- What parts of the pharmaceutical value chain benefit from AI the most?
Assay FAIR Data Annotation Project Team Workshop:
- Brainstorm the value and ROI of Assay FAIR annotation
There will be refreshment breaks between daytime workshops and opportunities for poster viewing and networking. Lunch will be provided.
- Summaries from the daytime sessions
- Launch of Datathon #2
- Plenary talks
- Networking reception with drinks and refreshments
- Poster viewing
Who should attend
- Research data scientists, AI/ML practitioners and managers who are keen to share the best practices and enhance the impact of AI/ML in our industry
- Those building their AI/ML adoption plans and keen to network with others
- We will have attendees reflecting the whole industry, pharma/biotech, vendors, research organisations etc reflecting the membership of Pistoia Alliance and beyond, with exciting networking opportunities
Topics that need to be addressed at the workshops include:
- What areas promise the biggest impact in 2020 & beyond?
- Where can we collaborate as Life Science industry to enhance FAIR data and AI/ML impact?
- Expanding FAIR data and AI/ML use within Research and Development
- What are the opportunities and challenges?
- What data needs to be available to build better models?
- What can we do to improve data quality beyond FAIR?