Date/Time
Date(s) - 22 Oct 2019
8:15 am - 9:00 pm

Location
Hilton Back Bay Boston

Categories No Categories


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.

 

Link to Draft Agenda

 

Daytime sessions

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.

 

Register here to attend

 

Evening session

  • 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?

 

Register here to attend