Marc Sze
Principal Scientist Merck & Co
Dr. Sze has spent over 7 years at Merck supporting early discovery efforts in Immuno-oncology and Immunology therapeutics areas. His team specializes in using multiple omics technologies to help research teams find novel targets, create PK/PD biomarkers for pipeline programs, and develop cutting edge methods and pipelines. His team’s work spans genomics, genetics, and proteomics-based technologies and approaches.
Seminars
Thursday 2nd April 2026
Panel Discussion: Enabling Precision Medicine in IBD Through Advanced Data Collaboratio
8:30 am
- Explore how different stakeholders, clinicians, technology providers, and patient organizations define the role of data collaboration in advancing precision medicine today
- Why is a data-driven precision medicine strategy critical now in IBD, and what are the clinical and patient consequences if collaboration doesn’t happen early enough?
- What are the biggest barriers and opportunities in standardising endpoints, clinical assumptions, metadata, and biospecimen strategies across real-world and clinical trial datasets?
- As AI becomes embedded in drug discovery and development, how does data quality, particularly in real-world versus clinical trial data, limit or enable its impact?
- What practical steps can the ecosystem take to translate collaborative data efforts into actionable precision medicine, from trial design and regulatory strategy to prescribing and patient access?
Thursday 2nd April 2026
Does Resolution Depth Still Matter in the Era of Big Data & “Artificial Intelligence”
8:00 am
- What is big data and what is generally considered big data. This section will provide a quick overview of how big data has evolved and how we classify “big data” has changed over time
- Learn about different issues that can limit the utility of genomics data and is normally not considered when aggregating data collections together
- Discover how assumptions made about the biology and the AI/ML methods being employed may lead to possible blind spots for accurate discovery
