2026 BREAKOUT DISCUSSIONS WITH CONTINENTAL BREAKFAST (IN-PERSON ONLY)
Start the day with small-group roundtable discussions designed to spark collaboration and exchange insights across the Bio-IT community. Attendees join themed tables—spanning AI, data ecosystems, foundational models, and more—for focused, peer-driven discussions that foster problem-solving, connection, and cross-functional perspectives ahead of the plenary keynote. Breakout Discussions are informal, moderated discussions, allowing participants to exchange ideas and experiences and develop future collaborations around a focused topic. Each discussion will be led by a facilitator who keeps the discussion on track and the group engaged. To get the most out of this format, please come prepared to share examples from your work, be a part of a collective, problem-solving session, and participate in active idea sharing.
Thursday, May 21
7:00 am Registration Open
7:00 am Connect & Collaborate: Breakfast Networking Roundtables (Sponsorship Opportunities Available)
Topic 1: Knowledge Graphs
Tom Plasterer, PhD, Managing Director, Knowledge Graph Capability, XponentL Data
- How are knowledge graphs supporting reliable scientific intelligence
- Lessons from early deployments
- Remaining challenges and barriers to scale
Topic 2: From Molecules to Qubits: A Collaborative Conversation on Pharma’s Quantum Future
Christopher Bishop, Chief Reinvention Officer, Improvising Careers
- Learn how leading global pharma companies are applying quantum principles to real-world research.
- Discover the quantum companies transforming traditional processes around drug development and drug discovery.
- Discuss how quantum, along with HPC and AI, is poised to help researchers tackle historically intractable problems and potentially find new treatments for diseases like cancer, Alzheimer’s, and diabetes.
Topic 3: From AI Tools to Autonomous Discovery: Are We Ready for Agentic AI in Drug Discovery?
Parthiban Srinivasan, PhD, Professor and Director, Centre for AI in Medicine, Vinayaka Mission's Research Foundation, India
- Where are we today? Are AI tools truly integrated into workflows, or still operating in silos?
- What changes with agents? How do LLM-based agents shift drug discovery from prediction to decision-making?
- What is blocking autonomy? Data quality, validation, trust, or organizational readiness for AI-driven discovery?
Topic 4: Beyond Data Management: Why AI Fails without Institutional Memory in Life Sciences
Alexandra Brocato, CEO & Co-Founder, Beakr, Inc.
- Why critical experimental knowledge is lost across ELNs, LIMS, and siloed teams, and the cost of reinventing work
- How structured experimental memory makes past decisions, failures, and context reusable across teams
- How organizations can make scientific knowledge reusable for both humans and AI
Topic 5: Supporting Innovation While Managing Risk: The CIO’s Dilemma in AI-Driven R&D
Chris Dwan, Independent Consultant, Dwan, LLC
- How do CIOs enable rapid AI and data innovation without breaking governance, compliance, or data integrity frameworks?
- What separates pilot-stage innovation from scalable, production-grade systems? Where do most organizations fail?
- How do leaders manage emerging risks (model bias, data provenance, regulatory exposure) while still pushing competitive advantage?
Topic 6: Real-World Data and Evidence: Unlocking Value Beyond Clinical Trials
Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC
- How real-world data is being used alongside clinical and experimental data
- Challenges in standardization, access, and regulatory acceptance
- Opportunities to improve outcomes, access, and long-term patient insights
8:00 am Plenary Keynote Program