PSB 2027 — Pacific Symposium on Biocomputing
by Pacific Symposium on Biocomputing (PSB)
A 300-seat, peer-reviewed symposium on AI and computation in biomedicine, held on the Big Island of Hawaii
About This Event
PSB 2027, the Pacific Symposium on Biocomputing, is a peer-reviewed research symposium held January 3-7, 2027 at the Fairmont Orchid on the Big Island of Hawaii. Founded in 1996, PSB defines its sessions fresh each year from community proposals. The 2027 programme is dominated by AI: sessions on AI and machine learning in clinical medicine (agentic systems, clinical validation, human-AI collaboration), NLP methods for embedding real-world clinical knowledge in large language models, AI-driven precision medicine, and graph neural networks and foundation models for systems biology. Five workshops and two keynotes run alongside, and registration is capped at 300 participants.
Key Dates & Deadlines
- Camera-ready deadline
- Travel award application deadline
- Early-bird registration
- Abstract / poster submission deadline
At a Glance
- Date
- January 3–7, 2027
- Location
- Fairmont Orchid, Big Island, Hawaii
- Format
- In-Person
- Event Type
- Conference
- Status
- Upcoming
- Expected Attendance
- 300 (registration capped)
- Pricing
- Paid
- Organizer
- Pacific Symposium on Biocomputing (PSB)
- Added
- Sep 21, 2026
- Updated
- Sep 21, 2026
Topics & Focus Areas
Who Should Attend
- ✓Chief medical information officers
- ✓Heads of AI in healthcare and life sciences
- ✓Biomedical and clinical data scientists
- ✓Computational biology and bioinformatics researchers
- ✓Pharma and biotech R&D leaders
- ✓Health-system AI governance and informatics leads
Why Attend
For a health-system CMIO, a pharma head of AI or a biotech CTO, PSB 2027 is a compact read on where clinical and biomedical AI research is heading 12-24 months before it reaches vendors: how agentic clinical systems are being validated prospectively, how LLMs are being grounded in real-world clinical knowledge, and where foundation models are replacing homology-based methods in drug discovery. With registration capped at 300, leaders can talk directly with the session chairs from academic medical centres and national labs, recruit PhD and postdoc talent, and leave with peer-reviewed evidence to inform build-versus-buy and governance decisions.
Event Features
- ✓Session: AI and Machine Learning in Clinical Medicine — agentic systems, clinical validation and human-AI collaboration
- ✓Session: NLP Methods for Embedding Real-World Clinical Knowledge in LLMs — responsible, transformative medical AI
- ✓Session: Precision Medicine — computational and AI approaches to the biological basis of health and disease
- ✓Session: Systems Biology and Network Analysis — multi-omics integration to causal and mechanistic understanding
- ✓Session: Biological Molecular Function: Beyond Homology
- ✓Session: Sociotechnical Ethnography in Health Informatics
- ✓Workshop: Multi-modal Spatial-Omics — from AI models to translational applications
- ✓Workshop: Digital Health and Precision Medicine in Reproductive Health in the AI Era
- ✓Workshop: Next-Generation Biocomputing Doctoral Training in the Era of AI
- ✓Keynotes: Chirag Patel (Harvard Medical School) and Michelle Mello (Stanford University)
What Makes This Unique
- ★Registration is capped at 300 participants, so sessions are small and senior researchers are reachable — the opposite of a trade-show floor.
- ★Sessions are peer-reviewed and published in open proceedings, and are re-chosen every year from proposals, so the agenda tracks what is actually new (in 2027: agentic clinical AI and LLMs for clinical knowledge).
- ★A 2027 keynote by Stanford's Michelle Mello, 'AI-Enabled Healthcare and the Polite Fiction of the Human-in-the-Loop', puts AI liability and oversight on the main stage.
- ★Expensive for a research meeting ($1,500-$1,900) but the fee bundles meals, workshops and keynotes; registration is non-refundable.
Featured Speakers From
Industries Represented
Pricing Details
Early registration through 31 Oct 2026: $1,500 general, $1,450 ISCB members, $1,400 students/postdocs. Regular (1-30 Nov 2026): $1,700 / $1,650 / $1,600. Late (from 1 Dec 2026): $1,900 / $1,850 / $1,800. Fee includes workshops, keynotes, paper and poster sessions and PSB-hosted meals (at minimum 1 reception, 4 continental breakfasts, 2 lunches, 1 banquet). Registration is non-refundable and capped at 300.
Organizer
Pacific Symposium on Biocomputing (PSB)
psb.stanford.edu/Frequently Asked
- When is PSB 2027?
- PSB 2027 takes place January 3–7, 2027 in Big Island, Hawaii.
- Where is PSB 2027 held?
- PSB 2027 is held in Big Island, Hawaii. The event format is in-person.
- What is the PSB 2027 submission deadline?
- The next PSB 2027 deadline is camera-ready deadline on Oct 1, 2026. Remaining dates: Travel award application deadline — Oct 15, 2026; Early-bird registration — Oct 31, 2026; Abstract / poster submission deadline — Dec 1, 2026.
- How much does PSB 2027 cost?
- PSB 2027 is paid. Early registration through 31 Oct 2026: $1,500 general, $1,450 ISCB members, $1,400 students/postdocs. Regular (1-30 Nov 2026): $1,700 / $1,650 / $1,600. Late (from 1 Dec 2026): $1,900 / $1,850 / $1,800. Fee includes workshops, keynotes, paper and poster sessions and PSB-hosted meals (at minimum 1 reception, 4 continental breakfasts, 2 lunches, 1 banquet). Registration is non-refundable and capped at 300.
- Who should attend PSB 2027?
- PSB 2027 is aimed at Chief medical information officers, Heads of AI in healthcare and life sciences, Biomedical and clinical data scientists, Computational biology and bioinformatics researchers, Pharma and biotech R&D leaders, Health-system AI governance and informatics leads.
- What topics does PSB 2027 cover?
- PSB 2027 covers AI and machine learning in clinical medicine, Agentic AI systems and clinical validation, Large language models for clinical knowledge, Precision medicine and genomic interpretation, Foundation models for protein structure and function, Graph neural networks and multi-omics integration, Spatial omics, Sociotechnical implementation of AI in health.
Sources
This page was written from 7 sources, 1 on domains other than psb.stanford.edu.
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