NIH / NIGMS COBRE · University of Hawai‘i · 2026–2031

Bringing AI & data science to medicine in Hawai‘i & the Pacific

PAC-AID — the Pacific Center for Artificial Intelligence and Data Science in Medicine.

Advancing AI and data science to improve health in Hawai‘i and the Pacific.

Supported by NIH / NIGMS award P20GM161995.

4
Research Project Leaders
Funded early-career investigators
2
Shared Cores
Administrative + MEDAI
5
Year Program
2026–2031, NIGMS COBRE Phase 1
8
Mentoring Team
Internal + external mentors

A nucleating site for AI in Pacific health

The Pacific Center for Artificial Intelligence and Data Science in Medicine (PAC-AID) is an NIH / NIGMS Center of Biomedical Research Excellence (COBRE, Phase 1) at the University of Hawai‘i. Hawai‘i is one of the most ethnically diverse places on Earth, with no majority group — yet data from indigenous and Pacific populations have too often been missing from the datasets that train modern AI. PACAID exists to change that.

Our long-term goal is to build self-sustaining shared cores and an AI and biomedical data-science program that continually trains investigators to use advanced data science across all available biomedical data — improving health outcomes in the diverse communities of Hawai‘i and the Pacific.

How we do it

  • Train a next-generation workforce to conduct biomedical research with artificial intelligence and advanced machine learning.
  • Build state-of-the-art cores and research projects that create real training opportunities for local investigators and foster collaboration across the national IDeA network.
  • Mentor through a national-caliber Advisory Committee and Mentoring Team, guiding research project leaders toward independent, sustained funding.

Read more about the program →

Services for every affiliated investigator

From data curation to AI modeling to mentoring — PACAID's cores are resources you can draw on, not just org-chart boxes.

Leadership, mentoring, and program coordination

Administrative Core

Integrates all Center operations — fiscal administration, the External Advisory Committee and Mentoring Team, evaluation, the Pilot Projects Program, and the annual workshop series.

  • Mentoring & career-development planning for project leaders
  • Pilot Projects Program administration and review
  • Evaluation, metrics, and NIH reporting support
  • Annual workshops, seminars, and site visits

Core Leads: John A. Shepherd, PhD · Youping Deng, PhD

Meet the core team →

Medical AI & Data Science shared resource

MEDAI Core

The center's technical engine: curates and harmonizes clinical, imaging, and multi-omics data, then builds and applies AI/ML and large-language-model methods so investigators can take on problems that were not solvable before.

  • Data curation, de-identification, and harmonization
  • AI/ML model development and interpretation
  • LLM / NLP methods for clinical text
  • Imaging analysis and deep-learning computer vision
  • Hands-on ML/AI workshops and tutorials

Core Leads: Yuanyuan Fu, PhD · Peter Sadowski, PhD

Meet the core team →

Request a core service →

Four projects, one mission

Early-career investigators lead the Center's inaugural research projects, each paired with internal and external mentors.

Project 1

AI triage of skin lesions via 2D photography

Trains a deep-learning computer-vision platform to triage skin lesions from ordinary 2D photographs, with dermatologist and pathology ground truth.

Lead: Kevin Cassel, PhD

Project team →

Project 2

Multi-omics of pancreatic cancer in NHPI & Asian populations

Integrates clinical and molecular data — transcriptional subtypes and the tumor microenvironment — to study pancreatic cancer in Native Hawaiian, Pacific Islander, and Asian populations.

Lead: Elizabeth Nakasone, MD, PhD

Project team →

Project 3

Systems functional genomics of congenital heart disease

Combines GWAS/omics analyses with hiPSC-cardiomyocyte validation to dissect the genetic basis of congenital heart disease.

Lead: Yiqiang Zhang, PhD

Project team →

Project 4

AI/ML causal discovery of environmental toxicants on fetal outcomes

Applies causal-inference and machine-learning methods to uncover how environmental toxicant exposures affect fetal outcomes.

Lead: Jonathan Huang, PhD, MPH

Project team →

Pilot Projects Program

The PACAID Pilot Projects Program funds early-stage projects that apply AI and data science to human health in Hawai‘i and the Pacific. The Fall 2026 cycle will make up to two awards of $50,000 each — pairing direct funding with hands-on access to the MEDAI Core, Hawai‘i's first biomedical AI data center, and curated representative datasets, to help investigators build toward independent research funding.

Opening soon Deadline: Anticipated Jan 2027 Award: Up to $50,000 / year (two awards)

Contact PACAID

Questions about the Center, the pilot program, cores, or collaborating? We'd love to hear from you.

PAC-AID — Pacific Center for Artificial Intelligence and Data Science in Medicine
University of Hawai‘i Cancer Center, Honolulu
701 Ilalo Street · Honolulu, HI 96813

Email the program →

Contact PD/PI: John A. Shepherd, PhD  ·  MPI: Youping Deng, PhD