Visit UC Davis →
UC Davis AI for Health Collab

Advancing responsible AI for Health research.

The UC Davis AI for Health Collab (AI4HC) is a cross-college hub jointly anchored in the Computer Science (College of Engineering (CoE)) and the Radiology Department (School of Medicine (SOM)) at UC Davis. Its purpose is to catalyze cross-department relationships for research, education, translation, and responsible innovation in artificial intelligence (AI) for health. Specific goals are to:

  • Serve the needs of its constitutive departments and UC Davis broadly.
  • Build a community around AI for Health through durable bridges between engineering and medicine for funding, research and education.
  • Establish shared resources for research and education, e.g., datasets, compute environments, and models.
  • Provide a welcoming, intellectually vibrant home for graduate students and trainees.
  • Convene seminars, meetups, and visiting talks to grow a cohesive community.
  • Establish and disseminate best practices for data access, analytics, and reproducible workflows across campus.

Explore Research
Campus-wide collaboration Healthcare, engineering & data science Centered on equity & ethics
15+
Clinical & Research Units
UC Davis Health, campus labs, and community partners
30+
Student & Trainee Projects
Interdisciplinary projects at the AI–health interface
6
Focus Areas
Imaging, predictive modeling, documentation, and more

Patients & Communities

Learn how AI projects are informed by real-world needs.

Students & Trainees

Find courses, projects, and mentoring opportunities.

Clinicians & Staff

Co-create AI tools that fit clinical workflows.

Researchers & Partners

Collaborate on grants, studies, and deployments.
About

A hub for AI innovation in healthcare at UC Davis.

AI for Health Collab serves as a cross-college hub for AI-related health research, education, and community engagement, working across UC Davis and UC Davis Health.

Our Mission

We aim to create AI systems that genuinely support clinicians and patients, improve outcomes, and reduce disparities. From early exploration to deployment, our work centers on transparency, collaboration, and impact.

  • Design and evaluate clinically meaningful AI tools.
  • Embed fairness, robustness, and safety into every project.
  • Translate research into real-world health improvements.

Our Core Pillars

Responsible by Design
We prioritize interpretability, bias assessment, and governance so that AI systems can be trusted by clinicians, patients, and the public.
Deep Collaboration
Projects are co-developed across schools, departments, and community organizations, ensuring that AI aligns with real needs and constraints.
Education & Training
We support students and trainees through seminars, projects, and mentorship at the intersection of AI, medicine, and society.
Resources

Datasets & Models

Explore the tools and assets we are assembling to accelerate responsible AI for health across UC Davis.

Datasets

This space will host datasets curated by the AI for Health Collab. We are currently preparing resources and will share them here as they become available.

Models

This is where you will find models created by the AI for Health Collab. Check back soon as we publish our first releases.

Research

Core Research Themes

Customize these themes with your own labs, projects, and collaborations within UC Davis and UC Davis Health.

Clinical AI & Decision Support

Predictive Modeling & Triage

Developing robust models using EHR, time series, and structured data for risk prediction, triage, and care planning, with an eye toward calibration and prospective evaluation.

Imaging & Multimodal Learning

From Pixels to Reports

Leveraging computer vision and multimodal architectures for radiology, pathology, and beyond, including tools that assist with interpretation, documentation, and follow-up.

Health Equity & Ethics

Equitable, Trustworthy AI

Studying bias, robustness, and governance in health AI systems and working with stakeholders to design interventions that promote fairness and accessibility.

Education & Training

Opportunities for Students & Trainees

Highlight courses, reading groups, fellowships, and project-based opportunities related to AI in healthcare.

Learning & Coursework

  • Seminars and workshops on AI in medicine and public health.
  • Project-based courses connecting CS, statistics, and health.
  • Opportunities to present work in interdisciplinary forums.

Student Involvement

  • Graduate and undergraduate research assistant positions.
  • Student-led reading groups and working groups.
  • Mentored projects with clinicians and community partners at UC Davis Health.
News & Events

Latest Updates from AI for Health

Replace these placeholders with news stories, announcements, and upcoming events for your initiative.

Latest News

  • Launch of the UC Davis AI for Health Seminar Series
    A recurring seminar featuring speakers from campus, UC Davis Health, and external partners on AI, medicine, and society.
    News Fall 2025
  • Call for Interdisciplinary Student Project Proposals
    Students from across UC Davis are invited to propose AI-for-health projects that pair technical innovation with clinical or community partners.
    Announcement Ongoing

Upcoming Events

  • AI for Health Kickoff & Networking Session
    Join faculty, trainees, and staff for lightning talks and informal networking around current and planned AI health projects.
    TBD Location: TBD
  • Responsible AI in Healthcare Workshop
    A hands-on session on fairness assessment, model interpretability, and human-centered evaluation in clinical AI.
    TBD UC Davis Health
This page will evolve as our community grows.
People

Our Community

Meet the faculty, postdoctoral researchers, and students shaping AI for Health at UC Davis.

Leadership
Portrait of Vladimir Filkov
Vladimir Filkov
Director

Vladimir Filkov serves as Director of AI4HC. He is a Professor of Computer Science at UC Davis and directs the DECAL and AI for Health labs, applying AI, data science, and network science to software, biological, and medical data.

Portrait placeholder for Misagh Piran
Misagh Piran
Co-director

Misagh Piran, M.D., serves as Co-director of AI4HC. He is a Clinical Professor of Radiology at UC Davis Health and specializes in cardiac MRI and CT with a focus on congenital heart disease.

Portrait of Roger Eric Goldman
Roger Eric Goldman
Associate Director

Roger Eric Goldman, M.D., Ph.D., serves as Associate Director of AI4HC. He is an Assistant Professor of Radiology at UC Davis Health specializing in vascular and interventional radiology and advancing minimally invasive, image-guided procedures and new interventional technologies.

Postdocs
Portrait of Dr. Xiaoguang Zhu (Apollo)
Dr. Xiaoguang Zhu
Postdoc

Xiaoguang (Apollo) Zhu is a DataLab Postdoctoral Scholar at UC Davis, working at the interface of data science, machine learning, and domain science. His recent work includes methods for modeling complex multimodal and health data, with applications in imaging and AI for scientific discovery.

Students
Portrait of Nafiz Imtiaz Khan
Nafiz Imtiaz Khan
Student

Nafiz Imtiaz Khan is a Ph.D. student in Computer Science at UC Davis and a member of the DECAL lab. His research focuses on applying large language models and machine learning to software engineering and health informatics, with an emphasis on scalable, retrieval-augmented AI systems.

Portrait of Saisha Shetty
Saisha Shetty
Student

Saisha Shetty is an M.S. student in Computer Science at UC Davis and a Graduate Student Researcher working on annotating radiology clinical notes with large language models. She is broadly interested in AI for health, deep learning, and building dependable ML systems.

Portrait of Raiyan Jahangir
Raiyan Jahangir
Student

Raiyan Jahangir is a Ph.D. student in Computer Science at UC Davis whose research lies at the intersection of artificial intelligence and health. His work includes machine-learning methods for cardiovascular risk prediction, medical imaging, and accessible assistive technologies.

Contact

Get Involved with UC Davis AI for Health

Use this space for a mailing list sign-up, interest form, or contact details for your organizing team.

Stay Connected

Replace these placeholders with your official contact information and communication channels (e.g., listserv, website, or help form).

  • Email (placeholder): ai-for-health@ucdavis.edu
  • Join our mailing list for seminar and event announcements across UC Davis and UC Davis Health.
  • Faculty & staff: reach out to discuss emerging projects, grant ideas, or educational initiatives.
  • Community partners: contact us to explore collaborations and co-designed AI tools.