Skip to content

University of Arizona AI Innovation Lab

AI Innovation Lab = AI iLab (Temporary name)

Justification for an AI Innovation Lab

The University of Arizona (U of A) is strategically positioned to establish an AI Innovation Lab to advance regional and national AI research. This initiative is supported by U of A's existing infrastructure, interdisciplinary strengths, workforce development commitment, and land-grant mission.

Key justifications include:

  • Strong Existing Foundation in AI and Data Science:
    • The University of Arizona utilizes AI as a "driver of discovery" for water systems modeling, space research, health research, and education.
    • The AI Research Institute promotes collaboration through its "Solve bigger questions together" vision, encompassing the US Government's AI Call of Action Initiative.
    • U of A makes AI tools and support accessible campus-wide for meaningful, ethical impact.
  • Diverse and Interdisciplinary Research Strengths:
    • AI Research Institute projects align with six strategic research pillars supporting U of A's mission (data, information systems and artificial intelligence; defense and national security; energy and environmental sustainability; the future of health and biomedical sciences; the human experience; and space sciences.)
    • Focus areas include NLP, ML, Bioinformatics, Geospatial, Data Visualization, and Image Informatics.
    • AI Innovation Lab in CNNs, Generative AI, LLMs, and Privacy-Preserving techniques.
    • U of A developed AI Verde, prioritizing accuracy, privacy, and IP protection.
  • Land-Grant Mission and Regional/National Impact:
    • As a land-grant institution, U of A addresses community challenges and builds relationships with Native communities.
    • AI research tackles regional challenges including:
      • Environmental Sustainability: Projects like AIIRA use AI for agriculture, HydroGEN helps manage water events, and COALESCE develops advanced farming systems.
      • Health Sciences: Research on Alzheimer's, cancer diagnosis (OMERO), and cancer manifestations (Soteria), HIPAA compliant (CyVerse Health).
    • U of A supports rural economic development through open innovation.
    • Nationally, U of A contributes to America's AI leadership goals, supporting the "AI Action Plan."

An AI Innovation Lab would consolidate these strengths to accelerate cutting-edge research, develop transformative applications, fulfill the land-grant mission, and foster new AI leaders.

Mission

The AI iLab's mission is to position the University of Arizona as a leader in America's AI global competitiveness by developing advanced AI skills and fostering innovation. Through training, workshops, and consultations, it aims to help the academic community integrate cutting-edge AI systems into their work, contributing to national priorities and security.

Vision

The AI iLab's vision is to serve as a catalyst for the University's prominence in the AI landscape and as a key component of the new AI Research Institute. It focuses on responsible development and adoption of frontier AI technologies, aligning with the US Government's AI Action Plan to accelerate innovation, strengthen infrastructure, and support international leadership in AI.

Building on AI iLab's mission to develop data science skills and incorporate AI systems for knowledge discovery, and its vision as an innovation hub for AI adoption, below is a roadmap with strategies for a 3-month timeline and priorities.

AI iLab will collaborate with AI Research Institute and CyVerse to address the three pillars of America's AI Action Plan: Accelerating AI Innovation, Building American AI Infrastructure, and Leading in International AI Diplomacy and Security. Strategies will use existing consultation, training, and partnerships to enhance research capabilities, promote ethical AI practices, and drive discovery across University research pillars (Energy, Defense, Health, Space Sciences, and Data/AI Systems).

Research-Focused Roadmap and Strategies for AI iLab's Transformation

AI iLab will collaborate with teams of the AI Research Institute, to address the three pillars of America's AI Action Plan:

  1. Accelerating AI Innovation,
  2. Building American AI Infrastructure, and
  3. Leading in International AI Diplomacy and Security.

Strategies will use existing consultation, training, and partnerships to enhance research capabilities, promote ethical AI practices, and drive discovery across University research pillars (Energy, Defense, Health, Space Sciences, and Data/AI Systems).

Major Part 1: Elevate Research Support for AI Innovation

Priority: High – Accelerates knowledge discovery and application of powerful AI systems.

  • Strategy 1.1: Advanced AI Application and Frontier Model Consultation.
    • Focus: Reposition consultation services for specialized support with frontier AI models (Generative AI, Vision Transformers, NLP) emphasizing interpretability and robustness. Target projects driving AI-Enabled Science across UA's research pillars in water systems, space phenomena, health, and education.
    • Desired Outcomes: Research projects demonstrate transformative AI applications leading to scientific advancements and competitive funding.
  • Strategy 1.2: Research-Oriented AI Literacy and Open Innovation.
    • Focus: Expand workshops to cutting-edge AI topics relevant to research challenges, including Open-Source AI methodologies, domain-specific machine learning, and interdisciplinary collaboration approaches.
    • Desired Outcomes: Research community equipped with advanced AI skills, collaborating to tackle complex questions not achievable individually

Major Part 2: Strengthen AI Research Infrastructure and Security Practices

Priority: High – Supports "Build American AI Infrastructure" pillar through secure, scalable research environments.

  • Strategy 2.1: Optimize AI Computational Resources.
    • Focus: Partner with UITS Research Computing, CyVerse, and AI Research Institute to optimize AI workloads on HPC, Jetstream2, and cloud platforms, including large-scale data visualization support.
    • Desired Outcomes: Researchers effectively use advanced computational resources for large-scale AI models, supporting energy generation and grid strengthening for AI leadership.
  • Strategy 2.2: Implement Secure AI Practices.
    • Focus: Provide consultation on secure AI technologies and cybersecurity. Use AI iLab and AI Research Institute expertise in privacy-preserving AI methods for sensitive health and security research.
    • Desired Outcomes: Research meets high security and privacy standards, protecting innovations and building AI system trust.

Major Part 3: Drive AI Research for Global Leadership and Security

Priority: Medium-High – Positions UA as a leader in responsible AI, aligning with national security and diplomacy goals.

  • Strategy 3.1: Proactive Engagement in National Security and Biosecurity AI Research.
    • Focus: Support research projects addressing security risks in frontier models and biosecurity, including Space Domain Awareness and health-related projects.
    • Desired Outcomes: UA research contributes to national security objectives and prevents "adversaries from free-riding on our innovation."
  • Strategy 3.2: Foster Ethical AI and American Values in Research Design.
    • Focus: Develop guidance on ensuring frontier AI protects free speech and American values in research planning. Promote ethical considerations in AI outcomes and data use.
    • Desired Outcomes: Research that is innovative, ethically sound, and aligned with societal values, advancing America's leadership in responsible AI.

Measurable Goals and Milestones in a 3-Month Timeline (Research Focus)

A complete transformation is a long-term goal, but these initial 3-month milestones are designed for rapid, impactful progress from a research perspective.

Within 3 Months:

1. Elevate Research Support for AI Innovation (High Priority)

  • Goal 1.1: Increase Advanced AI Research Consultations.
  • Milestone: Conduct 15+ in-depth consultations with researchers on frontier AI methods for ongoing projects. At least 5 consultations should target national priority areas (health, environment, space, defense, fusion).

  • Measure: Number of consultations (target: 15+); new proposals mentioning AI iLab support (target: 5+); researcher feedback (target: 4.0+/5.0).

  • Goal 1.2: Launch Focused Research AI Training Modules.

    • Milestone: Develop 2-3 advanced training workshops on topics like "AI Interpretability," "AI-Enabled Science Applications," and "Open-Source AI in Research," building on existing expertise.
    • Measure: New modules launched (target: 2-3); researcher participants (target: 60+); utility feedback (target: 4.2+/5.0).

2. Strengthen AI Research Infrastructure and Security Practices (High Priority)

  • Goal 2.1: Enhance Secure AI Infrastructure Guidance.
    • Milestone: Create a "Best Practices Guide for Secure AI Development" focusing on "Secure-By-Design Technologies" and "Critical Infrastructure." Detail CyVerse, Jetstream2, and HPC usage for secure AI research.
    • Measure: Guide published and promoted; 2+ webinars/office hours on implementation.
  • Goal 2.2: Privacy-Preserving AI for Sensitive Research.
    • Milestone: Identify 3+ research projects with sensitive data that can benefit from privacy-preserving AI methods. Begin consultations utilizing AI Research Institute's AI Verde platform.
    • Measure: Projects engaged (target: 3+); develop proof-of-concept plans for at least 1 project.

3. Drive AI Research for Global Leadership and Security (Medium-High Priority)

  • Goal 3.1: Develop Ethical & Security Framework for AI Research.
    • Milestone: Partner with AI Research Institute to draft an "Ethical and National Security AI Research Framework" covering security risks in frontier models, biosecurity, and protecting American values.
    • Measure: Framework document submitted for review; conduct 1 internal training session on implementation.
  • Goal 3.2: Establish AI iLab's Role in AI Ecosystem.
    • Milestone: Create referral channels between AI iLab and the AI Research Institute. Participate in 2+ planning meetings to integrate AI iLab services into strategic initiatives.
    • Measure: Documented referral process; representation in key meetings; ****AI iLab** featured in one official Institute communication**.

Feasibility in a 3-Month Timeline

While full "transformation" and "global dominance" require years, the proposed goals are achievable within 3 months as targeted research-focused actions.

These initial steps will:

  • Pivot research services to align with national AI Action Plan and University priorities.
  • Utilize existing partnerships (AI Research Institute, CyVerse, UITS) to deliver high-value support.
  • Show AI iLab's value as a key enabler for AI research within the new Research Institute.
  • Create a foundation for future program expansion and research integration.

This 3-month sprint will establish AI iLab as a catalyst in the University's AI research agenda.

Existing AI Laboratories

  • Berkeley Artificial Intelligence Research - BAIR. BAIR brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, control, robotics, and more.
  • Cornell AI Innovation Lab. At the GenAI Innovation Lab, we believe in the power of collaboration to tackle previously unsolvable challenges. Our projects focus on building AI Agents and Custom RAG Applications to push the boundaries of what's possible.
  • Harvard AI and Robotics Lab. Harvard AI and Robotics Lab strives to transform human wellbeing through the power of artificial intelligence and robotics.
  • Hopkins AI Lab. The Hopkins AI Lab offers members of the Johns Hopkins community secure and easy access to Large Language Models (LLMs) from industry leaders including OpenAI, Anthropic, and Meta. Developed by IT@JH, this secure environment mitigates security and privacy risks while ensuring that data remains protected. The objective is to make artificial intelligence accessible to the entire organization in support of the university’s mission of knowledge for the world..
  • Michigan AI Laboratory. Artificial Intelligence (AI) research at the University of Michigan is comprised of a multidisciplinary group of researchers conducting theoretical, experimental, and applied investigations of intelligent systems. Current projects include research in cognitive architectures, distributed systems of multiple agents, machine learning, data mining, computer vision, natural language processing, robotics, computational healthcare, human computing, and computational social science, and others. (Flyer)
  • MIT CSAIL. MIT's Computer Science and Artificial Intelligence Laboratory pioneers research in computing that improves the way people work, play, and learn. We focus on developing fundamental new technologies, conducting basic research that furthers the field of computing, and inspiring and educating future generations of scientists and technologists.
  • MIT-IBM Watson AI Lab. We are a community of scientists at MIT and IBM Research. We conduct AI research and work with global organizations to bridge algorithms to impact business and society.
  • Princeton AI Lab. At Princeton, nimble, cross-disciplinary research teams accelerate AI discovery by bypassing traditional university bureaucracy. Top engineers, scientists, humanists, and policy experts collaborate through a deeply interdisciplinary approach to problem solving.
  • Stanford AI Lab - SAIL. SAIL remains a proud leading intellectual hub for scientists and engineers, an education Mecca for students, and a center of excellence for cutting edge research work. (Brochure)
  • Wharton Generative AI Labs - UPenn. We combine research and prototyping to create AI applications that will change how we work and learn.

(Updated: 8/25/2025) C. Lizarraga