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AI Info Sessions

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Sessions takes place on Thursdays from 2:00 PM to 3:00 PM via Zoom.

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The Office of Responsible AI series - AI Info Sessions - is designed to help the University of Arizona and the local community build confidence, fluency, and shared understanding around responsible artificial intelligence. Each virtual session is intended to spark curiosity, create a common language, and bring clarity to responsible AI and what “AI at work” looks like.

We invite you to join sessions that will address topics such as:

  • AI basics and advanced capabilities
  • How secure AI works
  • What we mean by responsible AI
  • Licensed campus AI tools and resources
  • AI for Research
  • Terminology shaping today’s AI landscape
  • AI for Project Management
  • Copyright and data privacy considerations

Attend a Session

AI Info Sessions takes place on Thursdays from 2:00 PM to 3:00 PM via Zoom. Each session explores a unique responsible AI-related topic designed to inform and inspire. The series is designed to deepen understanding of responsible AI practices by presenting a distinct, carefully selected topic during every session. While the Spring semester dates are set, the specific topics for each session is unique and are finalized accordingly —stay tuned for updates.

View event calendar to register

Ask, Learn, and Connect

Whether you are faculty, staff, a student, or a member of our local community, you will find opportunities to learn about different aspects of responsible AI in a supportive, interesting, and conversational environment. Each session wraps up with a live Q&A, and recordings will be available on this page. If your department or team would benefit from a tailored group session, fill out the online request form.

View all Past AI Info Sessions Request a tailored group session

AI Info Sessions Presenters

Michele Cosi is a Plant Scientist by training and a Research Data Scientist by trade. His work as a plant scientist revolves around the bioinformatics and genomics aspects of the field with contributions such as the assemblies of the Oryza longistaminata (red rice) and Oryza glaberrima (Africa rice) genomes. Incorporating data science aspects in his research, Michele aided the development of scalable workflow systems capable of processing the large volumes of high-throughput phenotyping data originating from the world's largest scanalyzer by leveraging CyVerse and the UA High-Performance Computing systems. Michele is a strong believer in Open Science, educating researchers in data science best practices, FAIR and CARE principles, containerization, and cloud native technologies. His experience has seen him lead scientific workshops such as Foundational Open Science Skills (FOSS), Container and Cloud Native Camp, and international workshops such as CompBio Asia 2022 and 2023 (Thailand and Singapore), and the 6th Uppsala Transposon Symposium (Sweden).

Amanda Harrell is a Program Manager with the Office of Responsible AI. With a background in training and development, Amanda focuses on AI best practices in the workplace, helping teams and individuals navigate the human side of AI adoption — how we collaborate with AI tools and each other, deepen our expertise, and build responsible practices into our everyday work. She also hosts the weekly AI Insights Series. Her work focuses on shaping AI strategy through cross-disciplinary conversation and collaboration, and extending AI knowledge throughout campus and to the broader community.

Carlos Lizárraga-Celaya is a highly experienced Computational and Data Scientist at the Arizona Institute for Artificial Intelligence and Society (AI2S). His extensive career, spanning 30+ years, is built on a foundational technical background in Physics and Applied Mathematics.  Carlos holds an M.Sc. in Applied Mathematics (University of Arizona), an M.Sc. in Physics (UNAM), and a Ph.D. in Environmental & Water Sciences (ITSON). This strong scientific foundation allowed him to perform research as a Professor of Physics at the Universidad de Sonora from 1990 to 2021. His work included complex areas such as Climate Change Science, Micrometeorological Modeling, and the application of Machine Learning algorithms in Agriculture. He is deeply skilled in Mathematical Modeling / Numerical Analysis Algorithms. Carlos supports graduate students, staff, and faculty in assimilating advanced computational, data, and AI/ML science methods.

Past AI Info Sessions

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