Panel

Panel: Future of AI in eScience

Artificial intelligence (AI) is reshaping the way scientific discovery is conducted, from accelerating data-driven insights to enabling entirely new modes of research. As data volumes grow and experimental, observational, and simulation workflows become more complex, the integration of AI into scientific practice is no longer optional—it is transformative. This panel brings together experts from computing, domain science, and data infrastructure to discuss how AI will advance the frontiers of eScience. Panelists will explore emerging opportunities such as AI-enabled automation of experiments, large-scale model training across distributed cyberinfrastructure, and the integration of generative AI into scientific reasoning. They will also address challenges, including data quality, reproducibility, ethical and responsible AI, and the need for sustainable research ecosystems that balance human expertise with machine intelligence. By examining both current breakthroughs and long-term visions, this panel will provide the eScience community with perspectives on how AI can accelerate discovery while ensuring trust, transparency, and equity in the scientific enterprise.

  1. How has AI transformed eScience over the last 5 years? What are the most significant implications of these changes in your area?
  2. How has research software development changed with AI, and how will it change in the next few years?
  3. What are the critical infrastructure gaps (compute, data, security, software) that must be addressed to make AI a seamless part of the scientific process across disciplines, institutions, etc.
  4. What practices and technologies are needed to ensure AI-driven scientific results are explainable, trustworthy, and reproducible?
  5. How can AI augment human creativity and expertise in eScience? What are the limitations?
  6. How do we ensure that AI in science is deployed in ways that are equitable, unbiased, and balance openness with security concerns.
  7. What skills and training is needed for eScience practitioners to apply AI in their work?

Moderator

  • Kyle Chard, University of Chicago, USA

Panelists

  • Dan Katz, University of Illinois Urbana-Champaign
  • Carl Kesselman, University of Southern California
  • Tanu Malik, University of Missouri
  • Mike Papka, Argonne National Laboratory