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NSF Meta-Workshop: AI to Accelerate Science and Engineering Discover (AI2ASED)

2–3 Oct 2023
US/Eastern timezone

Session

Lightning talk I

2 Oct 2023, 11:30

Conveners

Lightning talk I: AI/ML for data-driven discovery

  • Joshua Agar
  • Shih-Chieh Hsu (University of Washington Seattle (US))

Presentation materials

Lightning1 note
  1. 12. AI for Science in Quantum, Atomistic, and Continuum Systems
    Shuiwang Ji (Texas A&M)
    02/10/2023, 11:30

    In this talk, I will provide an overview of research on developing AI methods to understand the natural world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate) scales. My talk will focus on how to capture symmetries in physical systems using equivariant models. I will also touch on a few other...

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  2. 16. CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models
    Ying Ding (The University of Texas at Austin)
    02/10/2023, 11:40

    Abstract: Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology, has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases...

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  3. 20. Fokker-Planck Inverse Reinforcement Learning: A physics-constrained approach to Markov Decision Process models of cell dynamics
    Krishna Garikipati (University of Michigan)
    02/10/2023, 11:50

    In this short talk I will discuss our recent work on an approach to introducing connections between the Fokker-Planck equation and learning algorithms for dynamical systems that follow Markov Decision Processes

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  4. 24. AI Enabled Scientific Revolution
    Vipin Kumar (University of Minnesota)
    02/10/2023, 12:00

    There is an increasing consensus in the wider scientific community that AI is poised to disrupt science by unlocking entirely new approaches, driving new scientific inquiry, and enabling greater scientific leaps with far-reaching societal consequences. In addition, challenges unique to scientific problems offer an opportunity to dramatically advance AI. However, there are substantial barriers...

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  5. 26. Unified Knowledge Representation for Science
    Wei Wang (UCLA)
    02/10/2023, 12:10

    The vast amount of knowledge accumulated in various science disciplines has been traditionally maintained in a way that is difficult for AI systems to use, due to differences in formats, standards, and types. This makes it challenging to integrate and share knowledge across different domains and to use it to build intelligent systems. To address these challenges, there is a pressing need to...

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  6. 4. Neural operators: AI + Science
    Animashree Anandkumar (California Institute of Technology)
    02/10/2023, 12:20
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