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

2–3 Oct 2023
US/Eastern timezone

Contribution List

39 out of 39 displayed
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  1. 1. NSF Leadership
    Michael Littman (NSF CISE/IIS)
    02/10/2023, 11:00

    NSF directors
    Margaret Martonosi (Assistant Director, CISE)
    Michael Littman (Division Director, CISE/IIS)
    Nina Amla (Senior Science Advisor, CISE)
    <a...

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  2. 2. Workshop organizers
    Aidong Zhang (University of Virginia), Shih-Chieh Hsu (University of Washington Seattle (US))
    02/10/2023, 11:20
  3. 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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  4. 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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  5. 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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  6. 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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  7. 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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  8. 4. Neural operators: AI + Science
    Animashree Anandkumar (California Institute of Technology)
    02/10/2023, 12:20
  9. 9. Breakout1: AI for data-driven discovery
    02/10/2023, 13:00

    Room1 Eric Toberer (Moderator) Anuj Karpatne (Scribe) note1

    Room2 Xinghua Mindy Shi (Moderator) Wei Ding (Scribe) note2

    Room3 Jianwu Wang (Moderator) Paul Hanson...

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  10. 28. Room1
    Eric Toberer (Colorado School of Mines)
    02/10/2023, 14:00
  11. 29. Room2
    Xinghua Mindy Shi (Temple University)
    02/10/2023, 14:07
  12. 30. Room3
    Jianwu Wang (University of Maryland, Baltimore County)
    02/10/2023, 14:13
  13. 22. From Harnessing the Data Revolution to Harvesting the Data Revolution
    Philip Coleman Harris (Massachusetts Inst. of Technology (US))
    02/10/2023, 15:00

    Developments in modern computation and instrumentation have led to the possibility of recording enormous amounts of data, the data revolution. Along with this incredible data flow, a new demand has emerged for algorithms that can run on all this data to “Harness the Data Revolution.” Large datasets are rapidly encompassing many scientific domains, including high-energy physics, Astronomy,...

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  14. 14. Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS)
    Daniel Angles-Alcazar (University of Connecticut)
    02/10/2023, 15:10

    The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project aims to overcome major obstacles limiting our understanding of the fundamental properties of the Universe by (1) providing thousands of state-of-the-art hydrodynamic simulations of cosmological structure formation covering a broad range of sub-grid models for the physics of galaxy formation and (2) developing...

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  15. 19. Foundation Models for Science: What happens when you train large (language) models for Science?
    Shirley Ho (Flatiron Institute)
    02/10/2023, 15:20

    In recent years, the fields of natural language processing and computer vision have been revolutionized by the success of large models pretrained with task-agnostic objectives on massive, diverse datasets. This has, in part, been driven by the use of self-supervised pretraining methods which allow models to utilize far more training data than would be accessible with supervised training. These...

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  16. 21. Using domain-aware metrics for deploying AI/ML in weather/climate applications
    Dr Peetak Mitra (Excarta)
    02/10/2023, 15:30

    Conventional AI/ML metrics (such as RMSE) for optimization often do not translate well for weather/climate-specific applications including for energy grid management, or modeling key physical prognostics that are driven by an underlying dynamical process. In this short talk, we will explore the importance of using domain-aware metrics for model training, post-training evaluation and eventual...

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  17. 23. Accelerating AI Applications in Environmental Sciences
    Yuhan "Douglas" Rao (Cooperative Institute for Satellite Earth System Studies/NOAA National Centers for Environmental Information)
    02/10/2023, 15:40

    In this lightning talk, we will provide an overview of NOAA Center for AI's approach to foster an open community discussion that gather members from academic researchers, industry leaders, and government researchers and managers around the topics of AI development in environmental sciences. Since 2022, the annual NOAA AI workshop transitioned into an open community forum where all interested...

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  18. 27. Workshop on Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges
    Dr L. Ruby Leung (Pacific Northwest National Laboratory)
    02/10/2023, 15:50

    This presentation briefly summarizes a workshop convened by the National Academies of Sciences, Engineering, and Medicine on February 7, 10, and 11, 2022, on the opportunities and challenges of using ML/AI to advance Earth system science, including their ethical development and use. The workshop explored how ML/AI approaches can contribute to improving understanding, analysis, modeling,...

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  19. 3. Breakout 2: Science and Engineering for data-driven discovery
    02/10/2023, 16:10

    Room1 Edgar Lobaton (Moderator) Anuj Karpatne (Scribe) Note1

    Room2 Bedrich Benes (Moderator) Wei Ding (Scribe) Note2

    Room3 Phil Harris (Moderator) Paul Hanson...

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  20. 31. Room1
    Edgar Lobaton (North Carolina State University)
    02/10/2023, 17:10
  21. 32. Room2
    Bedrich Benes (Purdue University)
    02/10/2023, 17:16
  22. 33. Room3
    Philip Coleman Harris (Massachusetts Inst. of Technology (US))
    02/10/2023, 17:22
  23. 17. NSF leadership
    David Manderscheid (NSF MPS/DMS)
    03/10/2023, 11:00
  24. 37. ACED workshop
    Christopher Yang (NSF CISE/IIS)
    03/10/2023, 11:15
  25. 15. NSF-NIH Joint Workshop on Emerging AI in Biology
    Carl Kingsford (Carnegie Mellon University)
    03/10/2023, 11:20

    New techniques in AI are rapidly being developed, extended and applied to challenging problems in biology. At the same time, as new assays, new data efforts, and greater understanding is developed in biology, the class and scope of problems that are amendable to AI approaches is growing. In order to survey the current frontier of the interface between AI methodology and biology and to chart...

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  26. 13. The Annual Accelerate Conference
    Brandon Sutherland (Acceleration Consortium)
    03/10/2023, 11:30

    In this short lightning talk I will discuss the Acceleration Consortium's annual Accelerate conference, which we ran in 2022 and 2023 in Toronto and are in the early stages of planning 2024 in a different host city. Accelerate spans the entire field of accelerated discovery with AI and automation: computational tools, high-throughput and autonomous experimentation, the ethics of accelerated...

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  27. 18. Pandemic Research for Preparedness and Resilience
    Prof. Madhav V Marathe (University of Virginia)
    03/10/2023, 11:40

    A Research Roadmap for the Next Pandemic PREPARE (Pandemic Research for Preparedness and Resilience) is an NSF CISE-sponsored virtual organization tasked with fostering research collaborations and synthesizing critical pandemic-related computing research into a roadmap to help inform NSF funding opportunities that will aid our nation’s effective response to the next pandemic. Since we started...

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  28. 25. The Frontiers of Artificial Intelligence-Empowered Methods and Solutions to Urban Transportation Challenges
    Lili Du (University of Florida)
    03/10/2023, 11:50

    With the quickly growing quantity and variety of transportation data, Artificial intelligence (AI) technologies are revolutionizing transportation research from system management to automated vehicle and infrastructure control. Emerging AI technologies combined with other analytical methods will lead to improved scientific understandings, transformative methods, and innovative, proactive...

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  29. 6. AI + Computation: Use inspired challenges from Manufacturing and Design
    Baskar Ganapathysubramanian (Iowa State University)
    03/10/2023, 12:00

    Baskar Ganapathysubramanian (Iowa State)

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  30. 39. Breakout3 topics introduction
    Aidong Zhang (University of Virginia)
    03/10/2023, 12:10
  31. 10. Breakout 3: Science and Engineering for data-driven discovery
    03/10/2023, 12:20

    Each room picks 2 or 3 topics to discuss Barrier, Challenge, Opportunities and Recommendations.

    Room1 AI-advanced Science & Science-informed AI: Xia Ning (Moderator) Wei Ding (Scribe) note1

    Room2 LLM and Continuous ML: Jing Gao (Moderator) Joshua Agar (Scribe) <a...

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  32. 35. Room1
    Xia Ning (Ohio State University)
    03/10/2023, 13:25
  33. 34. Room2
    Jing Gao (University of Delaware)
    03/10/2023, 13:32
  34. 38. Room3
    Philip Coleman Harris (Massachusetts Inst. of Technology (US))
    03/10/2023, 13:39
  35. 36. Room3
    Nirav Merchant (University of Arizona)
    03/10/2023, 13:46
  36. 8. Synergies between Accelerating Computer-Enabled Discovery topics
    Aidong Zhang (University of Virginia), Marti Hearst, Mingyi Hong (University of Minnesota, Minneapolis), Omar Gheta (The University of Texas at Austin), Rajagopalan Balaji (University of Colorado Boulder), Shih-Chieh Hsu (University of Washington Seattle (US))
    03/10/2023, 14:30

    Moderator: Jennifer Dy (NEU)
    HCI: Marti Hearst (Berkeley)
    Data, AI and Machine Learning: Aidong Zhang (UVA), Shih-Chieh Hsu (UW)
    Digital Twins: Omar Ghattas (UTexas)
    Smart Sensing and Analytics: Mingyi Hong (UMichigan)
    Rigorous & Reproducible Reasoning: Rajagopalan Balaji (Colorado)
    Programmable/Self-Driving Labs: TBC

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  37. 7. Writing breakout sessions
    03/10/2023, 15:30

    Room1 AI-advanced Science & Science-informed AI: Wei Ding (Moderator)
    Room2 LLM and Continuous ML: Joshua Agar (Moderator)
    Room3 Explainable and Robust AI: Anuj Karpatne (Moderator)
    Room4 Education & Outreach, Community and Cyberinfrastructure: Paul Hanson (Moderator)

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  38. 11. Closing
    Christopher Yang (NSF)
    03/10/2023, 17:00
  39. 5. Lightning talks (8mins each)
    Lai-Yung Ruby Leung (PNNL)

    Lai-Yung (Ruby) Leung (PNNL)

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