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Fast Machine Learning for Science Conference 2024

15โ€“18 Oct 2024
Purdue University
America/Indiana/Indianapolis timezone

Session

Invited talks

15 Oct 2024, 09:10
Steward Center 306 (Third floor) (Purdue University)

Steward Center 306 (Third floor)

Purdue University

128 Memorial Mall Dr, West Lafayette, IN 47907

Description

invited talks 30 mins + 5 mins Q&A

Presentation materials

There are no materials yet.

  1. 2. [Remote] Opening Talk
    Javier Mauricio Duarte (Univ. of California San Diego (US))
    15/10/2024, 09:10
  2. 3. Fast Machine Learning at the LHC
    Dylan Sheldon Rankin (University of Pennsylvania (US))
    15/10/2024, 09:45
  3. 37. Enabling real-time detection, characterization and inference for the time-domain sky
    Gautham Narayan (UIUC)
    15/10/2024, 10:20
  4. 36. [Remote] Machine learning and electronic structure calculation in materials and beyond
    Kieron Burke
    15/10/2024, 11:20

    I will briefly outline the huge importance of density functional theory (DFT) calculations
    to modern materials design (and to chemistry and warm dense matter, etc). I will then
    discuss the impact of machine learning on the field, especially the rise of machine-learned
    potentials. I will briefly mention my own work in using ML to improve DFT.

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  5. 32. ML for Accelerator control and design
    Daniel Ratner (SLAC)
    15/10/2024, 11:55
  6. 38. Topological diagnostics of ML and AI algorithms
    David Gleich (Purdue)
    15/10/2024, 12:30

    It is now standard practice across science to use models that have been trained, fit, or learned based on a set of data. Many of these models involve a large number of parameters that make direct interpretation of the model challenging and a near black-box model view appropriate. We explore the possibilities of using ideas based on topological analysis methods to understand and evaluate these...

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  7. 33. Deep Learning Complexity in Neuromorphic Quantum Materials
    Erica Carlson
    16/10/2024, 11:10

    Spatially resolved surface probes have recently revealed rich electronic textures at the nanoscale and mesoscale in many quantum materials. Rather than transitioning from insulator to metal all at once, VO2 forms an intricate network of metallic puddles that extend like filigree over a wide range of temperatures. We developed a convolutional neural network to harvest information from both...

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  8. 34. [Remote] P-bits (quantum-inspired probabilistic computing)
    Supriyo Datta (Purdue University)
    16/10/2024, 11:45
  9. 39. Tools, Methodologies, and Co-design Principles for Building Microelectronics Artifacts for ML
    Seda Ogrenci (Northwestern University)
    16/10/2024, 14:00
  10. 139. [Remote] ML for material science
    Sergei Kalilin
    16/10/2024, 14:35
  11. 13. Agile Hardware Development: Architecture and Tools
    Callie Hao
    17/10/2024, 09:00
  12. 9. LLMs for chip design
    Siddharth Garg
    17/10/2024, 09:35
  13. 40. Industry talk: AMD
    Abhishek Jain
    17/10/2024, 10:10
  14. 45. AI and ML at the future Electron Ion Collider
    Cristiano Fanelli (William & Mary)
    17/10/2024, 11:10

    The Electron Ion Collider (EIC) promises unprecedented insights into nuclear matter and quark-gluon interactions, with advances in artificial intelligence (AI) and machine learning (ML) playing a crucial role in unlocking its full potential. This talk will explore potential opportunities for AI/ML integration within the EIC program, drawn from broader discussions in the AI4EIC forum. I will...

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  15. 44. Real-time ML-FPGA filter for particle identification and tracking in nuclear physics
    Sergey Furletov (Jefferson lab)
    17/10/2024, 11:45
  16. 29. Invited talk 5
  17. 43. ML for material science
    Sergei Kalilin (UTK)
  18. 1. Welcome
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