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Accelerating Physics with ML@MIT

28 January 2023 to 2 February 2023
US/Eastern timezone

Contribution List

42 out of 42 displayed
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  1. 18. Workshop goals
    Philip Coleman Harris (Massachusetts Inst. of Technology (US))
    30/01/2023, 09:00
  2. 19. Inference-as-a-service in high energy physics
    William Patrick Mccormack (Massachusetts Inst. of Technology (US))
    30/01/2023, 09:30
  3. 20. Inference-as-a-service in gravitational-wave physics
    Alec Gunny, Alec Gunny
    30/01/2023, 10:00
  4. 21. Searches for compact binary coalescences-I
    Damon Beveridge, Damon Beveridge
    30/01/2023, 11:00
  5. 22. Searches for compact binary coalescences-II
    Ethan Marx (MIT), Ethan Marx
    30/01/2023, 11:15
  6. 23. Denoising methods-I
    Saleem Muhammed
    30/01/2023, 11:30
  7. 24. Denoising methods-II
    Dr Chia-Jui Chou (National Yang Ming Chiao Tung University), Chia-Jui Chou
    30/01/2023, 11:45
  8. 25. Parameter estimation methods
    Maximillian Dax
    30/01/2023, 12:00
  9. 16. The Challenges of Identification, Classification and Inference for Time-domain Astrophysics with Big Data
    Ashley Villar
    30/01/2023, 14:00
  10. 17. Multi-messenger astronomy infrastructure for optical follow-ups
    Konstantin Malanchev
    30/01/2023, 14:30
  11. 29. Challenges for identifying and following-up the next kilonova
    Niharika Sravan
    30/01/2023, 15:00
  12. 9. Software Infrastructure Challenges for IaaS with HEP (20'+10')
    Kevin Pedro (Fermi National Accelerator Lab. (US))
    30/01/2023, 16:00
  13. 10. SONIC Inference as a Service for Point Clouds
    Yongbin Feng (Fermi National Accelerator Lab. (US))
    30/01/2023, 16:35
  14. 11. ML GPU deployments for Neutrino Reconstruction
    Tingjun Yang (Fermi National Accelerator Lab. (US))
    30/01/2023, 17:00
  15. 12. Anomaly detection for EM
    Daniel Muthukrishna (MIT)
    31/01/2023, 09:00
  16. 13. Anomaly detection in GW
    Ryan Raikman (Carnegie Mellon University)
    31/01/2023, 09:20
  17. 14. MLY Pipeline
    Vasileios Skliris (Cardiff University)
    31/01/2023, 09:40
  18. 15. Anomaly Detection in HEP
    Katya Govorkova (Massachusetts Inst. of Technology (US))
    31/01/2023, 10:00
  19. 26. Point-Voxel CNN
    Zhijian Liu
    31/01/2023, 11:00
  20. 27. Sparse GPU implementations
    Haotian Tang
    31/01/2023, 11:30
  21. 28. Computing at the Massachusetts Green High Performance Center
    Chris Hill
    31/01/2023, 12:00
  22. 36. ML Workflows on NSERC
    Steven Farrell (Lawrence Berkeley National Laboratory)
    31/01/2023, 14:00
  23. 39. ML Algorithms at SDSC/NRP
    Frank Wurthwein (UCSD)
    31/01/2023, 14:30
  24. 37. HPC Discussion
    31/01/2023, 14:50
  25. 30. Machine learning applications in Icecube
    Josh Peterson
    01/02/2023, 09:00
  26. 31. Pointing to a Supernova
    Kate Scholberg
    01/02/2023, 09:20
  27. 32. Binary neutron star early warning detection using ML
    Alistair Mcleod
    01/02/2023, 09:40
  28. 33. Localization and waveform extraction for compact binary coalescences
    Chayan Chatterjee
    01/02/2023, 09:55
  29. 34. Unsupervised Learning for TESS Variable Stars
    Jeroen Audenaert
    01/02/2023, 10:10
  30. 38. Contextualized machine learning for applied data science
    Ben Lengerich
    01/02/2023, 10:30
  31. 40. HEP
    William Patrick Mccormack (Massachusetts Inst. of Technology (US))
    01/02/2023, 11:30
  32. 41. GW
    Deep Chatterjee, Deep Chatterjee, Deep Chatterjee
    01/02/2023, 11:50
  33. 42. EM Astronomy
    Daniel Muthukrishna, Daniel Muthukrishna, Daniel Muthukrishna (MIT)
    01/02/2023, 12:10
  34. 1. LIGO O4 Hackathon
  35. 2. LIGO O4 Hackathon
  36. 6. LIGO Run O4 Hackathon
  37. 7. LIGO Run O4 Hackathon
  38. 8. test
    Philip Coleman Harris (Massachusetts Inst. of Technology (US))
  39. 35. Unsupervised Learning for TESS Variable Stars
    Jeroen Audenaert
  40. 4. Workshop
  41. 3. Workshop-- Hackthon session #1
    Alec Gunny (MIT)

    Work on ML infrastructure and code deployment and debugging

    Go to contribution page
  42. 5. Wrap up