Hammers & Nails 2023 - Swiss Edition

Session

Invited speakers

30 Oct 2023, 09:30

Congressi Stefano Franscini (CSF)

1. Highlights of machine learning in particle physics for computer scientists

Loukas Gouskos (CERN)

30/10/2023, 09:30

2. Interdisciplinary Machine Learning for Fundamental Physics

Mariel Pettee (Lawrence Berkeley National Lab. (US))

30/10/2023, 11:00

6. Geometric Algebra Transformers: A Universal Architecture of Geometric Data

Taco Cohen

30/10/2023, 14:00

7. AI & material science

Kostya Novoselov

31/10/2023, 09:00

8. Machine learning: bridging scale gap between the worlds of materials and particles

Andrey Ustyuzhanin

31/10/2023, 10:30

5. Reduction and Closure of Dynamical Systems using Deep Learning

Qianxiao Li

31/10/2023, 11:15

14. The Vision of End-to-End ML models in HEP

Lukas Alexander Heinrich (Technische Universitat Munchen (DE))

01/11/2023, 10:30

58. Toward Building Large HEP Models with Self-Supervised Learning

Michael Kagan (SLAC National Accelerator Laboratory (US))

01/11/2023, 11:20

25. Simulation-based inference and the places it takes us

Jakob Macke

01/11/2023, 13:15

16. Generative Models - The Key to Manipulating Implicit Distribution for Bayesian Inference

Francois Lanusse

01/11/2023, 14:30

26. Strong Lensing Data Analysis in the Era of Large Sky Surveys

Laurence Levasseur

01/11/2023, 16:15

10. Image Denoising - Not What You Think

Michael Elad

01/11/2023, 17:30

13. Machine learning for the LHC Simulation Chain

Ramon Winterhalder (UC Louvain)

02/11/2023, 09:00

34. Bigger data, shorter time: Real-time inference on specialised hardware for scientific discovery

Thea Aarrestad (ETH Zurich (CH)), Thea Aarrestad

02/11/2023, 10:45

4. Aspects of Deep Learning in Particle Flow

Eilam Gross, Etienne Dreyer (Weizmann Institute of Science (IL))

02/11/2023, 12:00

69. Learning Image Representations Without Manual Annotations and Related Applications

Piotr Bojanowski

02/11/2023, 14:00

70. Calibrated uncertainty quantification in simulator-based inference

Ann Lee (Carnegie Mellon University)

02/11/2023, 15:45

71. Foundation models for science

Shirley Ho

02/11/2023, 17:00

17. Open problems in generative models

Barnabas Poczos

03/11/2023, 09:00

18. Normalizing flows, diffusion and annealing importance sampling

Alexander Matthews

03/11/2023, 10:45

3. Learning Image Representations Without Manual Annotations and Related Applications

Piotr Bojanowski

68. Learning Image Representations Without Manual Annotations and Related Applications

Piotr Bojanowski

12. Strong Lensing Data Analysis in the Era of Large Sky Surveys

Laurence Levasseur

11. TBC