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08:30
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Registration
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09:30
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Welcome
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09:35
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Session 1
-
Florencia Canelli
(University of Zurich (CH))
(until 10:55)
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09:35
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Experimental View on Deep Learning for Neutrino experiments
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Davide Sgalaberna
(ETH Zurich (CH))
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10:15
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Algorithmic View on Deep Learning for Accelerator Neutrino Experiments
- Dr
Saul Alonso Monsalve
(ETH Zurich)
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10:55
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Coffee break
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11:25
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Session 2
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Marina Krstic Marinkovic
(ETH Zurich)
(until 12:45)
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11:25
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Machine Learning in Lattice Field Theory
-
William Detmold
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12:05
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Diffusion Models for Lattice Field Theory
- Prof.
Gert Aarts
(Swansea University)
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09:00
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Session 5
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Thomas Gehrmann
(Univ. Zurich)
(until 10:20)
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09:00
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Variance reduction with Normalizing Flows - Application to NNLO QCD phase space integration
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Rene Poncelet
(IFJ PAN Krakow)
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09:40
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Learning reliable amplitude surrogates
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Nina Elmer
(University of Cambridge)
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10:20
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Coffee break
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10:50
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Session 6
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Gino Isidori
(University of Zurich (CH))
(until 12:10)
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10:50
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Neural Network Tools for Amplitudes
- Mr
Daniel Pierre Maitre
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11:30
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Quantum computation of perturbative QFTs
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Herschel Chawdhry
(Florida State University)
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09:00
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Session 9
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Marcelle Soares-Santos
(University of Zürich)
(until 10:20)
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09:00
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Can AI Enable Next Breakthroughs in Large-Scale Structure Cosmology?
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Tomasz Kacprzak
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09:40
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Simulation-based inference for gravitational-wave data analysis
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Konstantin Leyde
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10:20
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Coffee break
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10:50
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Session 10
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Nicola Serra
(University of Zurich (CH))
(until 12:10)
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10:50
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On Detector Design with Differential Methods
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Jan Kieseler
(KIT - Karlsruhe Institute of Technology (DE))
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11:30
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On Detector Design with Artificial Intelligence
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Shah Rukh Qasim
(University of Zurich (CH))
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12:45
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Lunch
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14:10
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Session 3
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Nicola Serra
(University of Zurich (CH))
(until 15:30)
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14:20
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Optimal Transport: from A to B...and beyond
- Prof.
Alessio Figalli
(ETH)
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15:00
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Data to theory: how to fuel BSM model building with anomaly detection
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Maurizio Pierini
(CERN)
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15:30
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Coffee break
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16:00
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Session 4
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Thomas Gehrmann
(Univ. Zurich)
(until 17:20)
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16:00
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Particle flow reconstruction with a learnable, differentiable, efficient ML mode
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Joosep Pata
(National Institute of Chemical Physics and Biophysics (EE))
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16:40
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On Deep Full Event Reconstruction
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Julian Garcia Pardinas
(Massachusetts Inst. of Technology (US))
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12:10
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Lunch
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13:30
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Session 7
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Thea Aarrestad
(ETH Zurich (CH))
(until 15:30)
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13:30
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On Foundation models for Fundamental Science [Title to be confirmed]
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Michael Kagan
(SLAC National Accelerator Laboratory (US))
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14:10
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OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks and beyond
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Vinicius Massami Mikuni
(Lawrence Berkeley National Lab. (US))
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14:50
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AI for Discovery
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Gregor Kasieczka
(Hamburg University (DE))
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15:30
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Coffee break
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16:00
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Session 8
-
Javad Komijani
(ETH Zurich)
(until 17:20)
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16:00
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Uncertainty Quantification in PDF Determinations
-
Luigi Del Debbio
(The University of Edinburgh (GB))
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16:40
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Unfolding, Precision, and Machine Learning
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Kyle Cormier
(CERN)
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18:00
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Dinner
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