Counting String Standard Models

• Andre Lukas (University of Oxford)

Machine Learning Calabi-Yau Volumes

• Rak-Kyeong Seong (Tsinghua University)

Machine learning in astrophysics & cosmology

• Ben Hoyle (MPE/USM Munich)

I present an overview of machine learning methods as they are applied in astrophysics and cosmology, and highlight both new developments and real problems that are facing the field.

Topological Data Analysis for Cosmology and String Theory

• Gary Shiu (University of Wisconsin-Madison)

The Early Universe Information Bottleneck

• Jonathan Frazer (DESY)

Classification of Quasi-Realistic Heterotic String Vacua

• Glyn Harries (University of Liverpool)

Data in (F-theory) flux

• Timo Weigand (CERN)

Data creation with Generative Adversarial Networks

• Fabian Ruehle (University of Oxford)

Big Data and Machine Learning in the Heterotic Orbifold Landscape

• Patrick Vaudrevange (TU Munich)

Deeping-learning the Landscape

• Yang-Hui He (City, University of London, Merton College, Oxford & Nankai University)

Applications of Machine Learning techniques at the ATLAS Collaboration

• David Handl (LMU Munich)

Classifying fibration structures in Calabi-Yau constructions

• James Gray (Virginia Tech)

Learning the non-Higgsable gauge groups in 4D F-theory

• Yinan Wang (MIT)

Persistent Homology and Flux Vacua

• Alex Cole (University of Wisconsin-Madison)

Axion hierarchies from Kahler cones

• Liam McAllister (Cornell University)

Vacuum selection from Cosmology on Networks of String Vacua

• Cody Long (Northeastern University)

Anomaly Detection: Mapping the IIB Lamppost with Reinforcement Learning

• James Halverson (Northeastern University)

Machine learning at Belle II

• James Kahn (The University of Melbourne)

Landscape and Complexity Catastrophe & Report on developments in mathematical software

• Michael Douglas (Stony Brook University)

Challenging the Refined Swampland Distance Conjecture in Calabi-Yau Moduli Spaces

• Daniel Klaewer (Max Planck for Physics)

History and perspectives

• Bert Schellekens (Nikhef)

Discussion

WG1: Datasets in String Theory

• Cody Long (Northeastern University)
• Christoph Mayrhofer (LMU Munich)

WG2: Quo vadis string landscape?

• Liam McAllister (Cornell University)
• Timo Weigand (CERN)

WG3: Machine learning tools for string theory

• James Halverson (Northeastern University)
• Fabian Ruehle (University of Oxford)