We are pleased to announce the first workshop on machine learning in geometry and physics at the Tsinghua Sanya International Mathematics Forum, 11-15 June 2018.
The goal of the workshop is to explore how machine learning techniques can be applied in modern mathematics and theoretical physics. We intend to bring together a diverse set of experts whose research interests and expertise are of general interest for researchers who are trying to approach research problems in formal physics and mathematics from a data science perspective. Some of the topics the workshop intends to cover:
- String landscape from a data science perspective
- Holography, RG flows and deep learning
- Novel geometric relations from data mining
Machine learning is a powerful new tool for many scientific fields, and is already entering and affecting our daily lives. It allows us to uncover new, often highly non-linear, structures and relations buried in vast amounts of data. In order to apply this machinery, it is imperative to be able to formulate the problem under consideration into a data science question – hence data is the key for machine learning. This yields an explanation why this novel tool has been of very limited interest and use so far in more formal scientific fields like theoretical physics or pure mathematics. What has been missing so far in these formal fields has been a set of suitable questions which can be viewed from a data science perspective. However, recently it became clear that there are in fact such problems in theoretical physics and mathematics which can be approached from a data science point of view, showcasing the discovery potential of machine learning techniques. For instance, one of the hopes which is emerging is that hints for new relations or proofs can be discovered in a statistical sense, with the aid of modern data analysis techniques based on machine learning - that is, leading to a novel notion of "experimental" mathematics.
We hope that this workshop helps to accelerate the establishment of machine learning as a powerful new tool in research fields related to geometry and theoretical physics.
Tsinghua Sanya International Mathematics Forum
Sanya, Hainan, China
Participants are lodged in the center with full board. Please indicate any special food requirements during registration.
|Rak-Kyeong Seong||Tsinghua University|
|Shing-Tung Yau||Tsinghua University & Harvard University|
|Masato Taki||RIKEN Tokyo|
|Fernando Quevedo||Abdus Salam International Centre for Theoretical Physics, ICTP|
|Koji Hashimoto||Osaka university|
|Sotaro Sugishita||Japan Society for the Promotion of Science|
|Akio Tomiya||Central China Normal University|
|Jason Morton||Pennsylvania State University|
|Cedric Beny||Hanyang University|
|Dan Oprisa||Agoda Services Co., Ltd|
|Per Berglund||New Hampshire|
|Gary Shiu||University of Wisconsin-Madison|
|Peter Toth||Google DeepMind|
|Artem Lenskiy||Korea University of Technology and Education|
|Artur Garcia Saez||Barcelona SC center|
|Gregory Chirikjian||Johns Hopkins University|
|Yi-Zhuang You||UCSD & Harvard|
|Maciej Koch-Janusz||Swiss Federal Institute of Technology (ETH) Zurich|
|Zheng Sun||Sichuan University|
|Jing Chen||Flatiron Institute|
|Greg Yang||Microsoft Research|
|Adam Smith||University of Oxford|
|Hiroyuki Fujita||University of Tokyo|
|Daham Lee||Tsinghua University|
|Shi-Min Hu||Tsinghua University|
-- registration is closed --
As the venue only offers limited space, it is mandatory to apply to participate in the workshop (email: stringsml2018 - at - gmail.com).
Selected participants will receive an official invitation letter with further information regarding travel and visa requirements.
Daniel Krefl (CERN)
Rak-Kyeong Seong (Tsinghua University)
Shing-Tung Yau (Harvard University and Tsinghua University)
stringsml2018 - at - gmail.com