国際シンポジウム ISPI2024 のご案内

[髙橋昂]

ibismlのみなさま

東大知の物理学研究センターの髙橋昂と申します。

同所属の樺島祥介教授の代理で、以下の国際シンポジウムの案内を投稿いたします。

どうぞよろしくお願いいたします。

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国際シンポジウム開催のお知らせ

東京大学知の物理学研究センターでは
来る11月6日〜8日に東京大学小柴ホールにて
下記の国際シンポジウムを開催致します。
参加費は無料です（＃懇談会への参加は有料です）。
皆様のご参加、ポスター発表へのお申し込みをお待ちしております。

【参加登録・ポスター発表申し込みフォーム】
https://forms.gle/GaEHSFjrHCKfg41XA

事務手続きの都合上、参加登録・ポスター発表申し込みの
締め切りは

2024年 9月30日（月）

とさせていただきます。
ただし、会場定員に達し次第、それ以前でも締め切りますので
お早めの登録・申込へのご協力、よろしくお願い申し上げます。

ISPI2024 組織委員

Announcement of the International Symposium

Institute for Physics of Intelligence, The University of Tokyo will hold the following international
symposium at Koshiba Hall, The University of Tokyo, from November 6th to 8th.

Participation is free (Participation in "Free Discussion" is charged).

We look forward to your participation and poster presentation applications.

[Participant registration and poster presentation application form]
https://forms.gle/GaEHSFjrHCKfg41XA

Due to administrative procedures, the deadline for participant registration and poster presentation applications will be

Monday, September 30, 2024.

However, applications will be closed before that date as soon as the venue capacity is reached,
so we appreciate your cooperation in registering and applying early.

ISPI2024 Organizing Committee

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DAIKIN International Symposium on Physics of Intelligence
-- Statistical Mechanics and Machine Learning: A Powerful Combination for Data Analysis --
ISPI2024: Nov. 6-8, 2024 @Koshiba Hall, The University of Tokyo
Web Page: https://www.phys.s.u-tokyo.ac.jp/about/41096/

【Objective】　
The goal of machine learning is to extract underlying regularities from training data.
The objective is no different from that of statistics, which has been developed for 200 years since Laplace.
However, in machine learning, the probabilistic models used to extract regularities are nonlinear and have
much higher degrees of freedom than previous statistical models. This leads to new challenges, such as
the difficulty of computation and the difficulty of performance evaluation in the data analysis. On the other
hand, statistical mechanics has greatly developed techniques for dealing with large-degree-of-freedom
nonlinear statistical models through the study of gases and magnetic materials. This suggests that statistical
mechanics may be useful for solving the new challenges posed by the birth of machine learning. This
symposium aims to bring together researchers in data analysis, machine learning, and statistical mechanics
to exchange their expertise.

【Registration & Application of Poster Presentation】　
https://forms.gle/GaEHSFjrHCKfg41XA

【Speakers】　　　　　　　　　　　　　　　　　　　　　　　　　
*plenary talk
※ In alphabetical order
SueYeon Chung  New York University (NYU) USA
Jorn Dunkel             Massachusetts Institute of Technology (MIT) USA
Alexander Hoffmann      University of California, Los Angeles (UCLA) USA
Sosuke Ito              The University of Tokyo (UTokyo) JPN
Shinpei Kawaoka         Tohoku University/Kyoto University JPN
Takeshi Kawasaki        Nagoya University JPN
Nobuyasu Koga    Osaka University JPN
Jian Ma Carnegie        Mellon University (CMU) USA
Hiroshi Makino  Nanyang Technological University (NTU) SGP
Marc Mézard *   Università Bocconi ITA
Daiki Nishiguchi        UTokyo JPN
Mor Nitzan              Hebrew University of Jerusalem (HUJI) ISR
Mariko Okada            Osaka University JPN
Cengiz Pehlevan         Harvard University USA
Gautam Reddy    Harvard University USA
Sunghan Ro              MIT USA
Yasushi Sako            Riken JPN
Kaoru Sugimura  UTokyo JPN
Shinsuke Uda            Yamaguchi University JPN
Vincenzo Vitelli        The University of Chicago USA
Lei Wang                Chinese Academy of Sciences (CAS) CHN
Matthiew Wyart  École polytechnique fédérale de Lausanne (EPFL) CHE
Sho Yaida Meta  USA
Hajime Yoshino  Osaka Univeristy JPN
Francesco Zamponi       Sapienza University ITA

【ISPI2024 Organizing Committee】
Yoshiyuki Kabashima
Kazumasa A. Takeuchi
Kyogo Kawaguchi

【Sponsors】
DAIKIN INDUSTRIES, LTD.
Institute for Physics of Intelligence, The University of Tokyo
JST JPMJCR1912 "Deciphering intracellular phenomena through information flow”
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