Fujitsu Labs presents a workshop on Topological Data Analysis and Beyond at the renowned NeurIPS2020 conference on AI

Workshop Overview

• Name

  Topological Data Analysis and Beyond

• Date

  December 11, 2020, from 8am PST

• Location

  Online

• URL

  https://tda-in-ml.github.io/

• Organizers

  Yuhei Umeda (Research Manager, FLL), Frédéric Chazal (INRIA Saclay – Île-de-France), Bastian Rieck(ETH Zurich), Smita Krishnaswamy (Yale University), Roland Kwitt (University of Salzburg), Karthikeyan Natesan Ramamurthy (IBM Research), Guy Wolf (Université de Montréal & MILA)

NeurIPS is the largest leading conference in the field of AI, where workshops are held to present and discuss current hot topics and promising technology areas. This year, amongst 167 proposals, a workshop on TDA was selected as one of 60 workshops to be held at NeurIPS.
　Modern and complex data can often be represented as mathematical shapes with interesting topological or geometric properties. Identifying, extracting, and utilizing the relevant topological and geometric features that underlie data is a problem of significant importance. The mathematical field of TDA arose in response to this challenge, and is now garnering increasing interest from both academia and industry. In the domain of machine learning (including deep learning), technologies of this past decade have made remarkable progress and are being applied in various business domains. As feature engineering remains important to the pursuit of good performance, advances in TDA have demonstrated promise when combined with other AI/ML approaches, opening new theoretical and applied research directions.

　Fujitsu Laboratories has been collaborating with Inria, France on TDA research since 2016. We are organizing this workshop in collaboration with our research partner Prof. Chazal of Inria.
　The purpose of this workshop is to bring together researchers and practitioners to discuss recent methodological and technical advancements in TDA and to share feedback on using TDA in AI/ML applications. In addition, this workshop aims to make connections of knowledge between various communities, especially the AI, Machine Learning, Deep Learning, and Applied Math communities. By presenting some new recent applications, we will discuss the potential of TDA as a toolkit for active researchers in the fields of data science and machine learning.

Inquiries regarding this matter

fj-tda-tss@dl.jp.fujitsu.com

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