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国立情報学研究所講演会「Data Science: Is it Real?」

スタンフォード大学名誉教授のジェフリー・デイヴィッド・ウルマン（Jeffrey David Ullman）氏の招へいを機に講演会「Data Science: Is it Real?」を開催いたします。

データベース理論研究の第一人者であるウルマン教授は、オートマトンや言語理論などコンピュータ科学の基礎理論に関する多数の著書でも知られ、Edgar. F Codd Innovations Award、Knuth賞、IEEEフォン・ノイマンメダル、NEC C&C賞など多数の賞を受賞されています。

ウルマン教授のご講演を聴く貴重なチャンスです。是非ご参加下さい。

なお、講演は全て英語で行われます（通訳はありません）。参加費は無料ですが、お席に限りがありますので下記フォームからのお申込が必要です。

日時

2月19日（月）17:00-18:15

会場

TKPガーデンシティPREMIUM神保町（千代田区神田錦町3-22 テラススクエア3F ）
https://www.kashikaigishitsu.net/facilitys/gcp-jimbocho/access/

講師

Jeffrey David Ullman
スタンフォード大学名誉教授

Jeffrey Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), and the NEW C&CFoundation Prize (2017).

He is the author of 16 books, including books on database systems, compilers, automata theory, and algorithms.

プログラム

Opening remarks
Director General, National Institute of Informatics
Masaru Kitsuregawa

Data Science: Is it Real?
Stanford W. Ascherman Professor of Computer Science (Emeritus)
Jeffrey Ullman

We shall discuss the various ways in which data science is approached by different communities, including the Statistics, Machine-Learning, and Database communities. Each presents a different viewpoint and values different outcomes. Some consequences of these approaches will be discussed. We also contrast approaches to education of the large number of data scientists that are expected to be required in the near future.