2021 ACM SIGKDD Election Results

2021 ACM SIGKDD Election Results

(For the term of 1 July 2021 – 30 June 2023)

Chair

Wei Wang
UCLA
Email: weiwang@cs.ucla.edu

Secretary-Treasurer

Hui Xiong
Rutgers University
Email: xionghui@gmail.com

Executive Committee

Mohammed J. Zaki
Rensselaer Polytechnic Institute
Email: zaki@cs.rpi.edu

Thorsten Joachims
Cornell

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2021 SIGKDD Doctoral Dissertation Award: Call for Nominations

2021 SIGKDD Doctoral Dissertation Award: Call for Nominations

This annual award by ACM SIGKDD recognizes outstanding research by doctoral candidates in the field of data science, data mining and knowledge discovery. The KDD Doctoral Dissertation Award winner and up to two runners-up will be recognized at the KDD conference, and their dissertations will have

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2020 SIGKDD Test of Time Award Winners

The SIGKDD Test of Time Award recognizes outstanding papers from past KDD Conferences beyond the last decade that have had an important impact on the data mining research community.

 

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ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining. 

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2020 SIGKDD Service Award: Michael Zeller

Michael Zeller, head of artificial intelligence (AI) strategy and solutions at Temask, is honored for his contributions to the field through dedication to ACM SIGKDD as the volunteer treasurer and secretary of the executive committee. Zeller has served on the executive board for eight years, playing an instrumental role in planning multiple KDD conferences. With a special emphasis on applied AI, his mission as an executive committee member is to foster strong partnerships between research institutions and industry organizations as a key for the continued success of the KDD community.

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2020 SIGKDD Innovation Award: Thorsten Joachims

Thorsten Joachims, professor of Computer Science and Information Science at Cornell University, is recognized for his research contributions in machine learning, including influential work studying human biases in information retrieval, support vector machines (SVM) and structured output prediction. Notably, Joachims pioneered methods for eliciting reliable preferences from implicit feedback, methods for unbiased learning-to-rank and ranking methods that provide fairness guarantees. The ACM SIGKDD Innovation Award is the highest honor for technical excellence in the field of knowledge discovery and data mining. It is conferred on an individual or group of collaborators whose outstanding technical innovations have greatly influenced the direction of research and development in the field.

 

“I am greatly honored by this recognition from the KDD community,” said Joachims. “KDD is known for innovation — not only as an academic endeavor but also with an eye towards real-world impact and social good.”

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Call for Nominations: ACM SIGKDD 2021 Innovation, Service, and Rising Star Awards

Call for Nominations: ACM SIGKDD 2021 Innovation, Service, and Rising Star Awards

ACM SIGKDD invites your nominations for its 2021 Innovation Award, Service Award, and Rising Star Award.

ACM SIGKDD, ACM’s Special Interest Group on Knowledge Discovery and Data Mining (KDD), is the premier global professional organization for researchers and professionals dedicated to the

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SIGKDD Executive Committee Call for Nominations

SIGKDD Executive Committee Call for Nominations

Dear SIGKDD members,

Hope everything is well during this extraordinary time. Time flies! The term of the current SIGKDD Executive Committee will end on June 30, 2021. According to the ACM rule, we need to prepare for the election early next year. The first step is to get a slate of capable

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ACM SIGKDD 2020 Awards Call for Nominations

ACM SIGKDD 2020 Awards Call for Nominations

ACM SIGKDD invites your nominations for its 2020 Innovation Award, Service Award, Rising Star Award, and Test of Time Award.

ACM SIGKDD, ACM’s Special Interest Group on Knowledge Discovery and Data Mining (KDD), is the premier global professional organization for researchers and professionals

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