SAN DIEGO, Aug. 13, 2020 /PRNewswire/ -- KDD 2020, the premier interdisciplinary conference in data science, today announced the recipients of the 2020 ACM SIGKDD Awards for exemplary individuals and research teams in data science, machine learning, big data and artificial intelligence. Ahead of the organization's annual conference on Aug. 23-27, the awards recognize those who have made a lasting impact in the industry as a whole.
"Since the inception of the conference 26 years ago, research conducted by the SIGKDD community and presented at KDD conferences has made a lasting impact in academia and industry and the lives of billions of global citizens," said Dr. Jian Pei, chair of ACM SIGKDD and professor of Computing Science at Simon Fraser University. "The outstanding scientists honored today are recognized not only for their advancements in a specialized field but for their significant contributions to the world."
ACM SIGKDD Innovation Award
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."
ACM SIGKDD Service Award
Michael Zeller, head of artificial intelligence (AI) strategy and solutions at Temasek, 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. The ACM SIGKDD Service Award is the highest recognition of service awarded in the field. The award honors an individual or group of collaborators for outstanding contributions to professional KDD societies or society-at-large through applications of knowledge discovery and data mining.
"As a longtime member of ACM SIGKDD, I am always incredibly impressed by the contributions of our volunteers," said Zeller. "Without their dedication and belief in our mission, we would never have been able to create such a vibrant data science community, let alone organize a conference of this magnitude and quality year after year."
ACM SIGKDD Dissertation Award
Rediet Abebe, incoming assistant professor of Computer Science at the University of California at Berkeley, earned this year's ACM SIGKDD Dissertation Award for her Ph.D. thesis, "Designing Algorithms for Social Good." Abebe is the first female computer scientist to be inducted into the Harvard Society of Fellows and co-founded Mechanism Design for Social Good (MDSG), a multi-institutional initiative to improve access to opportunity for historically underserved and disadvantaged communities. Jingbo Shang, assistant professor of Computer Science at University of California at San Diego, earned runner-up for his thesis, "Constructing and Mining Heterogeneous Information Networks From Massive Text." The ACM SIGKDD Dissertation Award recognizes outstanding work done by graduate students in the areas of data science, machine learning and data mining.
ACM SIGKDD Rising Star Award
Danai Koutra, Morris Wellman assistant professor of Computer Science and Engineering at University of Michigan, and Jiliang Tang, assistant professor of Computer Science and Engineering at Michigan State University, both received the first annual ACM SIGKDD honors of Rising Star. Koutra's research in large-scale data mining focuses on principled, interpretable and scalable methods for network summarization and multi-network analysis. Tang's notable work includes research into representation learning, especially on graphs and its applications on the internet and social media domains. New this year, the Rising Star Award celebrates individual work done in the first five years after earning a PhD. The award aims to celebrate the early accomplishments of the SIGKDD communities' brightest new minds.
SIGKDD Test of Time Award for Research
The SIGKDD Test of Time award recognizes outstanding KDD papers, at least ten years old, which have had a lasting impact on the data mining research community and continue to be cited as the foundation for new branches of research. This year, the Test of Time Award for Research goes to Victor S. Sheng, Foster Provost and Panagiotis Ipeirotis for their approach to selective acquisition of multiple labels featured in the 2008 peer-reviewed paper, "Get Another Label? Improving Data Quality And Data Mining Using Multiple, Noisy Labelers."
SIGKDD Test of Time Award for Applied Science
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang and Zhong Su received the inaugural Test of Time Award for Applied Science in recognition of their study of mining academic social networks published in the 2008 peer-reviewed paper, "ArnetMiner: Extraction And Mining Of Academic Social Networks." SIGKDD introduced this award to honor influential research in real-world applications of data science.
KDD 2020 is being held virtually on Aug. 23-27, 2020. For more information on this year's event, please visit: www.kdd.org/kdd2020.
About ACM SIGKDD:
ACM is the premier global professional organization for researchers and professionals dedicated to the advancement of the science and practice of knowledge discovery and data mining. SIGKDD is ACM's Special Interest Group on Knowledge Discovery and Data Mining. The annual KDD International Conference on Knowledge Discovery and Data Mining is the premier interdisciplinary conference for data mining, data science and analytics.
For more information on KDD, please visit: https://www.kdd.org/.
SOURCE ACM SIGKDD