SIGKDD Honors Groundbreaking Achievements in Knowledge Discovery and Data Mining

Awards Celebrate Innovation, Service and Rising Stars Ahead of the 27th Annual Conference  

SINGAPORE, Aug. 13, 2021 /PRNewswire/ -- The Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) today announced the recipients of the 2021 ACM SIGKDD Awards for exemplary individuals and research teams in data science, machine learning, big data and artificial intelligence (AI). Ahead of the organization's annual conference on Aug. 14-18, the awards program recognizes those who have made a lasting impact in the industry.

"The KDD conference has long served as an international platform that showcases the most innovative research in data mining and knowledge discovery," said Wei Wang, SIGKDD chair and professor in computer science at the University of California, Los Angeles. "This year's winners reflect the rapid growth and maturation of our community over the past 27 years, and the promise data science offers to affect real change today."

Based in Singapore, KDD 2021 will take place virtually with 24-hour global access featuring workshops, tutorials, an array of speakers, and more. As the largest international data science conference, this event brings together academia and professional practitioners across the data sciences to celebrate outstanding technical and service contributions.  

ACM SIGKDD Innovation Award
Recipient of the ACM SIGKDD Innovation Award is Johannes Gehrke, a technical fellow and managing director of research at Redmond, and the chief technology officer and head of machine learning for the Intelligent Communications and Conversations Cloud, which powers Microsoft Teams. Dr. Gehrke is recognized for his research contributions in database systems, distributed systems and machine learning. The ACM SIGKDD Innovation Award is the highest honor for technical excellence in 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 KDD.

“I am humbled and honored by this recognition from the community,” said Dr. Gehrke. “KDD is the place for innovation in data science, from deep science to impactful applications. I am grateful to my collaborators over the years with whom I had the chance to move the field forward.”

ACM SIGKDD Service Award
Recipient of the ACM SIGKDD Service Award is Dr. Shipeng Yu, who leads the communications AI team at LinkedIn. Dr. Yu is recognized for his contributions through dedication to ACM SIGKDD as general chair of KDD 2017 and currently as sponsorship director for SIGKDD. He oversees the sponsorship effort for the annual conferences and other data mining community activities. 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 sponsorship director for SIGKDD, it's a privilege serving and promoting the data mining community," said Dr. Yu. "I strongly believe in giving back to the research field, and I am grateful for all of the volunteers and individuals who help make this conference special every year."

ACM SIGKDD Rising Star Award
Recipient of the ACM SIGKDD Rising Star Award is Dr. Xia "Ben" Hu, professor of computer science at Rice University. Dr. Hu is recognized for his contributions in human-centric data mining, including influential work developing interpretable and automated methods to make complex machine learning algorithms easily used by domain experts. In its second year, the Rising Star Award celebrates individual work done in the first five years after earning a Ph.D. The award aims to celebrate the early accomplishments of the KDD communities' brightest new minds.

“The KDD conference has been celebrating the contributors in knowledge discovery and data mining for over two decades,” said Dr. Hu. “I am thrilled to be honored among my peers. This recognition is not only to me, but also to my mentors, talented students and wonderful collaborators. I look forward to contributing more to this great community moving forward!"

ACM SIGKDD Dissertation Award
Recipient of the ACM SIGKDD Dissertation Award is Aditya Grover, incoming assistant professor of computer science at University of California, Los Angeles, and research scientist on the core machine learning (ML) team at Facebook AI Research. Grover earned this year's award for his dissertation, "Learning to Represent and Reason Under Limited Supervision."

Shweta Jain, a postdoctoral researcher at the University of Illinois, Urbana-Champaign, earned runner-up for her dissertation, "Counting Cliques in Real-World Graphs."

Leonardo Pellegrina, a postdoctoral researcher at the University of Padova, received honorable mention for his dissertation, "Rigorous and Efficient Algorithms for Significant and Approximate Pattern Mining."

SIGKDD Test of Time Award for Research
The 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 was given to Chong Wang and David M. Blei for their approach to collaborative topic modeling for recommending scientific articles featured from 2011.

SIGKDD Test of Time Award for Applied Science
Diane Tang, Ashish Agarwal, Deirdre O'Brien and Mike Meyer received the Test of Time Award for Applied Science in recognition of their 2010 study on overlapping experiment infrastructure that enables quicker experimentation, as detailed in "Overlapping Experiment Infrastructure: More, Better, Faster Experimentation."

For more information on this year's event, please visit: https://kdd.org/kdd2021/.

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SOURCE ACM SIGKDD

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