SIGKDD News

KDD 2021, the premier interdisciplinary conference in data science, 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. Ahead of the organization's annual conference on August 14-18, the awards  recognize those who have made a lasting impact in the industry as a whole.

"Since the inception of the conference 27 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. Wei Wang, chair of ACM SIGKDD and Leonard Kleinrock Chair Professor in the Department of Computer Science at UCLA. "The outstanding scientists honored are recognized not only for their advancements in a specialized field but for their significant contributions to the world."

ACM SIGKDD Innovation Award

Dr. Johannes Gehrke, Microsoft Corporation

For his outstanding contributions to new data mining algorithms and data privacy

Johannes Gehrke is a Technical Fellow and the Managing Director of Research at Redmond and the CTO and head of machine learning for the Intelligent Communications and Conversations Cloud (IC3), which powers Microsoft Teams. Until June 2020, he was leading architecture and machine learning for IC3. Johannes’ research interests are in the areas of database systems, distributed systems, and machine learning. He has received a National Science Foundation Career Award, an Arthur P. Sloan Fellowship, a Humboldt Research Award, the 2011 IEEE Computer Society Technical Achievement Award, and he is an ACM Fellow and an IEEE Fellow. Johannes co-authored the undergraduate textbook Database Management Systems (McGrawHill (2002), currently in its third edition), used at universities all over the world. He is a member of the ACM SIGKDD Executive Committee. From 1999 to 2015, Johannes was on the faculty in the Department of Computer Science at Cornell University where he graduated 25 PhD students, and from 2005 to 2008, he was Chief Scientist at FAST Search and Transfer.

Innovation Award Committee:  Rakesh Agrawal; Michael Zeller; Ying Li; Thorsten Joachims; Ted Senator; Jiawei Han; Sunita Sarawagi; Vipin Kumar

ACM SIGKDD Service Award

Dr. Shipeng Yu, LinkedIn

For his outstanding history of serving and promoting the field of data mining and the data mining community

Shipeng Yu has been leading teams and driving AI innovations across industries such as social network, manufacturing, service, and healthcare. With a strong research background and an entrepreneurial mindset, he is enthusiastic about creative thinking, innovative business models, and great team work. 

Currently he leads the Communications AI team at LinkedIn, which owns the major AI engine driving engagement growth and member retention at LinkedIn. The team develops innovative AI algorithms, builds scalable AI pipeline and platforms, makes great business impact, and also actively contributes to research communities. 

Shipeng strongly believes in giving back to the research community. He served as General Chair for KDD 2017. He is currently the Sponsorship Director for SIGKDD, overseeing the sponsorship effort for the annual KDD conferences and other Data Mining community activities. 

Personally, he has a broad interest in machine learning and large-scale data analysis, working in a wide range of applications including recommendation system, information retrieval, text analysis, medical imaging, precision medicine, and clinical decision support. He co-edited 2 books, co-invented 30+ patents, and co-authored 60+ peer-reviewed publications in international journals and conferences, with 3,000+ citations. In 2009 he won the International Award for the Best Industrial Data Mining Application. 

SERVICE AWARD COMMITTEE: Rakesh Agrawal; Michael Zeller; Ying Li; Thorsten Joachims; Ted Senator; Jiawei Han; Sunita Sarawagi; Christos Faloutsos

ACM SIGKDD Dissertation Award

Winner: Aditya Grover, Stanford University (USA)
Dissertation title: Learning to Represent and Reason Under Limited Supervision

Aditya Grover is a research scientist in the Core ML team at Facebook AI Research. He also collaborates with Pieter Abbeel at UC Berkeley as a visiting postdoctoral researcher. In Fall 2021, he will join UCLA as an assistant professor of computer science. 

Aditya’s research is centered around machine learning for probabilistic modeling, unsupervised representation learning, and sequential decision making, with applications at the intersection of physical sciences and climate change.

Previously, he completed my PhD at Stanford University (2020) and his bachelors at IIT Delhi (2015), both in computer science. During his PhD, he spent wonderful summers interning at Google Brain, Microsoft Research, and OpenAI. At Stanford, he created and taught a new course on Deep Generative Models with his advisor Stefano Ermon.

Runner Up: Shweta Jain, University of California, Santa Cruz (USA)
Dissertation Title: Counting Cliques in Real-World Graphs

Shweta Jain is a postdoc at the University of Illinois, Urbana-Champaign, working with Prof. Hanghang Tong. She recently obtained a PhD in Computer Science from the University of California, Santa Cruz, where she was advised by Prof. Seshadhri Comandur. Shweta’s research interests are in randomized and approximation algorithms, combinatorial optimization, graph mining, and algorithms for massive data. 

Prior to joining UCSC, she completed her Master's in Computer Science at the University of Chicago.

Honorable Mention: Leonardo Pellegrain, University of Padova (Italy)
Dissertation title: Rigorous and Efficient Algorithms for Significant and Approximate Pattern Mining 

Leonardo is a Postdoctoral Researcher at the Department of Information Engineering of the University of Padova.  In his research, he works on providing novel efficient and statistically sound algorithms for knowledge and pattern discovery from data, often motivated by biological applications.

He obtained his Ph.D. in Information Engineering from the Department of Information Engineering of the University of Padova, under the supervision of Professor Fabio Vandin.  Leonardo also had an amazing opportunity of visiting the Department of Computer Science at Brown University as a Visiting Research Fellow under the supervision of Professor Eli Upfal.

DISSERTATION AWARD COMMITTEE: Tina Eliassi-Rad (Chair); Kristin P. Bennett; Christos Faloutsos; Rayid Ghani; Aristides Gionis; Yizhou Sun; Jie Tang; Evimaria Terzi

ACM SIGKDD Rising Star Award

Dr. Xia “Ben” Hu, Rice University

For his significant research in human-centric data mining and contribution to developing interpretable and automated methods to make complex machine learning algorithms easily used by domain experts.

Dr. Xia “Ben” Hu is an Associate Professor at Rice University in the Department of Computer Science. Dr. Hu has published over 100 papers in several major academic venues, including NeurIPS, ICLR, KDD, WWW, IJCAI, AAAI, etc. An open-source package developed by his group, namely AutoKeras, has become the most used automated deep learning system on Github (with over 8,000 stars and 1,000 forks). Also, his work on deep collaborative filtering, anomaly detection and knowledge graphs have been included in the TensorFlow package, Apple production system and Bing production system, respectively. His papers have received severaL Best Paper (Candidate) awards from venues such as WWW, WSDM and ICDM. He is the recipient of NSF CAREER Award. His work has been cited more than 10,000 times with an h-index of 41. He was the conference General Co-Chair for WSDM 2020.

RISING STAR AWARD COMMITTEE: Rakesh Agrawal; Michael Zeller; Ying Li; Thorsten Joachims; Ted Senator; Sunita Sarawagi; Vipin Kumar; Martin Ester; Zhi-Hua Zhou; Ke Wang; Diane Joyce Cook

SIGKDD Test of Time Award for Research

Chong Wang, David M. Blei: Collaborative topic modeling for recommending scientific articles. KDD 2011: 448-456. 

SIGKDD Test of Time Award for Applied Data Science

Diane Tang, Ashish Agarwal, Deirdre O’Brien, Mike Meyer: Overlapping experiment infrastructure: more, better, faster experimentation.  KDD 2010: 17-26