KDD 2019 Honors Top Data Scientists At Sold-Out Conference In Anchorage
ANCHORAGE, Alaska, Aug. 6, 2019 /PRNewswire/ — Today, KDD 2019, the premier interdisciplinary data science conference, awarded top prizes to research teams from academic and industry organizations at the organization’s sold-out conference in Anchorage, Alaska. Three illustrious awards honor lifetime achievement in innovation, sustained service to KDD and research that has stood the test of time. In addition, KDD 2019 recognizes KDD Cup competition winners and awards for best papers.
In its 25th year, KDD 2019’s award program seeks to recognize those that have made an impact in the industry as a whole. This year’s award winners for lifetime achievement include:
- ACM SIGKDD Innovation Award Winner—Charu Aggarwal, distinguished research staff member at IBM T.J. Watson Research Center, is recognized for his research contributions in high-dimensional data, privacy, data streams, uncertain data, graphs, text mining and social networks. The ACM SIGKDD Innovation Award is the highest industry award for tactical excellence, recognizing honorees who have made a lasting impact in advancing the theory and practice of the field.
- ACM SIGKDD Service Award Winner—Balaji Krishnapuram, director and distinguished engineer at IBM Watson Health, is honored for his contributions to society through the development of machine learning products to improve healthcare. ACM SIGKDD Service Award is the highest service award in the industry, honoring outstanding professional contributions to the world through knowledge discovery and data mining.
- SIGKDD Test of Time Award—Christos Faloutsos, Natalie Glance, Carlos Guestrin, Andreas Krause, Jure Leskovec and Jeanne VanBriesen earn this award for their trailblazing approach to outbreak detection featured in research paper, «Cost-Effective Outbreak Detention in Networks.» Since its debut at KDD 2007, the paper has been cited in more than 1,800 peer-reviewed papers. The SIGKDD Test of Time award recognizes outstanding research from past KDD Conferences that has a lasting impact on the data mining research community.
The 2019 KDD Cup challenged teams to apply their expertise to three real-world challenges. More than 2,800 registered teams from 39 countries and 230 academic and research institutions registered to compete in three distinct competition tracks. The following teams were recognized as winners of the 2019 KDD Cup competition:
- KDD Cup, Regular Machine Learning Competition, Task 1 Winner—Shiwen Cui, Long Guo, Changhua Meng, Weiqiang Wang, Can Yi and Xing Zhao are recognized for manually developing the best algorithm to generate context-aware multi-modal transportation recommendations. The machine learning track, sponsored by Baidu, required applicants to optimize their routes over various forms of transport across a variety of users and spatiotemporal contexts.
- KDD Cup, Regular Machine Learning Competition, Task 2 Winner— Tsukasa Demizu, Shin Ishiguro, Akihiro Kawana, Shohei Maruyama and Keiichi Ochiai are honored for best processing of data on multi-modal transportation into the report, «Simulating the Eﬀects of Eco-Friendly Transportation Selections for Air Pollution Reduction.»
- KDD Cup, Regular Machine Learning Competition, PaddlePaddle Winner—Enhong Chen, Joya Chen, Min Hou, Xianfeng Liang, Qi Liu, Yang Liu, Han Wu, Likang Wu, Yuyang Ye and Runlong Yu are recognized for best implanting a demo of their algorithm on the open source deep learning platform, PaddlePaddle.
- KDD Cup, Automated Machine Learning Competition Winner— Mingjian Chen, Jianqiang Huang, Bohang Zheng and Zhipeng Luo are honored for their excellent work deploying an automated machine learning solution to binary classification problems for temporal relational data. Sponsored by 4Paradigm, ChaLearn and Microsoft, the track evaluated submissions through comparison with five undisclosed human datasets.
- KDD Cup, «Research for Humanity,» Reinforcement Learning Competition Winner—Zi-Kuan Huang, Hung-Yu Kao and Jing-Jing Xiao are recognized for their outstanding work in applying machine learning tools to predict the efficiency of policy solutions that may curb the spread of malaria in sub-Sahara Africa. The competition track is sponsored by IBM Research Africa and Hexagon-ML.
- KDD Cup, Innovation Award—Hexagon-ML is awarded the Innovation Award for its part in pioneering the Reinforcement Learning Competition, a first-of-its-kind contest that aims to advance the data science community in reinforcement learning.
Interest in presenting research at the conference hit an all-time high at KDD 2019 with over 7,900 papers submitted from 58 countries and 1,200 organizations. Out of the 321 papers ultimately selected, the following research is recognized for its potential impact on the industry:
- Best Paper in the Research Track—Austin Benson (Cornell), David Bindel (Cornell) and Kun Dong (Cornell) for «Network Density of States.»
- Best Paper in the Applied Data Science Track—Lotte Bransen (SciSports), Jesse Davis (KU Leuven), Tom Decroos (KU Leuven) and Jan Van Haaren (SciSports) for «Action Speaks Louder Than Goals: Value Player Actions in Soccer.»
- Best Dissertation—Tim Althoff (Stanford), supervised by Jure Leskovec (Stanford), for «Data Science for Human Well-being.»
- Best Startup Research—Alang Liu (RealAI), Chao Liu (TianYanCha), Zhen Wei (Arkive) and Kartik Yellepeddi (Deepair) are recognized individually for their work with early-stage startups.
KDD 2019 is being held at the Dena’ina Convention Center and William Egan Convention Center in Anchorage, Alaska, Aug. 4-8, 2019. For more information on this year’s event, please visit: www.kdd.org/kdd2019.
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.
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SOURCE ACM SIGKDD