NEW YORK, May 21, 2019 /PRNewswire/ -- The Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) today announced that registration is now open for KDD 2019, the premier interdisciplinary data-science conference. Taking place in Anchorage, Alaska, Aug. 4-8, 2019, the KDD conference brings together researchers and practitioners from data science, machine learning, big data, and artificial intelligence.
KDD brings together leading experts in the world of data science and artificial intelligence to share their latest research results and apply recent findings to the challenges facing an array of industries. The conference is known for its highly selective peer-review process—less than 12% of more than 2,000 papers submitted are selected and highlighted throughout the five-day gathering. Also unique is the KDD Cup, an annual competition where thousands of experts and enthusiasts, from across the globe, participate to solve issues in applied data science for real world problems. This year, the contest has already reached new heights with over $100,000 in prize money across three groundbreaking categories.
"What makes KDD unique is its fantastic combination of depth and breadth—not only is there highly comprehensive technical innovation reported at KDD, but there is also a wealth of programming that addresses the social impact of this research," said Vipin Kumar, KDD 2019 conference co-chair and head of the computer science and engineering department at the University of Minnesota. "As the largest, most established gathering of data scientists from all over the world, KDD regularly attracts more attendees and more paper submissions than any other conference of its kind."
The conference program comprises workshops, tutorials, and designated special theme days, highlighting machine learning applications for environmental sustainability, healthcare, and deep learning. Sessions on topics as varied as theoretical advances in deep learning, healthcare AI, urban computing, and intelligent transportation ensure programmatic diversity and choice for conference attendees.
"Results, meetings, and discussions at KDD transform entire industries and create billions of dollars of realized impact, while also shaping the way we think of this impact and its effects on society and science. Whether hailing from academia, industry, or government, participants will experience an unparalleled content lineup, allowing for robust discussion of novel ideas on current and emerging topics," added Ankur Teredesai, KDD 2019 conference co-chair and co-founder of KenSci. "Entirely run and managed by volunteers, top international leaders in data science collaborate year after year to put together a fantastic KDD conference program. I'm thankful for the commitment and teamwork of our organizing committee and look forward to welcoming participants to help shape this very important field of science and technology."
Highlighting the conference's successful history of industry participation, KDD 2019 is sponsored by some of the world's biggest brands. Sponsors include Intuit, DiDi, Amazon, Facebook, LinkedIn, Apple, KenSci, Wayfair, Criteo, Inspur, D.E. Shaw & Co, Square Point, Naver Line, Microsoft, Siemens, Etsy, Two Sigma, Squirrel AI Learning, Gurobi, JD.com, Geneia, Pinterest, CRC Press, Indeed, and HexagonML.
KDD 2019 will be held at the Dena'ina Convention Center and William Egan Convention Center in Anchorage. For more information and to register, visit: https://www.kdd.org/kdd2019/attending/registration.
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. SIG 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