KDD 2017 is a premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.
Halifax, Nova Scotia - Canada
August 13 - 17, 2017
This year, KDD is using the mobile app, Whova! Whova has been used in thousands of events and is loved by attendees as you can browse event agenda, network with other attendees, scan & exchange business cards all digitally via your mobile phone.
- Step 1: Download and install the Whova app from App Store (for iPhones) or Google Play (for Android phones).
- Step 2: Sign up to create an account with your email.
- Step 3: Search "KDD" event and enter in access code: kddyw
- Step 4: You're all set. Now enjoy!
KDD 2017 Poster Presentation Instructions
Meet the Editors Panel KDD-2017
One-Day Option in SIGDOC for SIGKDD Attendees
Call for KDD 2017 Student Travel Award Applications
KDD 2017 Research and Applied Data Science Track Accepted Papers
New for KDD 2017 - Networking With Experts
Announcing BPDM@KDD2017 Student Travel Awards
Join Us in Halifax for SIGDOC 2017 and SIGKDD 2017
KDD 2017 Startup Travel Awards Call for Applications
Accepted KDD 2017 Hands-on Tutorials
Accepted KDD 2017 Conventional Tutorials
Accepted KDD 2017 Workshops
ACM SIGKDD Test-of-Time Paper Awards - Call for Nomination
2017 SIGKDD Doctoral Dissertation Award - Call for Nominations
2017 Innovation and Service Awards - Call for Nominations
Announcing KDD Cup 2017: Highway Tollgates Traffic Flow Prediction
KDD 2017 Call for Tutorials (Closed)
KDD 2017 Call for Workshop Proposals (Closed)
KDD 2017 Call for Applied Data Science Papers (Closed)
KDD 2017 Call for Research Papers (Closed)
Harvard University / Microsoft Research
Data, algorithms, and systems have biases embedded within them reflecting designers’ explicit and implicit choices, historical biases, and societal priorities. They form, literally and inexorably, a codification of values. “Unfairness” of algorithms – for tasks ranging from advertising to recidivism prediction – has attracted considerable attention in the popular press. The talk will discuss the nascent mathematically rigorous study of fairness in classification and scoring.
University of California at Berkeley
Three Principles of Data Science: Predictability, Stability, and Computability
In this talk, I’ll discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. The ultimate importance of prediction lies in the fact that future holds the unique and possibly the only purpose of all human activities, in business, education, research, and government alike. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results. It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. The three principles will be demonstrated through analytical connections, and in the context of two on-going projects, for which “data wisdom” is also indispensable. Specifically, the first project employs deep learning networks (CNNs) to understand pattern selectivities of neurons in the difficult visual cortex V4; and the second project predicts partisanship and tone of political TV ads by employing and comparing different latent variable models with a Lasso-based model.
Renée J. Miller
University of Toronto
The Future of Data Integration
The value of data explodes when it is integrated. In this talk, I present some important innovations in data integration over the last two decades. These include data exchange, which provides a foundation for reasoning about the correctness of transformed data, and the use of declarative mappings in integration. I discuss how data mining has been used to facilitate data integration and present some important new data integration challenges that arise in data science.
Applied Data Science Invited Talks
Mohak ShahHead of Data Science
Fireside Chat: AI For Automotive And Industrial Applications
Tuesday 10:00am – 12:00pm, Room 200D
Professor Vipin KumarProfessor
University of Minnesota
Big Data in Climate: Opportunities and Challenges for Machine Learning
Wednesday 10:00am – 12:00pm, Room 200D
Machine Learning Software in Practice: Quo Vadis?
Wednesday 1:30pm – 3:30pm, Room 200D
Applied Data Science Invited Panel
KDD 2017 Plenary Panel
The Future of Artificially Intelligent AssistantsMuthu Muthukrishnan (Distinguished Professor of Computer Science at Rutgers University)
Andrew Tomkins (Director of Engineering, Google)
Wednesday 1:30pm – 3:30pm, Room 202-205
Moderator: Muthu Muthukrishnan (Distinguished Professor of Computer Science at Rutgers University)
Moderator: Andrew Tomkins (Director of Engineering, Google)
Panelist: Deepak Agarwal (VP of Engineering, Head of AI and ML, LinkedIn)
Panelist: Usama Fayyad (CEO, Open Insights)
Panelist: Larry Heck (Research Director, Research & Machine Intelligence, Google)
Panelist: Bing Liu (Distinguished Profess of Computer Science, University of Illinois at Chicago)