KDD 2017
Halifax, Nova Scotia - Canada
August 13 - 17, 2017

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.

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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.

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Keynote Speakers

Cynthia Dwork

Professor / Distinguished Scientist
Harvard University / Microsoft Research

What’s Fair?

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.

Bin Yu

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 Shah

Mohak Shah

Head of Data Science

Fireside Chat: AI For Automotive And Industrial Applications
Tuesday 10:00am – 12:00pm, Room 200D

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Professor Vipin Kumar

Professor Vipin Kumar

University of Minnesota

Big Data in Climate: Opportunities and Challenges for Machine Learning
Wednesday 10:00am – 12:00pm, Room 200D

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Szilard Pafka

Szilard Pafka

Machine Learning Software in Practice: Quo Vadis?
Wednesday 1:30pm – 3:30pm, Room 200D

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Applied Data Science Invited Panel

KDD 2017 Plenary Panel

Moderators: Muthu Muthukrishnan and Andrew Tomkins

The Future of Artificially Intelligent Assistants

Muthu 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)

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