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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

Professor
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

Professor
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

Professor Andy Berglund

Professor Andy Berglund

Professor of Biochemistry and Molecular Biology in the College of Medicine at the University of Florida.
College of Medicine at the University of Florida

Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy
Tuesday 10:00am – 12:00pm, Room 200D

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Dr. Paritosh Desai

Dr. Paritosh Desai

Chief Data and Analytics Officer
Target

It Takes More than Math and Engineering to Hit the Bullseye with Data
Tuesday 10:00am – 12:00pm, Room 200D

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Mohak Shah

Mohak Shah

Head of Data Science
Bosch

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

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Dr. Rajesh Parekh

Dr. Rajesh Parekh

Director of Analytics
Facebook

Designing AI at Scale to Power Everyday Life
Tuesday 1:30pm – 3:30pm, Room 200D

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Dr. Josh Bloom

Dr. Josh Bloom

VP of Data & Analytics
GE Digital

Industrial Machine Learning
Tuesday 1:30pm – 3:30pm, Room 200D

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Professor Longbing Cao

Professor Longbing Cao

Professor
University of Technology Sydney

Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management
Tuesday 1:30pm – 3:30pm, Room 200D

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Professor Vipin Kumar

Professor Vipin Kumar

Professor
University of Minnesota

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

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Dr. Nick Malizia

Dr. Nick Malizia

Head of Data Science, TellusLabs
TellusLabs

Spaceborne data enters the mainstream
Wednesday 10:00am – 12:00pm, Room 200D

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Professor Jonathan P. How

Professor Jonathan P. How

The Richard C. Maclaurin Professor of Aeronautics and Astronautics
Massachusetts Institute of Technology

Planning and Learning under Uncertainty: Theory and Practice
Wednesday 10:00am – 12:00pm, Room 200D

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Eduardo Ariño de la Rubia

Eduardo Ariño de la Rubia

Chief Data Scientist
Domino Data Lab

More than the Sum of its Parts: Building Domino Data Lab
Wednesday 1:30pm – 3:30pm, 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

Moderator: Usama Fayyad

Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?

Moderator: Usama Fayyad

CEO
Open Insights


Moderator: Usama M. Fayyad - Open Insights

Panelist: Arno Candel - H2O.ai, Inc.

Panelist: Eduardo Ariño de la Rubia - Domino Data Lab

Panelist: Szilárd Pafka - Epoch

Panelist: Anthony Chong -IKASI

Panelist: Jeong-Yoon Lee - Microsoft

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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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