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Keynotes

	Title
	Jennifer Chayes: Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks
	Nando de Freitas: Learning to learn and compositionality with deep recurrent neural networks
	Greg Papadopoulos: A VC View of Investing in ML
	Joe Hellerstein: People, Computers, and The Hot Mess of Real Data
	Whitfield Diffie: The Evolving Meaning of Information Security

Plenary Panel

	Title
	Moderator: Andrei Broder: Is Deep Learning the New 42?

Full Day Workshops

	Title
	BPDM: 2 Day Workshop2 Day Workshop
	KDD Cup 2016: Towards measuring the impact of research institutionsTowards measuring the impact of research institutions
	Workshop 1: Mining and Learning with GraphsMining and Learning with Graphs
	Workshop 2: Large-Scale Sports AnalyticsLarge-Scale Sports Analytics
	Workshop 3: Data Science for Food, Energy and WaterData Science for Food, Energy and Water
	Workshop 4: Mining and Learning from Time SeriesMining and Learning from Time Series
	Workshop 5: The 5th International Workshop on Urban ComputingThe 5th International Workshop on Urban Computing
	Workshop 6: Interactive Data Exploration and AnalyticsInteractive Data Exploration and Analytics

Half Day Workshops

	Title
	Workshop 7: Outlier Definition, Detection, and Description On-DemandOutlier Definition, Detection, and Description On-Demand
	Workshop 8: Workshop on Causal DiscoveryWorkshop on Causal Discovery
	Workshop 9: Machine learning for large scale transportation systemsMachine learning for large scale transportation systems
	Workshop 10: Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and ApplicationsBig Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
	Workshop 11: Enterprise IntelligenceEnterprise Intelligence
	Workshop 12: Machine learning meets fashion: Data, algorithms and analytics for the fashion industryMachine learning meets fashion: Data, algorithms and analytics for the fashion industry
	Workshop 13: Machine Learning for Prognostics and Health ManagementMachine Learning for Prognostics and Health Management
	Workshop 14: 2016 KDD Workshop on Large-scale Deep Learning for Data Mining2016 KDD Workshop on Large-scale Deep Learning for Data Mining
	Workshop 15: Workshop on Issues of Sentiment Discovery and Opinion MiningWorkshop on Issues of Sentiment Discovery and Opinion Mining
	Workshop 16: 15th International Workshop on Data Mining in Bioinformatics15th International Workshop on Data Mining in Bioinformatics

Tutorials

	Title
	Hands-On: Scalable R on SparkScalable R on Spark
	Hands-On: Streaming AnalyticsStreaming Analytics
	Hands-On: CNTK—Microsoft’s open-source deep-learning toolkitCNTK—Microsoft’s open-source deep-learning toolkit
	Hands-On: Getting Started with Amazon Web Services BootcampGetting Started with Amazon Web Services Bootcamp
	Hands-On: Introduction to Spark 2.0Introduction to Spark 2.0
	Hands-On: MXNetMXNet
	Hands-On: Big Natural Language Data ProcessingBig Natural Language Data Processing
	Hands-On: Building Recommender Systems using Photon MLBuilding Recommender Systems using Photon ML
	Tutorial 3: Collective Sensemaking via Social Sensors: Extracting, Profiling, Analyzing, and Predicting Real-world EventsCollective Sensemaking via Social Sensors: Extracting, Profiling, Analyzing, and Predicting Real-world Events
	Tutorial 4: Lifelong Machine Learning and Computer Reading the WebLifelong Machine Learning and Computer Reading the Web
	Tutorial 5: IoT Big Data Stream MiningIoT Big Data Stream Mining
	Tutorial 6: Healthcare Data Mining with Matrix ModelsHealthcare Data Mining with Matrix Models
	Tutorial 7: Algorithmic bias: from discrimination discovery to fairness-aware data miningAlgorithmic bias: from discrimination discovery to fairness-aware data mining
	Tutorial 8: Extracting Optimal Performance From Dynamic Time WarpingExtracting Optimal Performance From Dynamic Time Warping
	Tutorial 9: Scalable learning of graphical modelsScalable learning of graphical models
	Tutorial 10: Business Applications of Predictive Modeling at ScaleBusiness Applications of Predictive Modeling at Scale
	Tutorial 11: Leveraging Propagation for Data Mining: Models, Algorithms and ApplicationsLeveraging Propagation for Data Mining: Models, Algorithms and Applications
	Tutorial 12: Enabling the Discovery of Reliable Information from Passively and Actively Crowdsourced DataEnabling the Discovery of Reliable Information from Passively and Actively Crowdsourced Data

Applied Data Science Invited Talks

	Title
	Andy Palmer: The Dirty Little Secret of Enterprise Data
	Kamakshi Sivaramakrishnan & Randell Cotta: Democratizing Consumer Identity: Data Science’s Answer to Facebook and Google
	Jeff Stribling: Large-Scale Machine Learning at Verizon: Theory and Applications
	Duncan Watts: Computational Social Science: Exciting Progress and Future Challenges
	Oliver Downs: How Machine Learning has Finally Solved Wanamaker’s Dilemma
	Ingo Mierswa: The Wisdom of Crowds: Best Practices for Data Prep & Machine Learning derived from Millions of Data Science Workflows
	Jonathan Becher: Can You Teach The Elephant To Dance? AKA: Culture Eats Data Science for Breakfast
	Ralf Herbrich: Learning Sparse Models at Scale
	Moderator: Evangelos Simoudis: Panel - Big Data Needs Big Dreamers: Lessons from successful Big Data investors
	Danny Shapiro: Accelerating the Race to Autonomous Cars
	Ching Law: Building User Profiles from Online Social Behaviors, with Applications in Tencent Social Ads
	Jeff Schneider: Bayesian Optimization and Embedded Learning Systems
	Moderator: Usama Fayyad: Panel: BigData Tools and Solutions: The Myths and the Reality
	Caitlin Smallwood: It's About Time