KDD 2017 Session Feedback

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Keynotes

Title
Cynthia Dwork: What’s Fair?
Renée J. Miller: The Future of Data Integration
Bin Yu: Three Principles of Data Science: Predictability, Stability, and Computability

Plenary Panel

Title
Moderators: Muthu Muthukrishnan and Andrew Tomkins: The Future of Artificially Intelligent Assistants

Full Day Workshops

Title
Mining and Learning from Time SeriesMining and Learning from Time Series
Big Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and ApplicationsBig Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Interactive Data Exploration and Analytics (IDEA)Interactive Data Exploration and Analytics (IDEA)
Mining and Learning with GraphsMining and Learning with Graphs
Fairness, Accountability, Transparency in Machine Learning (FATML)Fairness, Accountability, Transparency in Machine Learning (FATML)
Urban ComputingUrban Computing
Data Science + JournalismData Science + Journalism
Data Science for Intelligent Food, Energy and WaterData Science for Intelligent Food, Energy and Water
2017 Edition of AdKDD and TargetAd2017 Edition of AdKDD and TargetAd
Advancing Education With DataAdvancing Education With Data

Half Day Workshops

Title
Medical Informatics and HealthcareMedical Informatics and Healthcare
Workshop on Causal DiscoveryWorkshop on Causal Discovery
International Workshop on Data Mining in BioinformaticsInternational Workshop on Data Mining in Bioinformatics
Machine Learning Meets Fashion, Data, algorithms and analytics for the fashion industryMachine Learning Meets Fashion, Data, algorithms and analytics for the fashion industry
Machine Learning for Prognostics and Health ManagementMachine Learning for Prognostics and Health Management
Big data analytics-as-a-Service: Architecture, Algorithms, and Application in Health InformaticsBig data analytics-as-a-Service: Architecture, Algorithms, and Application in Health Informatics
Machine Learning for CreativityMachine Learning for Creativity
Data-Driven DiscoveryData-Driven Discovery
Anomaly Detection in FinanceAnomaly Detection in Finance
Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)

Tutorials

Title
Athlytics: Data Mining and Machine Learning for Sports AnalyticsAthlytics: Data Mining and Machine Learning for Sports Analytics
Machine Learning for Survival Analysis: Theory, Algorithms and ApplicationsMachine Learning for Survival Analysis: Theory, Algorithms and Applications
Data-Driven Approaches towards Malicious Behavior ModelingData-Driven Approaches towards Malicious Behavior Modeling
From Theory to Data Product: Applying Data Science Methods to Effect Business ChangeFrom Theory to Data Product: Applying Data Science Methods to Effect Business Change
Urban Computing: Enabling Intelligent Cities with Big DataUrban Computing: Enabling Intelligent Cities with Big Data
Context-Rich Recommendation via Information Network Analysis ApproachContext-Rich Recommendation via Information Network Analysis Approach
IoT in Practice: Case Studies in Data Analytics, with Issues in Privacy and SecurityIoT in Practice: Case Studies in Data Analytics, with Issues in Privacy and Security
System Event Mining: Algorithms and ApplicationsSystem Event Mining: Algorithms and Applications
Non-IID Learning in Big DataNon-IID Learning in Big Data
A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Data Mining in Unusual Domains with Information-rich Knowledge Graph Construction, Inference and SearchData Mining in Unusual Domains with Information-rich Knowledge Graph Construction, Inference and Search
Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity JoinsTime Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins
Making Better Use of the CrowdMaking Better Use of the Crowd
Recent Advances in Feature Selection: A Data PerspectiveRecent Advances in Feature Selection: A Data Perspective
Safe Data Analytics: Theory, Algorithms, and ApplicationsSafe Data Analytics: Theory, Algorithms, and Applications
Smart Analytics for Big Time-series DataSmart Analytics for Big Time-series Data
Network Embedding- Enabling Network Analytics and Inference in Vector SpaceNetwork Embedding- Enabling Network Analytics and Inference in Vector Space
Learning Representations of Large-scale NetworksLearning Representations of Large-scale Networks
Amazon Web Services & MxNETAmazon Web Services & MxNET
TensorFlow: A Hands-on IntroductionTensorFlow: A Hands-on Introduction
A/B Testing at Scale: Accelerating Software InnovationA/B Testing at Scale: Accelerating Software Innovation
Cloud based data mining tools for storage, distributed processing, and machine learning systems for scientific dataCloud based data mining tools for storage, distributed processing, and machine learning systems for scientific data
Declarative, Large-Scale Machine Learning with Apache SystemMLDeclarative, Large-Scale Machine Learning with Apache SystemML
Using R for Scalable Data Science: Single Machines to Hadoop Spark ClustersUsing R for Scalable Data Science: Single Machines to Hadoop Spark Clusters
Deep Learning for Personalized Search and Recommender SystemsDeep Learning for Personalized Search and Recommender Systems
META: A Unifying Framework for the Management and Analysis of Text DataMETA: A Unifying Framework for the Management and Analysis of Text Data
Massive Online AnalyticsMassive Online Analytics
Mining Entity-Relation-Attribute Structures from Massive Text DataMining Entity-Relation-Attribute Structures from Massive Text Data
Anomaly Detection in NetworksAnomaly Detection in Networks
Large Scale Hierarchical Classification: Foundations, Algorithms and ApplicationsLarge Scale Hierarchical Classification: Foundations, Algorithms and Applications

Applied Data Science Invited Talks

Title
Professor Andy Berglund: Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy
Dr. Paritosh Desai: It Takes More than Math and Engineering to Hit the Bullseye with Data
Dr. Rajesh Parekh: Designing AI at Scale to Power Everyday Life
Dr. Josh Bloom: Industrial Machine Learning
Professor Longbing Cao: Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management
Dr. Nick Malizia: Spaceborne data enters the mainstream
Professor Jonathan P. How: Planning and Learning under Uncertainty: Theory and Practice
Eduardo Ariño de la Rubia: More than the Sum of its Parts: Building Domino Data Lab
Moderator: Usama Fayyad: Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?
Szilard Pafka: Machine Learning Software in Practice: Quo Vadis?
Professor Vipin Kumar: Big Data in Climate: Opportunities and Challenges for Machine Learning
Mohak Shah: Fireside Chat: AI For Automotive And Industrial Applications