ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM SIGKDD KDD)*

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The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), formerly the International Conference on Knowledge Discovery in Databases (KDD) and the Workshop on Knowledge Discovery in Databases, is organized by the Association for Computing Machinery’s Special Interest group on Knowledge Discovery and Data Mining (ACM SIGKDD). KDD is a premier interdisciplinary conference which brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

Since 1997 in conjunction with the KDD Cup – the annual Data Mining and Knowledge Discovery competition.

Conference History:

28th ACM SIGKDD KDD 2022, August 14-18, 2022, Washington, DC, USA
(Organizer: Department of Computer Science, George Mason University)

Program TBD

27th ACM SIGKDD KDD 2021, August 14-18, 2021, Singapore Virtual venue
(Organizer: School of Information Systems, Singapore Management University; School of Computer Science and Engineering, Nanyang Technological University; School of Computing, Department of Computer Science, National University of Singapore)

KDD Deep Learning Day 2021
Understanding the Limits and Pushing the Boundaries of Deep Learning

Workshops:
The Sixth International Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD’21)
Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond
PRISM KDD
Machine Learning for Consumers and Markets
BIOKDD 2021
The Fifth International Workshop on Automation in Machine Learning
3rd Workshop on Adversarial Learning Methods for Machine Learning and Data Mining
The Second International MIS2 Workshop: Misinformation and Misbehavior Mining on the Web
Machine Learning in Finance
International Workshop on Knowledge Graph
MLHat: International Workshop on Deployable Machine Learning for Security Defense
Data-Efficient Machine Learning
AdKDD 2021
Data Quality Assessment for Machine Learning
epiDAMIK 4.0: The 4th International workshop on Epidemiology meets Data Mining and Knowledge discovery
7th SIGKDD Workshop on Mining and Learning from Time Series
2nd ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (DeepSpatial’21)
CityBrain Forum
Data Science with Humans in the Loop
The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021)
3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2021
3rd International Workshop on Data Science for Social Good
Visualization in Data Science (VDS at ACM KDD and IEEE VIS)

Lecture-Style Tutorials:
Advances in Mining Heterogeneous Healthcare Data
Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber
Physics-Guided AI for Large-Scale Spatiotemporal Data
Causal Inference from Network Data
Challenges in KDD and ML for Sustainable Development
Machine Learning Robustness, Fairness, and their Convergence
Data Science on Blockchains
From Deep Learning to Deep Reasoning
Automated Machine Learning on Graph
Creating Recommender Systems Datasets in Scientific Fields
Machine Learning Explainability and Robustness: Connected at the Hip
From Tables to Knowledge: Recent Advances in Table Understanding
High-Dimensional Similarity Query Processing for Data Science
Adversarial Robustness in Deep Learning: From Practices to Theories
Data Efficient Learning on Graphs
Towards Fair Federated Learning
Simple and Automatic Distributed Machine Learning on Ray
Data Quality for Machine Learning Tasks
Data Pricing and Data Asset Governance in the AI Era
Deep Learning on Graphs for Natural Language Processing
Fairness in Networks: Social Capital, Information Access, and Interventions
AutoML: A Perspective where Industry Meets Academy
All You Need to Know to Build a Product Knowledge Graph
Graph Representation Learning: Foundations, Methods, Applications and Systems

26th ACM SIGKDD KDD 2020, August 23-27, 2020, San Diego, CA, USA Virtual venue
(Organizer: Department of Computer Science and Engineering, University of California San Diego; USC Machine Learning Center, Computer Science Department, University of Southern California)

Pre-conference Workshops: (Aug 24)
19th International Workshop on Data Mining in Bioinformatics (BIOKDD’20); 16th International Workshop on Mining and Learning with Graphs; International Workshop on Knowledge Graph: Mining Knowledge Graph for Deep Insights; Machine Learning in Finance; 6th Workshop on Mining and Learning from Time Series; The Fourth International Workshop on Automation in Machine Learning; The Second International Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD’20); [DSHealth] 2020 KDD workshop on Applied data science in Healthcare: Trustable and Actionable AI for Healthcare; epiDAMIK: Epidemiology meets Data Mining and Knowledge discovery; Fragile Earth: Data Science for a Sustainable Planet; Knowledge Graphs and E-Commerce; 2nd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2020; Humanitarian Mapping: Harnessing Data and Human-Machine Intelligence for Actionable Policy Decisions; The KDD’20 Workshop on Causal Discovery (CD20); Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM); Second Workshop on Adversarial Learning Methods for Machine Learning and Data Mining; MLHat: Deployable Machine Learning for Security Defense; The Second International Workshop on Smart Data for Blockchain and Distributed Ledger; 2nd Workshop on Data Science Standards – What do you need to know as a Data Scientist? Special Workshop Theme: Training Data Scientists of the Future; 1st KDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (DeepSpatial); KiML 2020: First International Workshop on Knowledge-infused Mining and Learning for Social Impact (Advancing Decision Making in Health, Crisis Response, and Finance) ;Data Science with Human in the Loop; The Second International Workshop on Intelligent Information Feed (KDDFeed)

Pre-conference Lecture-style Tutorials: (Aug 23)
Causal Inference Meets Machine Learning; Fairness in Machine Learning for Healthcare; Learning from All Types of Experiences: A Unifying Machine Learning Perspective; Scientific Text Mining and Knowledge Graphs; Learning with Small Data; Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web; Recent Advances in Multimodal Educational Data Mining in K-12 Education; Data Pricing – From Economics to Data Science; Advanced Deep Graph Learning: Deeper, Faster, Robuster, and Unsupervised; Multi-modal Network Representation Learning: Methods and Applications

Hands-on Tutorials:
Building Forecasting Solutions Using Open-Source and Azure Machine Learning; Robust Deep Learning Methods for Anomaly Detection; Put Deep Learning to work: Accelerate Deep Learning through AWS EC2 and ML Services; Neural Structured Learning: Training neural networks with structured signals; Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial; In Search for A Cure: Recommendation with Knowledge Graph on CORD-19; Deep Learning for Search and Recommender Systems in Practice; Intelligible and Explainable Machine Learning: Best Practices and Practical Challenge; DeepSpeed: System optimizations enable training deep learning models with over 100; Introduction to computer vision and realtime deep learning-based object detection

Note: The conference was scheduled for August 22-27, 2020

25th ACM SIGKDD KDD 2019, August 4-8, 2019, Anchorage, AK, USA
(Organizer: Department of Computer Science and Engineering, University of Minnesota; KenSci)

Pre-conference Workshops: (Aug 5)
8th International Workshop on Parallel and Distributed Computing for Large-Scale Machine Learning and Big Data Analytics; 1st International Workshop on Intelligent Information Feed; Deep Learning on Graphs: Methods and Applications (DLG’19); Deep Reinforcement Learning for Knowledge Discovery; 3rd International Workshop on Automation in Machine Learning; Applied data science in Healthcare: bridging the gap between data and knowledge; Epidemiology meets Data Mining and Knowledge discovery (epiDAMIK); Learning and Mining for Cyber Security (LEMINCS); From Data Integration to Knowledge Graphs: Challenges and Experiences; 4th International Workshop on Fashion and KDD (AI for fashion); Data Collection, Curation, and Labeling for Mining and Learning; Adversarial Learning Methods for Machine Learning and Data Mining; 5th Workshop on Mining and Learning from Time Series; 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD; AdKDD 2019 Workshop; 15th International Workshop on Mining and Learning with Graphs (MLG); Deep Learning for Education (DL4Ed)

Pre-conference Lecture-Style Tutorials: (Aug 4)
Deep Bayesian Mining, Learning and Understanding; Optimize the Wisdom of the Crowd: Inference, Learning, and Teaching; Interpretable knowledge Discovery Reinforced by Visual Methods; Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned; Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning; Learning From Networks: Algorithms, Theory, & Applications; Statistical Mechanics Methods for Discovering Knowledge from Production-Scale Neural Networks; Data Integration and Machine Learning: A Natural Synergy; Deep Reinforcement Learning with Applications in Transportation

Hands-on Tutorials:
Put Deep Learning to Work: A Practical Introduction using Amazon Web Services; Democratizing & Accelerating AI through Automated Machine Learning; Introduction to computer vision and realtime deep learning-based object detection; Deep Learning for NLP with TensorFlow; Deep Learning at Scale on Databricks; Deep learning for time series forecasting; Building and Productionizing Machine Learning With Kubernetes: A Tutorial For Data Scientists; Learning Graph Neural Networks with Deep Graph Library; From Graph to Knowledge Graph: Mining Large-scale Heterogeneous Networks Using Spark

24th ACM SIGKDD KDD 2018, August 19-23, 2018, London, United Kingdom

23th ACM SIGKDD KDD 2017, August 13-17, 2017, Halifax, NS, Canada

22th ACM SIGKDD KDD 2016, August 13-17, 2016, San Francisco, CA, USA

21th ACM SIGKDD KDD 2015, August 10-13, 2015, Sydney, Australia

20th ACM SIGKDD KDD 2014, August 24-27, 2014, New York, NY, USA

19th ACM SIGKDD KDD 2013, August 11-14, 2013, Chicago, IL, USA

18th ACM SIGKDD KDD 2012, August 12-16, 2012, Beijing, China

17th ACM SIGKDD KDD 2011, August 21-24, 2011, San Diego, CA, USA

16th ACM SIGKDD KDD 2010, July 25-28, 2010, Washington, DC, USA

15th ACM SIGKDD KDD 2009, June 28 – July 1, 2009, Paris, France

14th ACM SIGKDD KDD 2008, August 24-27, 2008, Las Vegas, NV, USA

13th ACM SIGKDD KDD 2007, August 12-15, 2007, San Jose, CA, USA

12th ACM SIGKDD KDD 2006, August 20-23, 2006, Philadelphia, PA, USA

11th ACM SIGKDD KDD 2005, August 21-24, 2005, Chicago, IL, USA

10th ACM SIGKDD KDD 2004, August 22-25, 2004, Seattle, WA, USA

9th ACM SIGKDD KDD 2003, August 24-27, 2003, Washington, DC, USA

8th ACM SIGKDD KDD 2002, July 23-26, 2002, Edmonton, Alberta, Canada

7th ACM SIGKDD KDD 2001, August 26-29, 2001, San Francisco, CA, USA

6th ACM SIGKDD KDD 2000, August 20-23, 2000, Boston, MA, USA

5th ACM SIGKDD KDD 1999, August 15-18, 1999, San Diego, CA, USA
(Organizer: Microsoft Research)

Pre-conference Tutorials: (Aug 15)
Classification and Regression: Money *Can* Grow on Trees; Scalable Algorithms for Mining Large Databases; Clustering Techniques for Large Data Sets: From the Past to the Future; Mining Unstructured Data; Combining Estimators to Improve Performance; Data mining by business users: integrating data mining in business processes

Pre-conference Workshops:
Large-Scale Parallel KDD Systems
Workshop chairs: Dr. Mohammed J. Zaki, Rensselaer Polytechnic Institute, email: zaki@cs.rpi.edu and Dr. Ching-Tien (Howard) Ho, IBM Almaden Research Center, email: ho@almaden.ibm.com .
Multimedia Data Mining

Research Track Sessions:
Data Reduction & Scalability; Implementations & Applications of Data Mining; Discovering Rules; Data Mining for Database Systems; Mining Temporal Data; Clustering Techniques; Classification Techniques, Posters & Demos

KDD Industrial Track

Keynotes/Invited Talks:
Data Mining: Crossing the Chasm

Panels:
Integrating Data Mining into Vertical Solutions; KDD, A Discussion on the Last 10 and Next 10 years; Data Snooping, Dredging and Fishing: The Dark Side of Data Mining

4th KDD 1998, August 27-31, 1998, New York, NY, USA

3rd KDD 1997, August 14-17, 1997, Newport Beach, CA, USA

2nd KDD 1996, August 4-8, 1996, Portland, OR, USA

1st KDD 1995, August 20-21, 1995, Montreal, Canada
(Organizer: Machine Learning Systems Group, Jet Propulsion Lab, California Institute of Technology; Computer Science Department, General Motors Research; In conjunction with International Joint Conference on Artificial Intelligence (IJCAI-95), Aug 20-25)

Sessions:
Databases and Data Mining; Causality and Bayes Networks; Rough Sets and Databases; Supervised Learning: Issues and Applications; Temporal Databases; Inductive Learning; KDD and Statistics; Poster and Demo Session

Keynotes/Invited Talks:
A Database Perspective on Knowledge Discovery; Intelligent Local Learning: Statistical Algorithms for Prediction with High Dimensional Data

Panels:
Commercial KDD Applications: The Secret Ingredients for Success

KDD Workshop 1994, July 31-Aug 1, 1994, Seattle, WA, USA

KDD Workshop 1993, July 11-12, 1993, Washington, DC, USA

KDD Workshop 1991, July 14-15, 1991, Anaheim, CA, USA

KDD Workshop 1989, August 20, 1989, Detroit, MI, USA
(Organizer: Knowledge Based Systems Department, General Telephone & Electronics Laboratories; In conjunction with International Joint Conference on Artificial Intelligence (IJCAI-89), Aug 20-25)

Sessions:
Data-Driven Discovery
Knowledge-Based Approaches
Systems and Applications

Summary Panel: Research Issues and Applications of Knowledge Discovery
Incorporating Knowledge Discovery into Expert Database Systems
Issues in Knowledge Discovery
The Discovery Machine
Requirements for Knowledge Discovery in Databases

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