Meetings/Workshops on Information & Knowledge Management, Big Data Computing in the United States (USA)

The ADT 2019 conference focus is on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of Computer Science, Economics and Operations Research in order to improve the theory and practice of modern decision support.

Algorithms, Argumentation Theory, Artificial Intelligence, Computational Social Choice, Database Systems, Decision Analysis, Discrete Mathematics, Game Theory, Machine Learning, Matching, Multi-agent Systems, Multiple Criteria Decision Aiding, Networks, Optimization, Risk Management, and Utility Theory

Operational research,    Neural Networks and Artificial Intelligence, Machine Learning

The American University Center for Data Science will run the Institute for Data Science and Big Data on campus, which is open to all under a university course credit or certificate option, both geared to working professionals. The institute will cover the basics of data science, including obtaining relevant data, cleaning and exploring data, creating models, stating inferences, making reliable predictions, and communicating findings. The course consists of a mixture of lectures, guest speakers, and group assignments. It will take place over 10 working days, Monday through Saturday, 9 AM to 5 PM. To apply, interested parties can submit a resume and letter of interest to dsi@american.edu.

Networks, Internet,    Search Engines and Data Mining

The advancement in computing and storage capabilities of modern computational clusters fosters use of novel statistical techniques in machine learning and deep networks. Such data-driven techniques allow one to learn model features and characteristics that are difficult for mathematical methods alone to reveal. Many computational methods achieve model and complexity discovery using methods that lie at the nexus of mathematical, statistical, and computational disciplines. Statistical methods often employ "big data" approaches that glean predictive capability from diverse and enormous databases of information. Emerging methods in machine learning and deep networks can provide impressive results. This workshop gathers researchers at the frontier of large-scale statistical computation, data science, tensor decompositions and approximations, and data-driven model learning, to focus on modern challenges that use data to reduce complexity of models.

Association of Computing Machinery (acm)