BigDat 2020 will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of big data, which covers a large spectrum of current exciting research and industrial innovation with an extraordinary potential for a huge impact on scientific discoveries, medicine, engineering, business models, and society itself. Renowned academics and industry pioneers will lecture and share their views with the audience.
BigDat 2020 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends.
BigDat 2020 will take place in Ancona (Italy).
The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration facility disabled when the capacity of the venue is exhausted. It is highly recommended to register prior to the event.
Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
The program is possibly modifiable.
- Jiawei Han (University of Illinois, Urbana-Champaign): « From Unstructured Text to TextCube: Automated Construction and Multidimensional Exploration » [advanced]
- Wladek Minor (University of Virginia): « Big Data in Biomedical » [introductory/advanced]
- Peter Rousseeuw (KU Leuven): « Anomaly Detection by Robust Methods » [introductory]
- Asim Roy (Arizona State University): « Hardware-based (GPU, FPGA based) Machine Learning – An Overview of Algorithms and Implementation Ideas and Methods » [intermediate]
- Hanan Samet (University of Maryland): « Multidimensional, Spatial, and Metric Data Structures for Sorting in Space and Similarity Searching » [intermediate]
- Rory Smith (Monash University): "Learning from Data, the Bayesian Way." [introductory/intermediate]
- Jeffrey Ullman (Stanford University): "Big-data Algorithms That Aren't Machine Learning" [introductory]
- Wil van der Aalst (RWTH Aachen University): "Process Mining: A Very Different Kind of Machine Learning That Can Be Applied in Any Organization" [introductory/intermediate]