2nd International Workshop on Big Data Transfer Learning (BDTL) in Conjunction with IEEE BigData Conference 2017
-- Automatic Knowledge Mining and Transfer for Digital Healthcare

**Website**
http://www.cis.umassd.edu/~mshao/BDTL2017/index.html

**Submission**
http://www.cis.umassd.edu/~mshao/BDTL2017/submission.html

**Time**: Dec. 11th, 2017
**Location**: Boston MA, USA

**Important Date**
Oct 10, 2017: Due date for full workshop papers submission
Nov 1, 2017: Notification of paper acceptance to authors
Nov 15, 2017: Camera-ready of accepted papers
Dec 11, 2017: Workshops

The International Workshop on Big Data Transfer Learning (BDTL) is a serial workshop ever since 2016. The previous workshop in conjunction with IEEE BigData 2016 focused on the topic of Big Data Transfer Learning and Text Mining. This year, the one-day workshop in conjunction with IEEE BigData 2017 will provide a focused international forum to bring together researchers and research groups to review the status of transfer learning and knowledge mining, to exploit innovative knowledge transfer methodology given enormous weakly labeled/multi-source/multi-view/multimodal healthcare data for disease recognition/prediction, intelligent auxiliary diagnosis and emerging applications, and to explore future directions particularly in fields of increasing popularity such as deep learning, smart sensors and networks, wireless healthcare. The workshop will consist of one to two invited talks together with peer-reviewed regular papers (oral and poster). Original high-quality papers are solicited on a wide range of topics including:

* New perspectives, concepts, or theories on big data transfer learning and knowledge mining
* Big data transfer learning that works on multimodality, multi-source, latent domains, or multi-view healthcare data
* Development of analytics tools for emerging and profound digital healthcare problems
* Comparisons/survey of state-of-the-art analytics tools in health informatics
* Deep learning, representation learning and convolutional neural networks for big data analytics in digital healthcare
* Frontier label-free learning methodology for digital healthcare and health informatics, e.g., one-shot learning, self-taught learning, generative adversarial networks
* Wireless healthcare, smart sensor networks, wearable devices in big data analytics and digital healthcare
* New datasets, benchmarks, and open-source software for big data analytics in digital healthcare