IEEE International Workshop on Data Analytics for Smart Health (DASH 2020)
December 10 - 13, 2020
Atlanta, GA, USA
https://sites.google.com/view/ieee-dash-2020

Co-located with the IEEE International Conference on Big Data (IEEE BigData 2020)
December 10 - 13, 2020
Atlanta Marriott Marquis
Atlanta, GA, USA
http://bigdataieee.org/BigData2020/

Introduction
Wearable Internet of Things (wIoT) together with deep learning is revolutionizing the smart health and wellbeing applications. Predominantly, IoT devices are good at acquiring medical data and later sending it to the cloud. Edge-based deep learning infuses intelligence in terms of processing, analysis, and inference on edge devices such as wearables. Edge-based deep learning not only offload the cloud but also ensure high-throughput, low-latency solutions. With edge deep learning, the data is processed on edge leading to improved privacy and security as now the data is not transferred to the cloud for inference. Resource constraints on the edge and endpoint IoT devices pose challenges in adopting deep learning solutions. Systems and algorithms deployed in health and fitness devices require research on efficient approaches for signal sensing, analysis, and prediction. Recently, deep learning models are increasingly deployed on wearable and edge devices for neural prediction and inference. Modern smartwatches and smart textiles are health as well as fitness devices. Deep learning on edge also allows for personalization of medical solutions that enhances the user’s experience. Increasingly more wearables in health and fitness now rely on voice-based assistants. Recently, several custom chips with medical machine learning functionalities are developed to further advance edge deep learning. We live in exciting times when wearables and deep learning are growing in parallel and together creating tremendous impact on smart health & fitness devices, systems, and services.

Call for Papers:
This workshop invites researchers from academia and industry to submit their current research for fostering academia-industry collaboration. The scope of this workshop includes but not limited to the following topics:

● Resource-constrained deep learning for wearable IoT

● Deep machine learning for sensing, analysis, and interpretation in IoT healthcare

● Low latency decoding on edge for smart health

● Deep learning & AI for regenerative medicine

● Knowledge transfer and model compressions of deep neural networks for smart health

● Deep learning-based health & fitness devices, systems, and services

● Recent advances in Edge, Fog and Mist computing for machine learning in healthcare & fitness application

● Context-aware pervasive health systems based on edge machine learning

● End-to-end deep learning for health and fitness applications

● Scalability, privacy, and security aspects in IoT medical big data

● Edge devices with custom hardware for medical deep learning

● Emerging applications of edge devices in fitness and smart health applications

● Deep learning for personalized health and fitness monitoring, tracking and control

● Information theoretic, semi-supervised and unsupervised machine learning for health and fitness applications

● Design and development of open-source tools for edge machine learning

● Edge-coordinated health data analysis, visualization, and interoperability

● Role of big data in edge-based machine learning for smart health & fitness applications

● Edge based machine learning for blockchain in smart health

● Edge machine learning for Neuromorphic AI and cognitive computing in smart health

● Bio-inspired machine learning for Fog computing systems in healthcare

● Data mining for wearables and mobile devices

● Data storage, retrieval and transfer between edge devices, gateways, and cloud backend

● Cloud-assisted backup and recovery for data mining in IoT

● Real-time knowledge discovery for IoT

● Knowledge graphs and knowledge representation for smart health and IoT

Important Dates:

● Workshop paper submissions: October 18, 2020 (Anywhere on Earth)
● Workshop paper notifications: November 1, 2020
● Camera-ready of accepted papers: November 15, 2020
● Workshop date: December 10, 2020

Submission link:
To be added.

Paper Submission and Publication:
Prospective authors are invited to submit full-length papers (up to 10 pages) for technical content including figures and references. Submitted paper must be formatted according to the IEEE Computer Society Proceedings Manuscript Formatting Guidelines (https://www.ieee.org/conferences/publishing/templates.html). Manuscripts should be original (not submitted or published anywhere else). Papers will be accepted only by electronic submission via IEEE Big Data 2020 system. All submitted papers will be subject to peer reviews by Technical Program Committee members. All presented papers will be published in the IEEE Big Data 2020 proceedings and digitally archived in IEEE Xplore.

Keynote speaker:
● Professor Sajal K. Das, Daniel St. Clair Endowed Chair Professor, Missouri University of Science and Technology, Rolla, Missouri, USA
● Professor Diane J. Cook, Regents Professor and Huie-Rogers Chair Professor, School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, USA
● Professor Lucila Ohno-Machado, Chair, Department of Biomedical Informatics, Associate Dean for Informatics and Technology, University of California, San Diego, California, USA
● Dorin Comaniciu, Senior Vice President for Artificial Intelligence and Digital Innovation at Siemens Healthineers, Princeton, New Jersey, USA

Workshop Organizers:
● Harishchandra Dubey, Microsoft Corporation, Redmond, WA, USA
● Xiaoqian Jiang, UT Health School of Biomedical Informatics (SBMI), Houston, TX, USA
● Arindam Pal, CSIRO's Data61 and Cyber Security CRC, Sydney, NSW, Australia

Publicity Chairs:
● Cosimo Ieracitano, University Mediterranea of Reggio Calabria, Italy
● Rabindra Kumar Barik, KIIT University, India

Technical Program Committee:

● Deepak Puthal, School of Computing, Newcastle University, U.K.

● Chandan K. A. Reddy, Microsoft Corporation, Redmond, USA

● Meysam Asgari, Oregon Health & Science University, Portland, USA

● Fang Bian, UT Dallas, USA

● Heather Hayenga, UT Dallas, USA

● Suman Banerjee, University of Wisconsin–Madison, USA

● Young-tae Kim, UT Arlington, USA

● George Alexandrakis, UT Arlington, USA

● Sunghoon Ivan Lee, University of Massachusetts Amherst, USA

● Srivalleesha Mallidi, Tufts University, Medford, USA

● Thomas Nieland, Tufts University, Medford, USA

● Debanjan Borthakur, McMaster University, Canada

● Arijit Biswas, Amazon

● Daniel Sadoc Menasche, Federal University of Rio de Janerio, Brazil

● Antonia Guto Rocha, Fluminense Federal University, Brazil

● Victoria Manfredi, Wesleyan University

● Bin Tang, California State University, Dominguez Hills

● Aruna Balasubramanian, Stony Brook University

● Yeon-sup Lim, IBM Research, USA

● Fabricio Murai, Universidade Federal de Minas Gerais, Brazil

● Zubair Shafiq, University of Iowa, USA

● Hui Lu, SUNY Binghamton

● Aditya Mishra, Seattle University

● Bhavna Dalvi, AI2

● Shobeir Fakhraei, Amazon

● Bo Jiang, Shanghai Jiatong University

● Ayan Acharya, Linkedin

● Cosimo Ieracitano, University Mediterranea of Reggio Calabria, Italy