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Keynote Speaker


 

  - Speaker: Dr. Thomas Coughlin (President, Coughlin Associates, Inc., San Jose, Calif., USA)

  • Title: The Memory of Artificial Intelligence.
  • Abstract of the talk
    The use of various types of artificial intelligence (AI) is increasing in the data center, in factories, at the network edge and even in endpoint devices, such as smart watches. AI training and inference requires processing a lot of digital data. Traditional von Neumann computer architectures consume a lot of energy moving data around. This talk will look at processing closer to memory (including persistent memory such as MRAM, RRAM and FRAM) and storage from the data center to endpoint devices. This processing will require new computer architectures using UCIe, CXL, OMI and NVMe-oF that enable processing closer to where the data lives. These new architectures will soon dominate many computing tasks.
  • Short bio
    Tom Coughlin, President, Coughlin Associates is a digital storage analyst and business and technology consultant. He has over 40 years in the data storage industry with engineering and senior management positions at several companies. Coughlin Associates consults, publishes books and market and technology reports (including The Media and Entertainment Storage Report and an Emerging Memory Report), and puts on digital storage-oriented events. He is a regular storage and memory contributor for forbes.com and M&E organization websites. He is an IEEE Fellow, Past-President of IEEE-USA, Past Director of IEEE Region 6 and Past Chair of the Santa Clara Valley IEEE Section, past VP of the IEEE CTSoc, Chair of the Consultants Network of Silicon Valley and is also active with SNIA and SMPTE. For more information on Tom Coughlin and his publications and activities go to www.tomcoughlin.com.




  - (Invited) Speaker: Dr. Md Zia Uddin (SINTEF Digital, Oslo, Norway)

  • Title: Sensors and Machine Learning for Assisted Living.
  • Abstract of the talk
    Worldwide, the total amount of people is growing day by day. As the elderly population with age 60 years or more is increasing quite faster than youth with age 10-24 years, there will be fewer people to take care of the elderly in the future. Hence, there should be the necessity of assisted living technologies to take care of people especially elderly or disabled, to help them living independent daily life. User care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care at assisted living systems to identify current practices for future research directions. This keynote presentation is aimed at a discussion of data sources (e.g., sensors) with machine learning for various smart user care approaches for assisting living technologies. Different types of data sources such as motion, video (i.e., RGB, depth, and thermal), sound, and wearables will be discussed in association with machine learning (e.g., deep learning and XAI) algorithms. Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. This presentation will discuss towards that direction.
  • Short bio
    Md Zia Uddin completed his PhD degree in Biomedical Engineering in 2011 from Kyung Hee University of South Korea. Then, he served as a faculty member in reputed universities with good world ranking such as Sungkyunkwan University (QS world ranking 88 in 2021). He is currently working as a Research Scientist in Sustainable Communication Technologies department of SINTEF Digital, Oslo, Norway. SINTEF is the largest research institute in Scandinavia and one of the largest research institutes in Europe. His research fields are mainly focused on data & feature analysis from various sources (sensors and others) for physical/mental healthcare using machine learning/artificial intelligence. Dr. Zia has a good teaching experience with more than 20 computer science-related courses from bachelor's degree to PhD. He has got more than 130 research publications including prestigious international journals (e.g., Information Fusion with impact factor of 12.98), conferences, and book chapters. His google scholar citations are more than 2500. He got Gold Medal Award (2008) for academic excellence in undergraduate study. He was also Awarded Korean Government IT Scholarship (March 2007 to February 2011) and Kyung Hee University President Scholarship (March 2007 to February 2011). His research works received best/outstanding paper awards in several peer reviewed international conferences. He acted as a reviewer in many prestigious journals including IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Information Fusion, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Biomedical Engineering, etc. Dr. Zia has been recently enlisted in the World’s Top 2% Scientists for Single Year 2019 and 2020 by Stanford University of USA and Elsevier BV. For more information: https://sites.google.com/site/webpagezia/home.


 

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