Title: DESAL+ Living Lab, an accessible place to carry out R&D&i related to desalination in the Macaronesia

Abstract: The Canary Islands counts with a large experience in the exploitation of desalination plants, almost 60 years since the first desalination plant in Europe was installed in Lanzarote. Currently, the existence of a great variety of plant sizes, with a wide diversity of technologies, design conditions and locations, availability of desalination infrastructures and pilot plants for experimentation, excellent availability of natural resources (sun, wind and sea) together with a relevant critical mass of high-qualified researchers, engineers and desalination plant operators; provides the ideal environment for the creation of this open space for R&D&i on desalination and the application of Renewable Energy Sources (RES).
Led by the Canary Islands Institute of Technology (ITC), this public-private platform consists of a coordinated partnership of research groups, local public institutions and companies, which cooperates in applied research on desalination making their resources and skills in this field available to the end-users.
DESAL+ LIVING LAB is an open-access research ecosystem with several experimental and real locations in the Canary Islands mainly. With partners in Cape Verde, Madeira and Mauritania too. Testing, experimentation and demonstration can be carried out for the purpose of promoting and maturing the commercial potential of a technology, product and/or service. The LIVING LAB has created the necessary conditions, infrastructures and protocols for access to desalination plants to enable universities, research and technology centers, manufacturers, companies, operators and end users to collaborate and cooperate, using all the resources available within the ecosystem.

Prof. Dr. Ulas Bagci
Northwestern University, USA

Ulas Bagci, Ph.D., is a tenured Associate Professor at the Northwestern University's Radiology, Electrical and Computer Engineering, and Biomedical Engineering Departments at Chicago, and courtesy professor at the Center for Research in Computer Vision (CRCV), department of computer science, University of Central Florida (UCF). His research interests are artificial intelligence, machine learning and their applications in biomedical and clinical imaging. Dr. Bagci has more than 280 peer-reviewed articles in these topics. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health’s radiology and imaging sciences department, center for infectious disease imaging. Dr. Bagci holds several NIH R01, U01, R15, and R03 grants (as Principal Investigator) and serves as a steering committee member of AIR (artificial intelligence resource) at the NIH. Dr. Bagci also serves as an area chair for MICCAI for several years and he is an associate editor of top-tier journals in his fields such as IEEE Trans. on Medical Imaging, Medical Physics and Medical Image Analysis. Prof. Bagci teaches machine learning, advanced deep learning methods, computer and robot vision and medical imaging courses. He has several international and national recognitions including best paper and reviewer awards.

Title: Trustworthy AI for Imaging-based Diagnoses

Abstract: In this talk, I will focus on the failures of deep learning / AI algorithms and propose several approaches to increase robustness of AI powered medical imaging systems. Roadmaps to such trustworthy systems will be analyzed: 1) algorithmic robustness, 2) interpretable / explainable machine learning systems, and 3) human in the loop machine learning system. For each of these I will give a layout. For algorithmic robustness, I will introduce a success story of a deep network architecture, called capsule networks, and demonstrate its effectiveness and robustness compared to commonly used systems; hence, increasing its trustworthiness to be used in high-risk application. For human in the loop system, I will share our unique experience for developing a paradigm-shifting computer-aided diagnosis (CAD) system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. Last, but not least, I will introduce our new algorithm developed to better localize regions where the algorithm learns. Compared to commonly used Grad-Cam algorithms, we obtain superior performance when depicting salient regions that are most informative. Lastly, I will discuss about future directions that medical imaging physicians and scientists should think when AI comes into play.

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Dr. Kaushal Shah
Pandit Deendayal Energy University, India

Dr. Kaushal Shah is currently an Assistant Professor in the Computer Science and Engineering department at Pandit Deendayal Energy University (PDEU). Dr. Kaushal Shah has vast teaching experience of more than ten years. Before joining PDEU, Dr. Kaushal Shah was an Assistant Professor, Sr. Grade I at Vellore Institute of Technology in the Computer Engineering Dept. Dr. Kaushal Shah did his Ph.D. at Sardar Vallabhbhai National Institute of Technology in Information Security. He was a gold medallist for being a branch topper in the Master of Engineering in Computer Science and Engineering from Gujarat Technological University. Dr. Kaushal Shah has received a financial grant from AICTE for conducting the ATAL Faculty Development Program. Dr. Kaushal Shah has also received a financial grant from the Government of Gujarat for the Student Start-up and Innovation Policy. Dr. Kaushal Shah is an active researcher in Information Security, Blockchain Technology, Cryptography, and Smart Grid. Dr. Kaushal Shah has also published several research articles and delivered keynote talks at reputed places.

Title: Towards Applying Blockchain Technology in Industry 4.0

Abstract: Industry 4.0, or the Fourth Industrial Revolution, refers to developing modern factories to achieve better performance, end-to-end connectivity, lower cost, and higher quality. Industry 4.0 aims at achieving interconnectivity and automation using technologies of the Internet of Things, Cloud Computing, Machine Learning, Artificial Intelligence, Big Data Analytics, or Robotics. However, better connectivity and improved automation come with their challenges. Trust and reliability, enhancing transparency, product traceability, developing an efficient supply chain, storing and sharing transactional data securely, and certification for quality compliance are among them. Due to its decentralization, immutability, authenticity, and transparency, blockchain technology has been in discussion for various industrial applications. In this talk, we will discuss how blockchain technology is applicable in various Industry 4.0 applications and how it can effectively tackle the issues they face. Firstly, we will mention the fundamentals of Industry 4.0. Then, we will illustrate how blockchain works and why and how it can contribute to Industry 4.0. Later, we will discuss the challenges of Industry 4.0 and how we can apply Blockchain technology in the age of Industry 4.0. Finally, the advantages and challenges of using blockchain technology to grow Industry 4.0 will also be discussed.