Speaker: Dr. Danda B. Rawat, Howard University, Washington, DC, USA

Title of the Talk: Secure and Trustworthy Machine Learning and Artificial Intelligence for Emerging Systems and Applications: The Triumph and Tribulation

Biography: Dr. Danda B. Rawat is a Full Professor in the Department of Electrical Engineering & Computer Science (EECS), Founder and Director of the Howard University Data Science and Cybersecurity Center, Director of Cyber-security and Wireless Networking Innovations (CWiNs) Research Lab, Graduate Program Director of Howard CS Graduate Programs and Director of Graduate Cybersecurity Certificate Program at Howard University, Washington, DC, USA. Dr. Rawat is engaged in research and teaching in the areas of cybersecurity, machine learning, big data analytics and wireless networking for emerging networked systems including cyber-physical systems, Internet-of-Things, multi domain battle, smart cities, software defined systems and vehicular networks. His professional career comprises more than 18 years in academia, government, and industry. He has secured over $16 million in research funding from the US National Science Foundation (NSF), US Department of Homeland Security (DHS), US National Security Agency (NSA), US Department of Energy, National Nuclear Security Administration (NNSA), DoD and DoD Research Labs, Industry (Microsoft, Intel, etc.) and private Foundations. Dr. Rawat is the recipient of NSF CAREER Award in 2016, Department of Homeland Security (DHS) Scientific Leadership Award in 2017, Researcher Exemplar Award 2019 and Graduate Faculty Exemplar Award 2019 from Howard University, the US Air Force Research Laboratory (AFRL) Summer Faculty Visiting Fellowship in 2017, Outstanding Research Faculty Award (Award for Excellence in Scholarly Activity) at GSU in 2015, the Best Paper Awards (IEEE CCNC, IEEE ICII, BWCA) and Outstanding PhD Researcher Award in 2009. He has delivered over 20 Keynotes and invited speeches at international conferences and workshops. Dr. Rawat has published over 200 scientific/technical articles and 10 books. He has been serving as an Editor/Guest Editor for over 50 international journals including the Associate Editor of IEEE Transactions of Service Computing, Editor of IEEE Internet of Things Journal, Associate Editor of IEEE Transactions of Network Science and Engineering and Technical Editors of IEEE Network. He has been in Organizing Committees for several IEEE flagship conferences such as IEEE INFOCOM, IEEE CNS, IEEE ICC, IEEE GLOBECOM and so on. He served as a technical program committee (TPC) member for several international conferences including IEEE INFOCOM, IEEE GLOBECOM, IEEE CCNC, IEEE GreenCom, IEEE ICC, IEEE WCNC and IEEE VTC conferences. He served as a Vice Chair of the Executive Committee of the IEEE Savannah Section from 2013 to 2017. Dr. Rawat received the Ph.D. degree from Old Dominion University, Norfolk, Virginia. Dr. Rawat is a Senior Member of IEEE and ACM, a member of ASEE and AAAS, and a Fellow of the Institution of Engineering and Technology (IET).

Talk description: This keynote focuses on both AI for cybersecurity and cybersecurity for AI for emerging systems and applications. Lately, ML algorithms and AI systems have been shown to be able to create machine cognition comparable to or even better than human cognition for some applications. Machine learning algorithms are now regarded as very useful cybersecurity solutions for different emerging applications. However, because ML algorithms and AI systems can be controlled, dodged, biased, and misled through flawed learning models and input data, they need robust security features and trustworthy AI. It is very important to design and evaluate/test ML algorithms and AI systems that produce reliable, robust, trustworthy, explainable and fair/unbiased outcomes to make them acceptable by diverse users. The keynote covers applications and use cases of secure and trustworthy ML/AI and their success and pitfalls.

Speaker: Dr. Nicolas Sklavos, University of Patras, Hellas

Title of the Talk: In Hardware We Trust: Electronic Design Automation

Biography: Dr. Nicolas Sklavos is Associate Professor, in Computer Engineering and Informatics Department (CEID), Polytechnic School, University of Patras, Hellas. He is Director of SCYTALE Group. His research interests include Cryptographic Engineering, Hardware Security, Cyber Security, Digital Systems Design, and Embedded Systems. He has participated to a number of European/National, Research and Development Projects. He has received several scientific awards in the related areas of his research. He has participated to the organization of international scientific conferences, of IEEE/ACM/IFIP, serving several committee duties, as well as Editorial Board Member of Scientific Journals. He has authored technical papers, books, chapters, reports etc, in the areas of his research. His published works has been cited in several papers of other authors, in technical literature. He is Senior Member of IEEE, Associated Member of HiPEAC and member of IACR. His works, have received a great number of references, in scientific, technical literature. (Homepage:

Talk description: Modern handheld devices and systems are developed day by day, in order to satisfy the complexity of usersí needs and applications. Nowadays, integrated circuits (ICs) play a sensitive role in devicesí operation, since they are the main cores for almost each type of process and data transaction. The needs for high performance, minimized area, and less power, are more demanding each time, and electronic design automation (EDA), is oriented as a crucial factor, for these targets. Although, besides the traditional circuits and systems, design approaches, the arising threats in hardware each time, make very important the priority for secure hardware design, and trusted devices, at the same time. Traditional approaches of design and test, are argued, since most of the processesí parts, need considerations, assumptions and specifications, for both trustworthy and security in all metrics, including modeling and evaluation. This keynote talk, gives a detailed overview of hardware security and EDA approaches, including security threats, in integrated circuits, though the design cycle. It also, deals with, the countermeasures and the motivation of the prior art. Examples of modern applications are introduced, in sense of trusted hardware, and secure by design. Solutions, and alternative approaches are figured out, as well, detailed overview is discussed, for the expectations of future, for both usersí applications, and devices.

Speaker: Michael Losavio, Department of Criminal Justice, College of Arts and Science, University of Louisville, KY, USA.

Title of the Talk: Artificial Intelligence and the Internet of Things: Security and Autonomy

Biography: Michael Losavio teaches in the Department of Criminal Justice and the Department of Computer Science and Engineering at the University of Louisville, Louisville, Kentucky, U.S.A. on issues of law, society and information assurance in the computer engineering and justice administration disciplines. His focus is on law and social sciences as they relate to computer engineering, evidence and digital forensics. Courses include Digital and Computer Crime, Transnational Cybercrime and Legal Issues with Data Mining and Information Assurance. He holds a J.D. and a B.S. in Mathematics from Louisiana State University, Louisiana, U.S.A. He lectured on computer law and crime at Perm State University, Perm, Russian Federation.

Talk description: The Internet of Things and Artificial Intelligence offer tremendous opportunities through the powerful analytics against a vast distribution of sensors. They provide for the unprecedented collection of data for new, powerful analytics creating new knowledge, from government processes to industrial systems.

Yet these all have elements of the human condition that may affect the impact of their implementations, for good and for ill. The AI/IoT/Big Data world presents significant challenges in law, ethics and public policy. Such analytics and data-driven actions manifest all the challenges relating to the use of expert systems and information assurance, including authentication, validation and protection of this data. And we have seen that the data generation, transmission and collection begin to parallel issues similarly seen in information security and assurance.

With more and modeling and analysis, the predictive ability for behavioral inferences has increased, posing different legal, administrative and political concerns. We examine analyses of applications of these new powerful ways to learn for the cautionary lessons they can teach.