Keynote Speakers
Prof. Olubayo Adekanmbi
Founder/CEO DSNai Lagos, Nigeria
Data-driven analytics for Agriculture
Biography
Olubayo (Bayo) is driven by a vision to build an AI-first society where Artificial Intelligence is effectively deployed to solve local problems, particularly the sustainable millennial goals. He believes that AI will provide a springboard for Africa’s development by enhancing how we live, work and play. He believes that Nigeria’s population of 200million with median age of about 18 years is a huge strategic advantage that can position Nigeria as one of the top 10 AI Knowledge centres in the world, especially the opportunity to raise 1 million AI talents in 10 years with globally relevant skills that can drive increased employability, FOREX inflow, AI-enabled start-ups and enhancement of the general quality of life through solution-oriented AI applications in Health, Agriculture, Financial Inclusion, Smart City etc. He is a leading expert in Data Science, Advanced Analytics, Business Transformation, Marketing and Strategy, with 19 years of cognate industry experience from two largest economies in Africa (Nigeria &South Africa).
Assoc. Prof. Nikola S. Nikolov
University of Limerick, Ireland
Domain-Specific Sentiment Analysis for Predicting Financial Market Behaviour
Domain-specific sentiment analysis can be utilised for effective predictive analysis of financial market data. While the availability of large amounts of textual data on social media and the advances in machine learning in recent years have led to significant progress in this field, domain-specific ontologies and both the semantic and syntactic characteristics of the text are yet to be fully utilised for effective sentiment analysis on text related to the financial markets. In this talk, the focus is on presenting a study that leads to the development of a deep-learning framework for financial market prediction based on sentiment analysis of data available on social media. Our framework is particularly tailored for the language used to express both formal and informal opinions about the financial markets. We show how considering the specifics of the financial markets domain leads to better results than a generic sentiment analysis approach. In this talk, we also mention the variety of text mining research projects by the Big Data and Analytics research group at the University of Limerick as a context of our work on sentiment analysis in the financial markets domain.
Biography
Dr Nikola S. Nikolov is a Senior Lecturer in the Department of Computer Science and Information Systems (CSIS) at the University of Limerick where he has been an academic faculty member since 2001. Dr Nikolov is a member of the IEEE Technical Committee on Visual Analytics and Communication and the author of more than 60 peer-reviewed research papers in network visualization and data mining, including articles in Discrete Applied Mathematics, Journal of Big Data, Wireless Communications and Mobile Computing, Journal of Medical Internet Research, and other journals. As a co-head of the Big Data and Analytics Research Group (BDARG), Dr Nikolov has organised more than 20 BDARG talks at UL with speakers from various Irish and international academic organisations and software companies. Dr Nikolov has worked as a principal investigator on research projects funded by Science Foundation Ireland (SFI) and the Irish Research Council, as well as a supervisor of PhD students funded by the SFI Centre for Research Training in AI, the SFI Centre for Research Training in Data Science, the China Scholarship Council, the Ministry of Education of Libya and the government of Saudi Arabia. Since 2016, Dr Nikolov has received multiple Erasmus+ mobility grants for establishing long-term collaboration between Laboratoire Systèmes Intelligents et Applications at University Sidi Mohamed Ben Abdellah in Morocco and BDARG. Since 2006, Dr Nikolov has acted as a course director of ten different CSIS programmes. Currently, he is the course director of the M.Sc. in Artificial Intelligence and Machine Learning (since 2020). Dr Nikolov has been actively involved in the design and delivery of Data Science related modules to various M.Sc. and B.Sc. programmes in UL, including the Irish Skillnet-supported national M.Sc. in AI (delivered online) and the M.Sc. in Business Analytics.
Prof. Habib Hamam
University of Moncton, Canada
It's time to understand the physical mechanism behind deep learning
Chess world champion, Magnus Carlson, has no chance of to resist against the intelligent moves of the deep learning-based Alpha-Zero system. Deep neural networks have greatly improved automatic speech recognition, visual recognition, language processing as well as the analysis of electroencephalograms, cosmology, and particle physics. The deep learning inherent to these networks has proven itself in almost all sectors including the economy, health, industry and education. New applications and opportunities have been created by this statistical approach. But a major question arises: Is there a physical mechanism or a mathematical theory behind deep learning? It is important to ask this question because it will allow us to better use deep learning and identify its limitations. This keynote speech explores this major question.
Biography
Habib Hamam obtained the B.Eng. and M.Sc. degrees in information processing from the Technical University of Munich, Germany 1988 and 1992, and the PhD degree in Physics and applications in telecommunications from Université de Rennes I conjointly with France Telecom Graduate School, France 1995. He also obtained a postdoctoral diploma, “Accreditation to Supervise Research in Signal Processing and Telecommunications”, from Université de Rennes I in 2004. He was a Canada Research Chair holder in “Optics in Information and Communication Technologies”, the most prestigious research position in Canada – which he held for a decade (2006-2016). The title is awarded by the Head of the Government of Canada after a selection by an international scientific jury in the related field. He is currently a full Professor in the Department of Electrical Engineering at Université de Moncton. He is OSA senior member, IEEE senior member and a registered professional engineer in New-Brunswick. He obtained several pedagogical and scientific awards. He is among others editor in chief and founder of CIT-Review, academic editor in Applied Sciences and associate editor of the IEEE Canadian Review. He also served as Guest editor in several journals. His research interests are in optical telecommunications, Wireless Communications, diffraction, fiber components, RFID, information processing, IoT, data protection, COVID-19, and Deep learning.
Prof. Kaoutar El Maghraoui
IBM Research AI, USA
Scaling Foundation Models: Paving the Future Path for Enterprise AI
Modern AI models are adept at learning from vast amounts of data, offering innovative solutions to intricate problems. Nevertheless, constructing these systems typically demands a substantial investment of time and a copious volume of data. The forthcoming evolution of AI introduces a paradigm shift, replacing task-specific models with versatile foundation models, which are trained on extensive unlabeled datasets and require minimal fine-tuning for various applications. These foundation models serve as the underpinning for many AI use cases. These models can effectively apply their generalized knowledge to specific tasks by employing self-supervised learning and fine-tuning techniques.
Foundation models are revolutionizing the adoption of AI within the business sector. Drastically reducing the labor-intensive tasks of data labeling and model programming will make it significantly more accessible for businesses to integrate AI into a wide array of mission-critical scenarios. This presentation will shed light on our strategy for extending the reach of foundation models to the enterprise, all within a seamlessly integrated hybrid-cloud environment. It will also showcase IBM Research's approach and vision to developing software, middleware, and hardware that facilitates frictionless, cloud-native development while harnessing the potential of foundation models for enterprise AI, focusing on practical industry use cases. The talk will also highlight upcoming research trends and offer insights into the future directions of foundation models.
Biography
Dr. Kaoutar El Maghraoui is an accomplished Principal Research Scientist at the prestigious IBM T.J. Watson Research Center and an Adjunct Professor of Computer Science at Columbia University in the City of New York. Kaoutar's work resides at the intersection of systems and artificial intelligence (AI). Within IBM, she spearheads the AI testbed at the IBM Research AI Hardware Center, a prominent global research hub dedicated to advancing efficient next-generation accelerators and systems tailored for AI workloads. Her leadership extends to open-source development and enhancing the cloud end-user experience for IBM's emerging Digital and Analog AI accelerators. Her research areas span systems research, distributed systems, high-performance computing, and efficient AI hardware-software co-design. Her academic journey includes earning a Ph.D. from Rensselaer Polytechnic Institute in the USA and a bachelor’s and master’s Degrees from Al Akhawayn University in Morocco.
Dr. El Maghraoui has received many accolades and awards throughout her career. These include the Robert McNaughton Award for best thesis in computer science, Best of IBM award in 2021, IBM’s Eminence and Excellence award for leadership in increasing Women’s presence in science and technology, several IBM outstanding technical accomplishments, 2021 IEEE TCSVC Women in Service Computing award, and 2022 IBM Technical Corporate award. Kaoutar is the global vice-chair of the Arab Women in Computing organization, a MoroccoAI non-profit organization board member, and an active member in many women in science and technology initiatives. She is an ACM Distinguished Member, Senior IEEE Member, and member of the Society of Women Engineers, TinyML foundation, and AnitaB.org.
Dr. Mohamed Ibnkahla
University Carleton, Canada
AI for Big IoT Data Analytics : From Sensor to Cloud
The Internet of Things (IoT) has made a tremendous impact on our society in almost every
sector, ranging from smart cities and e-Health to autonomous vehicles and energy. The
unprecedently large amounts of IoT data - Big IoT Data – has made the data collection, analytics
and decision-making cycle very challenging. Artificial Intelligence (AI) is playing an important
role to address these challenges through accurate analyrics and timely decision-making.
Moreover, with flexible deployment, ranging from the sensor board level to the Cloud, AI has
given designers and end users lots of potentials to beJer benefit from this technology.
However, there are still fundamental research questions that need to be addressed for a beJer
use of AI in this domain, including :
1- What are the best AI tools to be used for Big IoT Data analytics?
2- How the deployment strategies can affect AI performance?
3- What is the impact of AI tools on the IoT security threat landscape?
This plenary talk will give an answer to these quesrions through concrete examples and
industry-based use cases, and will provide the future trends for research in this field.
Biography
Dr. Mohamed Ibn Kahla joined the Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada in 2015 as a Full Professor where he holds the Cisco Research Chair in Sensor Technology for the Internet of Things (IoT) and the NSERC/Cisco Industrial Research Chair in Sensor Networks for IoT. He obtained the Ph.D. degree and the ”Habilitation a Diriger des Recherches” degree from the National Polytechnic Institute of Toulouse, Toulouse, France, in 1996 and 1998, respectively. Prior to joining Carleton University, he has been a Professor at the Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada, from 2000 to 2015. Over the past 10 years, he has been conducting multi-disciplinary research projects designing, developing and deploying secure and reliable IoT systems in various domains including intelligent transportation systems, e-Health, smart buildings, public health, renewable energies and smart grid, public safety and security, environment monitoring, and smart cities. He published 6 books and more than 70 peer-reviewed journal papers and book chapters, 20 technical reports, 110 conference papers, and 4 invention disclosures. He is the author of Wireless Sensor networks: A Cognitive perspective, CRC Press - Taylor and Francis, 2012 and Cooperative Cognitive Radio Networks: The Complete Spectrum Cycle, CRC Press - Taylor and Francis, 2015. In the past 5 years he gave more than 30 keynote talks and invited seminars. He received the Leopold Escande Medal, 1997, France, and the Premier’s Research Excellence Award, Canada, 2001. He is the joint holder of 5 Best Paper Awards, including IEEE GLOBECOM Conference, Workshop on Experimental Wireless Platforms, December 2022. Dr. Ibn Kahla has been recognized by IEEE Women in Engineering as “a man who supports the development and career growth of women… who actively changed the rules in the educational or working environment to promote inclusion”, May 2022.
Dr. Nabil ZARY
Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
Harnessing Intelligent Digital Health: Transformative Approaches for Strengthening Health Systems
Digital technology's quick development has sparked a paradigm change in healthcare, providing creative answers to problems confronting health systems worldwide. With a focus on how intelligent digital health may change healthcare delivery, improve patient outcomes, and lessen the load on healthcare systems, this keynote lecture explores the transformational potential of this technology. We'll start by giving a general review of intelligent digital health, describing the major theories, innovations, and uses that characterize this developing area. This includes a discussion of telemedicine, wearable technology, big data analytics, artificial intelligence (AI), and machine learning, among other innovations. We will then examine how these technologies could benefit health systems by enhancing accessibility, effectiveness, and care quality. We will present case studies demonstrating how intelligent digital health solutions may be successfully incorporated into various healthcare settings, including distant and resource-constrained ones. We will also examine how digital health may be used to manage chronic illnesses, promote preventative care, and address healthcare inequities. We will discuss the difficulties and moral issues related to the broad adoption of intelligent digital health in addition to the potential it presents. Data privacy, security, legislative frameworks, and the digital divide are among the subjects that will be covered. We will also take into account the possible effects of algorithmic bias and investigate methods to guarantee the equal creation and application of digital health technology. Finally, we will give suggestions for all parties involved in leveraging the power of intelligent digital health, such as legislators, healthcare professionals, and technology developers. We can collaborate to create a future health system that is more robust, egalitarian, and patient-centered by embracing these disruptive technologies.