Keynote Speakers
Biography
Dr. Monika Porwoł is a professional translator, scholar and university professor. Even though she specializes in Linguistics and Translation Studies, her expertise encompasses linguistic data analysis, e-lexicography, digital humanities, digital infrastructure, natural language processing (NLP) and artificial intelligence (AI).
She is a multidisciplinary researcher and teaching fellow at the English Department – Institute of Modern Language Studies – State University of Applied Sciences (Poland). She published her monograph entitled Unifying Concepts in Translation Analysis: Practical Resource Book (2021) with a special part dedicated to the concept of bridging language and technology. Moreover, she recently accomplished postgraduate studies in Data Science: Big Data and systems of advanced data analysis and Executive MBA. As a lifelong learner, she is highly engaged in investigating various approaches that empower communication through linguistics and AI technology.
Talk Title: Creativity in Society 5.0: the interplay of human ingenuity and AI
Abstract
As we transition into Society 5.0, a super-smart society where digital transformation harmonizes with human ingenuity, the interplay between creativity and artificial intelligence (AI) becomes increasingly pivotal. This keynote session explores the critical role of creativity within this context, particularly in the field of translation.
It will begin with an introduction to the concept of Society 5.0, highlighting its objectives and the essential role that creativity and innovation play in achieving its vision. A short historical overview of translation practices will be provided, tracing the evolution from traditional human methods to the integration of AI technologies.
A significant part of the discussion will focus on the importance of creative translation, showcasing case studies that demonstrate the unique contributions of human creativity. The benefits and limitations of current AI tools in translator education will be analyzed, emphasizing the necessity of integrating AI without compromising the creative processes that are vital to effective translation.
Strategies for blending AI tools with human creativity will be presented, providing examples of successful collaborations that highlight the potential of a hybrid approach. Additionally, the session will cover the skills and competencies required for future translators in Society 5.0, offering recommendations for curriculum design that balances traditional skills with modern technological proficiency.
Lastly, an exploration of future advancements in AI for translation and the ethical implications of these technologies, advocating for responsible AI use that enhances human creativity and decision-making will be presented.
Biography
Anmol Sood is the co-founder of Alai, a Y-Combinator backed startup, which allows people to create high quality presentations quickly using AI. Before founding Alai, he spent 5 years as a staff software engineer at Meta where he worked on building the recommendation system for Instagram Threads, Horizon Workrooms and Facebook Jobs. He has dealt extensively with leveraging the most recent breakthroughs in AI and VR research into useful, scalable and reliable products.
Talk Title: Building AI That Works: Challenges, Insights, and Practical Solutions
Abstract
In the last few years, we have witnessed rapid advancements in the fundamental capabilities of AI models. The AI landscape is evolving at an unprecedented pace, with significant improvements emerging every month. While these developments have unlocked numerous new opportunities, leveraging these models to create genuinely useful AI products remains a complex challenge.
Using practical examples, we will discuss some of these key challenges and how to approach them:
- Providing Context for AI Models: Making sure AI systems understand all the relevant context is critical.
- Choosing and Prompting AI Models: There are a large number of models and approaches possible in terms of how to prompt them.
- Human-AI Collaboration: Developing interfaces and controls that enable effective human-AI collaboration, recognizing the unique strengths of each.
- Monitoring and Safety: Strategies for ongoing monitoring, ensuring safety, and establishing feedback loops to continually improve AI systems.
Biography
Dr. Ahmed Ammar holds master’s and Ph.D. degrees in electrical engineering from West Virginia University. Currently, he serves as an assistant professor in the Department of Electrical and Computer Engineering and Computer Science at Ohio Northern University. His research focuses on energy harvesting communication systems and high-precision hardware design. Dr. Ammar has secured funding for his research endeavors and has collaborated with various professionals in the field. Notably, one of his research contributions has been honored with the Best Paper Award. He actively participates in organizing committees for several conferences and is deeply engaged in educational research and activities. Recently, he co-led an education workshop at Ohio Northern University and was invited as a speaker at the KEEN national conference.
Talk Title: Machine Learning in Energy Harvesting Cooperative Communication Systems
Abstract
Leveraging energy harvesting technology in conjunction with cooperative communications substantially enhances the efficiency of wireless communication systems. This efficiency is further improved when multiple relays are employed. However, common challenges such as relay selection, scheduling, and synchronization become more complex due to the rapidly changing channel characteristics and the stochastic nature of energy harvesting processes.
These challenges have been extensively addressed in the literature, spanning various scenarios with diverse objectives, such as maximizing secrecy performance, minimizing outage probability, mitigating interference, and maximizing throughput. While many proposed algorithms approach near-optimal or optimal performance, they frequently entail high complexity, extended run-time, or the need for additional information. Recently, machine-learning techniques have emerged as promising solutions for these challenges, applicable to both energy harvesting and conventionally battery-powered systems. These techniques yield performance ranging from moderate to optimal, and offer resilient, low-complexity solutions.
This talk will address the common challenges encountered in energy harvesting wireless sensor networks, particularly in relay selection, scheduling, and synchronization, along with proposed solutions. It will also explore the advantages of integrating machine learning into the system, alongside discussions on the related challenges and potential future directions.
Biography
Zag ElSayed was born in Odessa; he is a computer engineering scientist specializing in Brain Machine Interface, Artificial Intelligence, Machine Learning, VLSI Design, Cybersecurity, I2oT as well as IoE. He received his B.Sc. and M.Sc. with Distinction degree of Honor from Alexandria University in 2005, where he implemented the earliest framework architecture for Industrial Internet of Things (IoT) implementation. Zag got his second M.Sc. and Ph.D. in Computer Engineering from the University of Louisiana at Lafayette in 2016. Currently Zag is an assistant professor at the School of Information technology the University of Cincinnati, Ohio. Zag also is and active consultant of IT automation for industrial companies in Louisiana, Oklahoma and Texas. He worked as a research engineer in Africa, Europe, and the USA. Since 2014, he has been a system designer, automation architect and developer for leading oil and gas research companies specializing. He is fluent in nine languages, a nationally recognized painting artist, and a registered Red Cross ERV volunteer. Zag believes the key to understanding the Universe is ciphered in the human brain. His talk was given at a TEDx event, https://youtu.be/BvYRSQNZ67c.
Talk Title: Space, Time and Signal, what it takes to create a thought: BCI Chronicle
Abstract
The intersection of neuroscience, technology, and machine learning has propelled Brain-Computer Interface (BCI) systems to the forefront of scientific innovation. This keynote dives into the dynamic realm where space, time, and neural signals converge, revealing the transformative potential of decoding the human mind. Zag will guide participants through the evolution of BCI technology, from its early experimental stages to its status as a groundbreaking tool for direct brain-to-computer communication.
As we dive into the intricate mechanisms that allow brain signals to be translated into actionable data, this talk will explore how these advancements are reshaping our understanding of thought itself. The keynote will highlight the role of machine learning in enhancing BCI systems, enabling more natural and intuitive interactions between humans and machines. By examining the latest developments, participants will gain insight into how these innovations are poised to revolutionize fields as diverse as healthcare, communication, and immersive virtual environments.
Standing at the crossroads of neuroscience and technology, this keynote invites attendees to envision a future where the seamless integration of human cognition and computational power opens new horizons of possibilities. Join us for an inspiring exploration of the immense potential that lies within the neural architecture of the human brain and how it is being harnessed to create the next generation of intelligent systems.
Biography
Dr. Tala Talaei Khoei is a professor in the Khoury College of Computer Sciences at Northeastern University, based at the Roux Institute. Prior to joining Khoury College in 2024, she received her doctorate in Electrical Engineering from the University of North Dakota, where she also worked as a graduate teaching and research assistant. She also got her Master of Information Technology from Southern New Hampshire University.
Her research interests encompass inclusivity in computer science, integrating computational thinking and data science education, data science and mining, artificial intelligence, deep learning, and reinforcement learning. Dr. Khoei enjoys working on projects associated with data quality, visualization, and interpreting data in different networks. Notably, she has a track record of publications in top-tier journals and high-ranked conferences and was awarded Best Paper Presenter at the 13th IEEE UEMCON. She also served in several IEEE conferences, such as SmartNet, ICMI and several others.