Biography

Dr. Nelly Elsayed is an Assistant Professor, founder and leader of the Applied Machine Learning and Intelligence (AMLI) Lab at the School of Information Technology at the University of Cincinnati. Her research focuses on Applied AI and Machine Learning for Healthcare Informatics, Cybersecurity, Smart Technologies, Computer Vision, and Business Intelligent Solutions. She received a BS. and MS. degree in Computer Science from Alexandria University and her MS. and Ph.D. from the University of Louisiana at Lafayette. She is an active member of the IEEE Computational Intelligence Society. She has served as a principal and co-principal investigator in different federal, educational, and industrial level-funded research projects. She received the Faculty Incentive Award for Research and Scholarship from the CECH, UC, recognizing her research contributions, journal and conference peer-reviewed publications, and professional presentations in 2021 and 2025. She received the Love of Learning Award from the Honor Society Phi Kappa Phi in 2019, 2021, and 2023. She received the Golden Apple Award for Excellence in Teaching (Graduate Level), CECH. She received the UCAADA Sarah Grant Barber Outstanding Advising Faculty Award for from the University of Cincinnati. She has been an Ambassador for Goodwill of Lafayette, Louisiana, since 2017. Dr. Nelly Elsayed has been nominated for the for the Presidential Awards for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM) which is the most prestigious mentoring award in the Nation.

Talk Title: Beyond the Speech: Understanding Behaviors and Mental Health Disorders in the Era of Applied AI

Abstract:

In an age where artificial intelligence increasingly shapes our daily lives, its application to mental health offers both profound promise and critical responsibility. This presentation explores the transformative role of speech emotion recognition in identifying and understanding mental health disorders. By moving beyond traditional diagnostic methods to reveal underlying behavioral and psychological patterns. Grounded in applied AI, this presentation work demonstrates how machine learning models can be trained not only to recognize emotions but also to support early detection, continuous monitoring, and personalized intervention strategies.

Biography

Dr. Syed Attique Shah is a Senior Lecturer in Smart Computer Systems at the Department of Computer Science, Birmingham City University (BCU), UK. He also serves as the Course Lead/Director for the MRes in Computing and MSc Advanced Computer Networks at BCU. With over 12 years of experience in teaching and research, he has established a distinguished academic career with expertise in computer science. Prior to joining BCU, he held positions as a Lecturer/Assistant Professor at the Data Systems Group, Institute of Computer Science, University of Tartu, Estonia, and as an Associate Professor and Chairperson of the Department of Computer Science at BUITEMS, Quetta, Pakistan. Dr. Shah completed his Ph.D. at Istanbul Technical University, Turkey, in 2019.

His research focuses on cutting-edge areas such as Machine Learning, Data Science, Image Processing, Software-Defined Networking (SDN), and the Internet of Things (IoT). He has published over 30 Q1 journal papers with a cumulative impact factor exceeding 100, accumulating more than 2,800 citations and an h-index of over 24. He has served as an editor on special issues for journals such as Big Data and Cognitive Computing and IET Smart Cities, and has chaired sessions at top conferences (e.g., IEEE BigData 2023). His work has been presented at leading international conferences, further demonstrating his research impact.

Dr. Shah's leadership as Principal Investigator (PI) on two major UK-funded projects highlights his success in securing funding (£100k total) and driving innovation. He is spearheading the Alan Turing Institute/UKRI DTNet+ project (£50,000), establishing AI-enabled digital twin frameworks to model and optimise Positive Energy Districts using real-time data and intelligent decision support. As PI on another EPSRC/UKRI/DfT project (£50,000) under the National Hub for Decarbonised, Adaptable, and Resilient Transport Infrastructures (DARe) Transport Hub, he applied multi-agent systems and federated learning to develop AI strategies enhancing climate resilience in UK transport infrastructure.

He has also contributed to multiple funded research projects as Co-Principal Investigator and Project Lead. His research integrates interdisciplinary approaches to drive technological advancements, with a focus on shaping the digital future and adapting to emerging technological trends. Committed to excellence in teaching, research, and professional development, Dr. Shah continues to inspire students and colleagues alike while advancing the field of computer science through his scholarly contributions.

Biography

Dr. Ala Al-Fuqaha is the Associate Provost for Teaching and Learning at Hamad Bin Khalifa University (HBKU). He is a professor at the Information and Computing Technology division, College of Science and Engineering, HBKU. His research interests include the use of machine learning in general and deep learning in particular in support of the data-driven and self-driven management of large-scale deployments of IoT and smart city infrastructure and services, ethical aspects of AI deployments, Wireless Vehicular Networks (VANETs), cooperation and spectrum access etiquette in cognitive radio networks, management and planning of software- defined networks (SDN), and engineering education. He is a senior member of the IEEE, a senior member of the ACM, and an ABET Program Evaluator (PEV) and commissioner. He served on editorial boards of multiple journals, including IEEE Communications Letters, IEEE Network Magazine, and Springer AJSE. He also served as chair, co-chair, and technical program committee member of multiple international conferences, including IEEE VTC, IEEE Globecom, IEEE ICC, and IWCMC.

Talk Title: Socially Good Applications In the Era of Generative AI: Privacy, Reliability, and Impact on Smart Cities and Education

Abstract:

In this talk, we explore the theme of socially good applications in the era of generative AI, with a focus on two high-impact domains: smart cities and education. Additionally, this talk highlights our ongoing research contributions in two areas: (1) adversarial ML for social good, where adversarial attacks and defenses are leveraged to strengthen system reliability and resilience, and (2) generative AI for social good, where we enhance the reliability and privacy of the Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). By presenting representative studies from our work, we demonstrate how these research directions converge to address fundamental concerns of privacy and reliability, ensuring that emerging AI technologies, including Adversarial ML and Generative AI, can be leveraged to develop applications with a positive impact on society.