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.