Georgia Tech School of Electrical and Computer Engineering (ECE) Professor Ghassan AlRegib was selected to the 2026 IEEE Signal Processing Society Distinguished Lecturer class, recognizing his leadership in both research and academic instruction.

The Society's Distinguished Lecturer Program offers its chapters access to individuals who are well-known educators and authors in the field of signal processing to lecture at meetings. The effort enables chapters to engage with dynamic signal processing content and foster interest in a variety of topics.

As part of the program, Alregib will deliver lectures that span foundational academic topics and advanced research in signal processing and machine learning, including:

• Inferential Machine Learning: Towards Human-collaborative Foundation Models
• Ethics, Trust, and Responsible Deployment of Machine Learning
• Robustness in Machine Learning: Explainability, Uncertainty, and Intervenability
• A Foundation Model is Secretly a Signal Processing Pipeline
• From Pixels to Decisions: Image Understanding with Deep Learning
• Datasets Creation: the Good, the Bad, and the Ugly
• Learning AI for All “AI First” 

AlRegib, who holds the John and Marilu McCarty Chair Professorship in ECE, runs the Omni Lab for Intelligent Visual Engineering and Science (OLIVES) and serves as the director of the Center for Signal and Information Processing.

His research group develops robust and interpretable machine learning algorithms, focusing on uncertainty, trust, and human-in-the-loop systems. Their work spans a wide range of applications, including autonomous systems, medical imaging, and subsurface imaging. The group is committed to advancing both the theoretical foundations and real-world deployment of these technologies, according to AlRegib.

He is also a leading expert in AI curriculum development. He played a central role in developing Georgia Tech’s AI Makerspace, a campus-wide resource that provides students with hands-on access to AI tools and computing clusters. His work focuses on making AI education more accessible and experiential.

Two of the first courses to utilize the AI Makerspace were developed by Alregib:

• ECE 2806: AI First (AI Foundations) — a sophomore-level course open to all students that covers core AI concepts including data literacy, learning, decision-making, planning, and ethics.
• ECE 4252/8803: FunML (Fundamentals of Machine Learning) — an advanced course where students apply machine learning techniques to real-world datasets using the AI Makerspace.

He also leads a Vertically Integrated Projects team of over 60 students who build tools and services to enhance the AI Makerspace and broaden its reach across campus.

AlRegib is an IEEE Fellow, and his work has received a number of awards throughout his career, including the Denning Faculty Award for Global Engagement.