The University of Alabama Where Legends Are Made

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New Fall 2025 Faculty

Learn about the new CS faculty who will be arriving in Fall 2025!

Dr. Runlong Yu

Assistant Professor

Research Areas:
AI for Science, Physics-Guided Machine Learning, Foundation Models

Dr. Runlong Yu’s research focuses on advancing artificial intelligence and data science to address real-world challenges with significant societal and scientific impact. A central theme of his work is the development of machine learning models that integrate scientific knowledge to facilitate adaptation to dynamic environments. His interdisciplinary approach has broad applications across domains such as hydrology, aquatic science, wildfire science, climate science, agriculture, and biomedicine.

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Dr. Runlong Yu

Dr. Noorbakhsh Amiri Golilarz

Dr. Noorbakhsh Amiri Golilarz

Assistant Professor

Research Areas:
AI, Deep Learning, Computer Vision, Pattern Recognition, Image Processing

Noor Amiri is an Assistant Professor in the Department of Computer Science at The University of Alabama. Prior to joining UA, he served as an Assistant Research Professor in the Department of Computer Science and Engineering at Mississippi State University. Before that, he was a Postdoctoral Research Fellow at Boston College in Massachusetts, USA. Amiri holds two Ph.D. degrees: one in Computer Science and Technology from the University of Electronic Science and Technology of China, and another in Electrical and Computer Engineering from Southern Illinois University Carbondale, USA. His research interests include artificial intelligence, deep learning, computer vision, pattern recognition, and image processing. He has published various peer-reviewed articles and has served as a lead guest editor for multiple SCI-indexed journals. In 2024, he co-founded AI Letters, a double-blind peer-reviewed journal, where he currently serves as the Associate Editor-in-Chief.

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Dr. Mahmoud Mahmoud

Associate Professor

Research Areas:
Trustworthy AI, Adversarial Machine Learning, Cyber-physical Systems

Dr. Mahmoud is an Associate Professor at the University of Alabama. He received his BS and MS in Computer Engineering from Cairo University and his PhD in Electrical and Computer Engineering from Tennessee Tech University in 2019. He previously served as Assistant and then Associate Professor at North Carolina A&T State University from 2019 to 2025. His research focuses on trustworthy AI, adversarial machine learning, explainable AI, and cyber-physical systems, with applications in UAV security, smart grids, and vehicular networks. Dr. Mahmoud has published in leading journals such as IEEE Transactions on Dependable and Secure Computing, IEEE Internet of Things Journal, IEEE Transactions on Smart Grid, and IEEE Transactions on Mobile Computing, and presented at top international conferences. His work has been supported by agencies including NSF, DOT, DoN, CIA, NASA, Intel, Cisco, and Lockheed Martin. He currently serves as an editor for the IEEE Transactions on Dependable and Secure Computing.

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Dr. Mahmoud Mahmoud

Dr. Dibbo Sayanton

Dr. Sayanton Dibbo

Assistant Professor

Research Areas:
Security and Privacy, AI/ML, Trustworthy Generative AI, IoT Authentication

Dr. Sayanton Dibbo’s research mission is to identify and systematically study adversarial and privacy threats to AI/ML systems under different realistic setups/assumptions, including Generative AI models, and develop innovative defense frameworks/tools to mitigate AI/ML vulnerabilities. His primary objective is to improve the AI/ML systems’ robustness to ensure the AI/ML systems are more secure and trustworthy. AI/ML cyberattacks (i.e., adversarial and privacy attacks) aim to make the model/system vulnerable by generating incorrect predictions or allowing the AI/ML systems to leak/infer sensitive private training data. This scenario is even more threatening in the case of generative AI (GenAI) models. As a result, innovative and cutting-edge defense tools are necessary to ensure secure and trustworthy AI/ML computations by improving the robustness of the AI/ML systems against cyberattacks.

Dr. Dibbo’s research integrates AI/ML and Security/Privacy domains. In particular, his research investigates the impact of different security and privacy threats on different data modalities, including images, texts, tabular, and audio data. His research vision is to develop novel tools that can mitigate adversarial and privacy attacks targeting AI/ML systems involving various data modalities. Dr. Dibbo’s outstanding research direction enabled receiving various prestigious awards, such as the Dartmouth Cybersecurity Research Cluster Pre-Doctoral Research Fellowship, NSF Secure & Trustworthy Cyberspace (SaTC) Aspiring PI Award, and Center for Non-Linear Studies (CNLS) Fellowship from the National Lab. His work has been published in top-tier AI/ML and Cybersecurity conferences and journals, including USENIX Security, IEEE Computer Security Foundation (CSF), IEEE Secure and Trustworthy ML (SaTML), IEEE Transactions on Dependable and Secure Computing, ACM Conference on Computer and Communications Security (CCS), and European Conference on Computer Vision (ECCV). He has also served as a reviewer and technical program committee member in several top ACM/IEEE/Springer conferences and journals.

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Dr. Tu Le

Associate Professor

Research Areas:
Security and Privacy, Human-centered Computing, Technology Policy

Dr. Tu Le’s research interests broadly include Security and Privacy, Human-centered Computing, and Emerging Technologies, with applications to smart systems and public policy. He specifically focuses on usable security and privacy for emerging technologies (e.g., IoT, AI, XR) and the implications of technology and policy. The overarching goal is to bridge the gaps between computing applications’ behaviors and user perceptions/preferences, as well as inform secure and privacy-respecting designs for future smart systems.

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Dr. Tu Le

Dr. Kaushik Roy

Dr. Kaushik Roy

Assistant Professor

Research Areas:
Neurosymbolic AI, Knowledge-based Learning and Reasoning, AI Systems, AI for Social Good

Dr. Roy’s research focuses on developing neurosymbolic methods for declarative and process knowledge-infused learning, reasoning, and sequential decision-making, with a particular emphasis on social good applications. His research interests span machine learning, artificial intelligence, and their application in social good settings. He is passionate about pushing the boundaries of AI, particularly in areas where it intersects with human knowledge and understanding, and decision-making.

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