Dr. Jonathan Vance is a Lecturer in the School of Computing within the Franklin College of Arts and Sciences at the University of Georgia. He earned both his Ph.D. (2023) and B.S. (2009) in Computer Science from the University of Georgia. His research and teaching center on artificial intelligence, with a focus on practical applications of machine learning.

Dr. Vance’s work explores the use of machine learning techniques in areas such as precision agriculture and audio analysis, combining computational models with real-world data to enhance understanding and outcomes in these domains. His broader interests include artificial intelligence in agriculture, deep learning, climate science, image processing, and audio processing.

Thirimachos Bourlai is a professor in the School of Electrical and Computer Engineering and has been an Adjunct Faculty at the Institute for Cybersecurity and Privacy, both at the University of Georgia. He also serves as an adjunct faculty at West Virginia University in the Lane Department of Computer Science and Engineering and in the School of Medicine’s Department of Ophthalmology. He is the founder and director of the Multi-Spectral Imagery Lab; a Series Editor of Advanced Sciences and Technologies for Security Applications; and an Associate Editor for Elsevier’s Pattern Recognition and the IET Electronics Letters. His professional service includes the Board of Directors of the Document Security Alliance, former Vice President for Education of the IEEE Biometrics Council, and membership in the Academic Research and Innovation Expert Group of the Biometrics Institute. He has published four books in biometrics and identity management, along with numerous conference papers, book chapters, and magazine articles.

Bourlai completed a Ph.D. in face recognition and a postdoctoral appointment at the University of Surrey’s CVSSP Group, followed by a second postdoc in thermal imaging and human-based computational physiology in a project between Methodist Hospital and the University of Houston. He joined WVU in 2009, serving in multiple faculty roles through 2017. He has chaired and served on committees for leading biometrics and computer vision conferences and has been invited to present to agencies and organizations including the CIA, NSA, U.S. Secret Service, U.S. Army, FBI, Amazon, and several universities. His research centers on technologies for supporting, confirming, and determining human identity in challenging conditions using face images across the imaging spectrum (UV, visible, NIR, SWIR, MWIR, LWIR), with additional work on iris, fingerprints, ears, tattoos, liveness detection, mobile biometrics, multi-spectral eye and pupil detection, and image restoration for ID document mugshots. He has taught courses including Pattern Recognition/Machine Learning, Deep Learning, Advanced Biometric Systems, Human-Computer Interaction and Programming, Computer Systems Security, and Biomedical Imaging.

Dr. Jin Lu specializes in various fields, such as machine learning, data mining, optimization, smart mobility, and the informatics of both biomedical and health sectors. A key area of interest for Dr. Lu is the development of machine learning models with provable outcomes, along with the advancement of distributed learning algorithms and optimization techniques.

Dr. Pidaparti’s research interests are in the broad areas of multidisciplinary design innovation and informatics, AI and Machine Learning applications, Biomedical Engineering and Bio-inspired Design and Intelligence. He develops and applies AI/ML models to a variety of engineering and computing applications. Currently, he is studying bio-inspired intelligence in self-assembly systems, AI for Middle School Teachers and STEM education. He is a member of professional societies including Fellow of American Association for the Advancement of Science; Fellow of Royal Aeronautical Society; Fellow of American Society of Mechanical Engineers; Associate Fellow of American Institute of Aeronautics & Astronautics; and member of American Society of Engineering Education.

Fei Dou is an Assistant Professor in the School of Computing at the University of Georgia. She earned her Ph.D. (2023) in Computer Science and Engineering from the University of Connecticut, where she worked in the Laboratory of Machine Learning & Health Informatics under Prof. Jinbo Bi. Her work contributes to fundamental machine learning for the Internet of Things and cyber-physical systems, particularly federated learning, reinforcement learning, and self-supervised learning.

Dou’s research advances machine intelligence for ubiquitous computing in distributed systems, emphasizing scalable, responsible, and context-aware AI. Key areas include human-centered sensing and indoor intelligence; federated and distributed ML for decentralized collaboration; multimodal and explainable AI for scientific problems (e.g., biological data integration); and sensing in underwater, remote, and low-power environments through resilient protocols and ML-based analytics.