Joshit Mohanty, GRA & Ph.D. candidate in Systems Engineering at Old Dominion University, specializing in systems engineering and human-technology integration. His dissertation introduces the Complexity Paradox Boundary (CPB), a theoretical and computational framework that identifies when decomposition-based analytical approaches become invalid in complex systems. His research uses agent-based modeling and LLM-driven persuasion dynamics to quantify when local optimization and perspective coherence collapse into global contradiction. He also served as a Co-Principal Investigator for the U.S. Department of Defence, Military Sealift Command Division, where he contributed to TF-IDF and NLP-based pipelines to analyze SAMM maintenance text data, identify patterns, and support model validation, insight extraction, and decision analysis.
Azita Saeidi, GRA & Ph.D. student in the Department of Engineering Management & Systems Engineering at Old Dominion University. Her research focus is on Complexity, Cyber-Physical Systems (CPS), and Systems Engineering, with a particular focus on integrating these domains to address contemporary challenges in engineering. Azita has several publications in journals and conferences.
Farshid Javadnejad, GRA & Ph.D. student in Engineering Management & Systems Engineering at Old Dominion University. His research focuses on predictive maintenance, uncertainty quantification, and anomaly detection in dynamic systems. He has worked on system reliability and decision-making by applying advanced machine learning models such as Gaussian Mixture Models, Hidden Markov Models, and Deep Neural Networks. Farshid has published in peer-reviewed journals and conferences with Springer Nature, IEEE, and Taylor & Francis. He has received research funding from the Military Sealift Command, NSF, and the College of Sciences at ODU. His work addresses critical challenges in naval systems, high-stakes systems, and industrial system optimization.
Arman Ghavidel, GRA & Ph.D. candidate in Engineering Management and Systems Engineering at Old Dominion University. His research focuses on applying machine learning and data-driven methods to predictive modeling, particularly in healthcare, medical forecasting, and predictive maintenance. Arman has published in peer-reviewed journals and conferences with Springer Nature, IEEE, and Taylor & Francis. He has contributed to predictive maintenance projects funded by the National Science Foundation (NSF) and the Military Sealift Command. His work aims to enhance the predictive capabilities of machine learning models in healthcare and maintenance.
Rafi Soule, GRA & Ph.D. candidate, focused on integrating Human-Centered Design (HCD) and Participatory Design (PD) into early Mission Engineering to improve decision-making and system resilience. Her dissertation focuses on developing frameworks that link technology and human factors to advance user-centered design in complex systems. She is involved in integrating Digital Threads (DT) with Model-Based Systems Engineering (MBSE) for the Military Sealift Command (MSC) and examining its role in decision-making, operational analysis, maintenance, and reliability. It aims to represent DT effectively within MBSE to capture data relationships, create comprehensive views of DT concepts, and enhance lifecycle management through improved data flow and decision-making.
Cansu Yalim, GRA & Ph.D. candidate and research assistant in the EMSE Department. She holds a bachelor’s degree in industrial engineering and a master’s degree in Engineering Management. Her professional experience spans the thermotechnology and automotive industries, where she has contributed to a wide range of initiatives focused on operational excellence and strategic workforce optimization. In the thermotechnology sector, Yalim has led numerous continuous improvement initiatives across planning, sourcing, manufacturing, and delivery processes. Her primary research interests lie in developing data-driven solutions for root cause diagnosis in mechanical systems, with a focus on leveraging advanced analytical tools to increase system reliability and performance.
Jiajun Jiang, GRA & Ph.D. candidate in the Electrical and Computer Engineering department at Old Dominion University, USA. Currently, he is working toward his dissertation under Dr. Chung-Hao Chen’s supervision. Previously, he received his BS degree in Electrical and Electronic Engineering from the University of Electronic Science and Technology of China, China, and his MS degree in Mathematics and Physics from North Carolina Central University, USA. His research interests include computer vision, digital image processing, machine learning, deep learning, and video forensics.
Sergio Pallas Enguita, is a PhD student in the Department of Electrical and Computer Engineering at Old Dominion University. He received his B.S. in Electrical Engineering with a minor in Mathematics from Florida International University (FIU). He is currently working under Dr. Chen Chung-Hao in the SMART Lab, where his interests are machine learning, computer vision, image processing, and sensor technologies, particularly in their application to biological and maintenance systems.
Shahab Uddin, GRA & Ph.D. candidate in the Department of Electrical and Computer Engineering at Old Dominion University (ODU). He joined ODU in 2019 and has successfully passed his proposal stage. He is expected to graduate in Spring 2025. His research interests include applied machine learning, with applications ranging from image analysis and large vision models to radiation prediction.
Omid Rajabi Rostami, GRA & Ph.D. candidate in the Department of Electrical and Computer Engineering at ODU. He joined ODU in Spring 2020 and has successfully passed his dissertation proposal. He is expected to graduate in Fall 2025. His research interests include trustworthy AI and generative AI models for image analysis.
Isabel Xiao is a junior undergraduate student in the Department of Computer Science at the University of Virginia. He worked as a summer intern in 2024 on the project.