Dr. Luchsinger investigates unconventional models of computation, including chemical reaction networks, molecular computing, and algorithmic self-assembly. His work develops rigorous theoretical frameworks for computation beyond traditional digital systems, exploring the expressive power and fundamental limits of these abstract models.
Dr. Fu bridges theoretical computer science with practical applications in networking and bioinformatics, with a particular focus on computational complexity theory. His work develops algorithmic foundations for analyzing biological data and designing efficient network protocols, drawing on deep expertise in combinatorics and complexity.
Dr. Kim leads the Machine Intelligence Lab at UTRGV, applying AI to bioinformatics challenges including biomarker discovery, biological network inference, and computational drug design. He also conducts research in reinforcement learning for motion control and has extended this work to applications in materials science, agriculture, and coastal flood forecasting.
Dr. Enriquez co-leads the Machine Intelligence Lab at UTRGV, focusing on reinforcement learning architectures and multi-agent coordination. His research investigates encoder representations in RL, graph neural networks for agent perception, and the deployment of intelligent agents in robotic systems.
Dr. Tang develops interpretable deep learning models and adversarial machine learning methods with applications to brain connectomics and bioinformatics. His work has appeared in KDD, IEEE TNNLS, Medical Image Analysis, and MICCAI.
Dr. Zhang focuses on developing robust, interpretable, and reliable data mining and machine learning tools for large-scale sensor time series data. Her work emphasizes high-resolution temporal datasets and applications in medical sensing, where accurate pattern recognition from continuous streams is critical.
Dr. Ayati develops algorithms for network analysis and systems biology, integrating diverse omics datasets to unravel the molecular underpinnings of complex diseases. Her computational approaches identify disease-driving network perturbations that are invisible to single-modality analyses.
Dr. Gu’s research mainly focuses on deep learning for biomedical image analysis, including image segmentation, classification, and detection; topology-driven image analysis; self-supervised learning; agentic AI; large language models; multimodal large models; foundation models in biomedical imaging; and multimodal data analysis. He has published in top-tier journals and conferences, including CVPR, ACM MM, ECCV, WACV, MICCAI, IEEE BIBM, IEEE ISBI, Medical Image Analysis, Bioinformatics Advances, and Theoretical Computer Science. His research has also been supported by the NSF, USDA, and AHA.
Dr. Lu leads the MARS (Multiple Autonomous Robot Systems) Lab, researching swarm robotics and autonomous multi-robot coordination. His work explores decentralized task allocation, foraging algorithms, and resilient swarm behavior, with support from NSF, FRA, DHS, and the U.S. Department of Education.
Professor Schweller's research centers on the design and analysis of algorithms for molecular programming systems, particularly the algorithmic self-assembly of DNA and chemical reaction networks. He also works on robot motion planning, exploring the computational principles underlying programmable matter and reconfigurable systems.
Dr. Chuprov focuses on the robustness and security of AI/ML systems in practical deployments, including federated learning environments and distributed edge networks. His work develops defense mechanisms against adversarial attacks and data degradation to ensure the reliability of AI models across various domains, particularly safety-critical systems such as intelligent transportation.
Dr. Wylie's work spans computational geometry, combinatorics, and algorithmic self-assembly, with a focus on complexity theory and tile assembly models. He also pursues research in high-dimensional data indexing and mining, bridging classical theoretical foundations with modern data-driven challenges.
Dr. Gao's research centers on self-supervised and representation learning for time series data, developing efficient methods for uncovering temporal patterns at scale. His work addresses motif discovery, anomaly detection, and similarity search in large datasets across domains including health monitoring and industrial sensing.
Dr. Yang develops AI-driven sensing and measurement systems, integrating machine vision with intelligent sensor design for precision metrology across scales. His work targets industrial and scientific applications requiring high-fidelity measurement and perception.
Dr. Dong's research advances control theory and autonomous systems, including mobile robots and unmanned aerial vehicles. His work addresses navigation, motion planning, and human-robot interaction for reliable autonomous operation in unstructured environments.
Dr. Li leads research at the intersection of advanced manufacturing and artificial intelligence, developing methods for additive manufacturing of high-performance materials and sustainable production systems. His work spans energy applications and next-generation fabrication processes.
Dr. Wu investigates human factors and cognitive effects in virtual and immersive environments, studying how embodiment in VR affects working memory, ergonomics, and user experience. His research contributes to the design of human-centered metaverse applications.
Dr. Ramoni's research focuses on metal and polymer additive manufacturing processes, including wire arc and fused filament fabrication, with an emphasis on material properties and process optimization. He also works on improving engineering education through hands-on fabrication experiences.
Dr. Tarawneh leads research in railway safety and transportation engineering, developing thermal analysis methods and diagnostic systems for bearing condition monitoring. His work on predictive maintenance contributes to safer and more reliable freight rail operations.
Dr. Zgheib applies computational fluid dynamics to problems in thermal sciences and environmental flows, including frost formation, heat transfer enhancement, and sediment transport in geophysical settings. His simulations provide insight into complex multi-physics phenomena at engineering and environmental scales.
Dr. Ali applies machine learning and data-driven methods to construction engineering and power infrastructure, including distributed solar generation and electric power market analysis. His work advances computational approaches to resilient and sustainable infrastructure systems.
Dr. Noruzoliaee develops computational methods for smart transportation and resilient infrastructure, combining machine learning with operations research to optimize traffic systems and autonomous vehicle integration. His work addresses safety, efficiency, and resilience in next-generation mobility networks.
Dr. Cheng's research addresses fluid flow and contaminant transport in porous media, with applications to managed aquifer recharge and environmental remediation. Her work develops computational approaches for climate-responsive water management under drought and flood scenarios.
Dr. Ai develops machine learning and acoustic emission methods for nondestructive evaluation and structural health monitoring of civil infrastructure. His work focuses on automated damage detection and characterization in composite and concrete structures, including alkali-silica reaction assessment.
Dr. Kim directs the SC² Lab, developing cyber-physical systems and digital twin technology for real-time monitoring and predictive analytics in construction. His research integrates computer vision and object detection for construction automation and worker safety in extreme heat conditions.
Dr. Mukherjee is a member of the LIGO-Virgo-KAGRA collaboration, contributing to gravitational wave detection and analysis. Her research spans signal processing methods for astrophysical transients, detector noise characterization, and searches for gravitational wave signals from core collapse supernovae.
Dr. Moqbel investigates the behavioral and organizational dimensions of technology adoption, including the effects of social media use and emerging AI tools. His research in health IT and information security addresses how individuals and organizations interact with and are affected by digital systems.
Dr. Roy develops drug delivery systems and therapeutic strategies for infectious diseases, with a focus on HIV treatment and vaccine development. His research integrates genomic and proteomic approaches to identify neurological disease biomarkers and advance translational medicine.
Dr. Castillo's research addresses security and privacy challenges in machine learning systems and IoT networks, developing defenses against adversarial attacks and solutions for secure and resilient federated learning. His work also spans blockchain-based security applications and semiconductor device characterization.
Dr.Chakrabarti’s research focuses on elucidating the molecular regulation of responses to abiotic and biotic stresses and key developmental processes in crop plants using genetic, physiological, molecular, genomic, bioinformatic, and biotechnological tools. Dr.Chakrabarti’s research also focuses on improving non-model crop plants.