Dr. Hackl’s research focuses on complex infrastructure systems, intelligent risk and resilience assessments to climate change, as well as integrated solutions to future challenges facing our cities and society. His research interests lie at the interface between formal methods in network sciences and their integration with prevailing simulation methods, such as digital twins. He is particularly interested in developing scalable data analytics and machine learning techniques for spatial-temporal networks applied to dynamic processes in complex multiscale civil engineered systems to open and interconnect new perspectives for, e.g., modeling of usage, behavior, and performance; analysis of system integration; as well as detection of systemic risks in socio-technical systems. Another aspect of his work covers integrating these data-driven approaches with physics-based models to create digital twins that can learn from and update based on multiple data sources, as well as represent and predict the current and future conditions of their physical counterparts.

Work on solid ion conductors for advanced energy storage and conversion applications. We are interested in all solid state devices for electrochemical fuel production as well as energy storage systems for electric vehicles. In addition we study low-cost thermal energy storage systems for concentrated solar power integration and production.

Research focuses on the political economy and energy policies of oil producing states in the Persian Gulf.

Seeks to understand the biosphere’s dynamics by developing a new family of theories, and quantitative analyses

Computational imaging at the intersection of computer vision, AI, computer graphics, optics and sensing. Computational optics and holographic displays. Optical computing and computational photonics. Generative AI and neural rendering. Vision and sensing for autonomous robots.

Wind turbine aerodynamics, turbulent drag reduction, turbulent heat transfer, atmospheric fluid mechanics, and instrumentation

Biogeochemical cycles applied to environmental remediation, especially water quality and soils. Bioremediation of uranium-contaminated groundwater; biogeochemical cycling in wetlands including immobilization of toxic metals; methane emissions from rice paddies; nitrogen transformation in urban streams and hydrological infrastructure; linking water quality models with climate change models; bioremediation of recalcitrant pollutants, including PFAS; energy efficient methods for nitrification and denitrification.

Jesse’s research focuses on improving and applying optimization-based energy systems models to evaluate and optimize low-carbon energy technologies, guide investment and research in innovative energy technologies, and generate insights to improve energy and climate policy and planning decisions.

Sensors to reduce electricity and heating costs in buildings; power/thermal analysis and optimization of integrated circuits and systems

Development of molecular simulation approaches for applications in synthetic biology and metabolic engineering; Sustainable chemical synthesis; Thermoresponsive biomaterials; Biosensor engineering.