Seth Guikema is a Professor of Industrial & Operations Engineering and Civil and Environmental Engineering. Prof. Guikema’s research is focused on and grounded in risk analysis, statistical learning theory, Bayesian probability, stochastic simulation, decision analysis, and agent-based modeling. The issues studied are related to climate adaptation and the sustainability of cities and infrastructure, disaster risk analysis, critical infrastructure modeling, natural hazards, and terrorism risk. Current projects include large-scale agent-based simulation models of evolution of regions in response to repeated climate-related events under different policy scenarios, data-driven evaluation of urban renewal and sustainability, and data-driven predictive modeling of the impacts of storms on power systems.
Cong Shi is an Associate Professor in the Department of Industrial and Operations Engineering.
Professor Shi’s current research is focused on the design and performance analysis of efficient algorithms for stochastic optimization models, arising in the context of inventory and supply chain management, revenue management, as well as logistics. These stochastic optimization problems involve sequential decision-making under highly evolving or poorly understood environments, which are typically hard to solve to optimality. He constructs efficient heuristics that admit worst-case or average-case performance guarantees, and in doing so he develops novel analytical and computational techniques that are applicable to a broad class of models.
Ruiwei Jiang is an Assistant Professor in the department of Industrial & Operations Engineering. Prof. Jiang’s research focuses on stochastic optimization and integer programming. He aims to develop data-enabled stochastic optimization (DESO) models and solution methodology that bring together data analytics, integer programming, stochastic programming, and robust optimization. Together with his collaborators, Prof. Jiang applies DESO approaches to various engineering problems, including power and water system operations, renewable energy integration, and healthcare resource scheduling.
Jeff Fessler is a Professor in the Department of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division. His research interests include numerical optimization, inverse problems, image reconstruction, computational imaging, tomography, magnetic resonance imaging. Most of these applications involve large problem sizes and parallel computing methods (cluster, cloud, GPU, SIMD, etc.) are needed.
His research group investigates simulation-based and data-driven computational synthesis of for mechanical, industrial and biomedical systems. The target systems are modeled by utilizing tools and algorithms in computational mechanics, geometric reasoning, image recognition, statistical data processing, and optimized by numerical optimization algorithms. Recent application domains includes lightweight automotive structures, intelligent transportation systems, water desalination systems, energy-efficient production systems, biomedical deformable image registration, and statistical protein energy potentials.
Prof. Shen’s research derives multifaceted mathematical optimization models for decision making under data uncertainty and information ambiguity. The models she considers often feature stochastic parameters and discrete (0-1) decision variables. The goal is to seek optimal solutions for balancing risk and cost objectives associated with complex systems. She also develops efficient algorithms for solving the large-scale optimization models, based on integer programming, stochastic and data-driven approaches, and special network topologies. In particular, her research has been applied to cyberinfrastructure design and operations management problems related to power grids, transportation, and Cloud Computing systems.
Professor Lee’s research focus is on nonlinear discrete optimization (NDO). Many practical engineering problems have physical aspects which are naturally modeled through smooth nonlinear functions, as well as design aspects which are often modeled with discrete variables. Research in NDO seeks to marry diverse techniques from classical areas of optimization, for example methods for smooth nonlinear optimization and methods for integer linear programming, with the idea of successfully attacking natural NDO models for practical engineering problems.
Dr. Lee is interested in designing and developing mechanisms, models, and systems that support diverse decision-making processes to manage the dynamics in large-scale civil infrastructure development and maintenance. His current research interest, in particular, is distributed, interoperable, and multi-paradigm simulation and its integration with sensors. Application examples include human-centered construction operations analysis, post-disaster planning and management, and building energy simulation.
Martins’ research is on algorithms for multidisciplinary design optimization (MDO) that can take advantage of high-fidelity simulations and high-performance parallel computing. He has been focusing on applying these algorithms to the design optimization of new aircraft configurations. In the design of an aircraft wing in particular, since it is flexible, it is crucial to consider the coupling of the aerodynamics and the structure. In addition, when performing design optimization, it is important to simultaneously account for the aerodynamic performance and the structural failure constraints. In the MDO Lab, Martins’ team has developed ways to perform design optimization based a Reynolds-averaged Navier-Stokes model for the aerodynamics that is tightly coupled to a detailed structural finite-element model. The optimization of the coupled system is done with a gradient-based algorithm, where the gradients of the coupled system are computed using a two-field adjoint system of equations. This enables the high-fidelity aerostructural design of aircraft configurations with respect to thousands of design variables.
In order to understand the relationship between molecular structure and dynamics and biological function, the Frank research group seeks to develop and deploy integrative modeling tools to elucidate the structure and dynamics of biologically relevant molecules. Our methods will utilize readily accessible experimental observables from a variety of sources to first guide structure prediction efforts and then guide atomistic simulations to map the entire conformational landscape of these molecules. We are primarily interested in using our methods to understand how functional ribonucleic acids, either by themselves or in concert with other molecules, achieve specific cellular functions. Our research makes heavy use of advanced machine learning and optimization techniques.