Portrait of Zhang, Yang

Yang Zhang

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Our research can be summarized in two words: Matter and Machine. On the Matter side, Z lab studies far-from-equilibrium physics. They synergistically combine and push the boundaries of statistical and stochastic thermodynamic theories, accelerated molecular simulations, understandable AI/ML/DS methods, and neutron scattering experiments, with the goal of significantly extending our understanding of a wide range of long timescale phenomena and rare events. Particular emphasis is given to the physics and chemistry of liquids and complex fluids, especially at interfaces, driven away from equilibrium, or under extreme conditions. On the Machine side, leveraging their expertise in materials and modeling, his group advances the development of soft robots and human-compatible machines, swarm robots and collective intelligence, and robots in extreme environments, which can lead to immediate societal impact.

Seth Guikema

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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

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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.