I study small- and large-scale sustainable resource management using a variety of techniques including mathematical modeling, agent-based simulation, and Statistical Inference
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Evolutionary computation
I have developed several agent-based and cellular automata applications combining agent-based modelling, geographical information systems and visualisation to understand the complex mechanisms of decision making in land use change and environmental stewardship in order to analyse:
• the role of pastoral agriculture in regional development,
• the tradeoffs between land use intensification and water quality,
• the adoption of land-based climate change mitigation practices, and
• the incorporation of cultural values into spatial futures or scenario modelling.
Use of ABM in areas related to Systems Engineering and Automatic Control.
Dr. Mariam Kiran is a Research Scientist at LBNL, with roles at ESnet and Computational Research Division. Her current research focuses on deep reinforcement learning techniques and multi-agent applications to optimize control of system architectures such as HPC grids, high-speed networks and Cloud infrastructures.. Her work involves optimization of QoS, performance using parallelization algorithms and software engineering principles to solve complex data intensive problems such as large-scale complex decision-making. Over the years, she has been working with biologists, economists, social scientists, building tools and performing optimization of architectures for multiple problems in their domain.
Industrial Engineering, Multi-criteria Decision Making, Optimization Techniques, Global/International Facility Location, Agent-based Modeling
Analyzing economic dynamics through game theory and agent based evolutionary models. My research topics go from dynamics of organizations to industrial dynamics, macroeconomic dynamics and economic policy analysis.
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