Evolutionary computation
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.
Mathematical modeling
agent-based modeling
coupling of agent-based models and mathematical models
machine learning algorithms
deep learning algorithms
Statistical inference
infectious diseases modeling
Topics:
Behavioural aspects of environmental problems: Use of evolutionary approaches to investigate how people react to environmental policy.
Resource scarcity
Climate-economic Models: Understand how economic agents think and decide about climate change and climate protection
Sustainable Development
Methods:
Agent-Based-Modeling
Genetic algorithms
Evolutionary economics
Behavioural economics
Ecological economics
Complexity Theory
Modeling of Social Phenomena, Graph Algorithms, Opinion and Information Dynamics
Use of ABM in areas related to Systems Engineering and Automatic Control.
Game theory, artificial intelligence, agent-based models, genetic algorithms.
Industrial Engineering, Multi-criteria Decision Making, Optimization Techniques, Global/International Facility Location, Agent-based Modeling
Main Research Topics :
1) Agent-based Modeling (Communication between agents)
2) Economic and Econometric Algorithms and Software Development
3) Optimal International Trade Configuration