Mainly interested in studying social networks of learners, teachers, and innovators. Uses Social Network Analysis, but also sentiment analysis, data mining, and recommender system techniques.
Did some work in Multi-agent modeling for inventory routing problem using JADE. doing Research application Multi-agent modeling in Supply chain management with Internet of Things, Networked manufacturing
I am investigating the use of machine learning techniques in non-stationary modeling environments to better reproduce aspects of human learning and decision-making in human-natural system simulations.
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.
Complex Adaptive Systems, Agent Based Simulation, Technology Enhanced Learning, and Theoretical Biology
Northwest Coast Archaeology
Agent-based Modeling, Maching Learning, Algorithmic Marketing, Diffusion of Innovations, Online Communities
Sae Schatz, Ph.D., is an applied human–systems researcher, professional facilitator, and cognitive scientist. Her work focuses on human–systems integration (HSI), with an emphasis on human cognition and learning, instructional technologies, adaptive systems, human performance assessment, and modeling and simulation (M&S). Frequently, her work seeks to enhance individual’s higher-order cognitive skills (i.e., the mental, emotional, and relational skills associated with “cognitive readiness”).