I’m interested in inter-individual interactions in general, demo-genetics and group behaviour. I’m currently working on locusts. Visit my website for more info.
I am interested in the interface between biology and computation. I am especially focused on modelling and simulation of evolutionary processes.
André Calero Valdez does research on Computational Communication Science investigating the influence of network structure and algorithms on communication flow using agent-based modeling.
Primate evolutionary biologist and geneticist at the University of Texas at Austin
I conduct long-term behavioral and ecological field research on several species in the primate community of Amazonian Ecuador to investigate the ways in which ecological conditions (such as the abundance and distribution of food resources) and the strategies of conspecifics together shape primate behavior and social relationships and ultimately determine the kinds of societies we see primates living in. This is a crucial and central focus in evolutionary anthropology, as understanding the ways in which behavior and social systems are shaped by environmental pressures is a fundamental part of the discipline.
I complement my field studies with molecular genetic laboratory work and agent-based simulation modeling in order to address issues that are typically difficult to explore through observational studies alone, including questions about dispersal behavior, gene flow, mating patterns, population structure, and the fitness consequences of individual behavior. In collaboration with colleagues, I have also started using molecular techniques to investigate a number of broader questions concerning the evolutionary history, social systems, and ecological roles of various New World primates.
My field of interests concerns two axes:
First, epistemology of computational modeling and simulation of complex systems. I am particularly interested in a sociological inquiry about social implication of knowledge derived from complex systems’ study.
Second, assessing the possibilities and limits of studying social complexity with complex systems tools, particularly, agent-based modeling and simulation.
My profound interest in networks convinced me to work in these subjects and start my master project on an application of social network analysis for detecting organized fraud in Automobile insurance, which helps to flag groups of fraudsters. The key point of this project is simply to find fraudulent rings, while the most of traditional methods have only taken opportunistic fraud into consideration. My duty in research is to design an algorithm for identifying cyclic components, then to be compared with theoretical ones. This project showed me how networks are used in the analysis of relations.
I live in Salento (Italy) the tiny land between two seas, where I work as a teacher in a school for adults. My education includes a degree in Life Sciences; in my post-graduate training, I have been involved in searching for the genetic and molecular responses of some cellular systems to environmental and genomic stresses. Now, one of my great interests is the approach to theoretical biology through agent-based modeling techniques, even if - I know - nothing can be more surprising than the complexity of Nature and the cognition about it.
Complex Adaptive Systems, Agent Based Simulation, Technology Enhanced Learning, and Theoretical Biology
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.