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Professor-Researcher and Consultant in Knowledge Management, Strategy, and Innovation. He received his Ph.D. in Business Management and Organisation from Universidad Autónoma de Madrid (Spain) and a Postdoctoral in Administration from Universidad de São Paulo (Brazil).
Knowledge Management, Organisational Learning, Entrepreneurship, and Innovation Management.
Moscow City University, Professor: Institute of Digital Education - http://digida.mgpu.ru
National Research University Higher School of Economics, Professor: Institute of Education / Department of Educational Programmes. Leading Expert: Institute of Education / Laboratory for Digital Transformation of Education - 2019 – present
2016 – present Leading Researcher at Moscow City University, Educational policies & educational practices
2018 – 2020 World Bank, Consultant. Children Learning to Code: Essential for 21st Century Human Capital
2011 - 2019 - Co-founder, chief community officer at WikiVote!
Educational network - Letopisi.org 2006 – present, Co-founder, chief community officer
Scientific project “Mobile and ubi-learning”, 2009 - 2011
ABM, wiki, NetLogo, StarLogo Nova, R, Collaboration
PhD student in economics
Peter Gerbrands is a Post-Doctoral Researcher at the of Utrecht University School of Economics, where is develops the data infrastructure for FIRMBACKBONE. He teaches data science courses: “Applied Data Analysis and Visualization” and “Introduction to R”. His research interests are agent-based simulations, social network analysis, complex systems, big data analysis, statistical learning, and computational social science. He applies his skills primarily for policy analysis, especially related to illicit financial flows, i.e. tax evasion, tax avoidance and money laundering and has published in Regulation & Governance, and EPJ Data Science. Prior to becoming an academic, Peter had a long career in IT consulting. In Fall 2023, he is a Visiting Research Scholar at SUNY Binghamton in NY.
agent-based simulations
social network analysis
complex systems
big data analysis
statistical learning
computational social science
RN [Mental Health & General], Community Mental Health Nurse (Cert.)
PG Cert. Ed
BA(Joint Hons.) Computing and Philosophy
PG(Dip.) Collaboration on Psychosocial Education [COPE]
MRES. e-Research and Technology Enhanced Learning
Nursing, Integrated, Person-Centred, Holistic (mental - physical) care.
Study and champion - “Hodges’ Health Career - Care Domains - Model” a generic conceptual framework for health and education.
‘Health career’ refers to ‘life chances’.
The care domains relate to academic subjects - knowledge and are:
SCIENCES
INTRA- INTERPERSONAL
SOCIOLOGY
POLITICAL
The blog below includes a bibliography and template link in the sidebar.
https://hodges-model.blogspot.com/
A new website remains an aspiration - using Drupal, Pharo..?
Developing ideas on Hodges’ model (not Wilfred btw) when viewed as a mathematical object, using category theory as a ‘non-mathematician’.
Work part-time still in the community in NW England.
Twitter - ‘X’ @h2cm
The main research area is operation research in logistics with a focus on logistic cluster development and innovative technology usage. Due to mathematical background, Gružauskas focuses on quantitative analysis by conducting simulations, stochastic and dynamic models and other analytical approaches to amplify the developed theories. Gružauskas also is working as a freelance data analyst with a focus on statistical analysis, web scraping and machine learning.
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”).
Research focuses on the coupled dynamics of human and natural systems, specifically in the context of forest dynamics. I utilize a variety of modeling and analysis techniques, including agent-based modeling, cellular automata, machine learning and various spatial statistics and GIS-related methods. I am currently involved in projects that investigate the anthropogenic and biological drivers behind native and invasive forest pathogens and insects.
I am a Ph.D. candidate in Computational Social Science (CSS) program at George Mason (GMU). I hold a MAIS from GMU and a Bachelor of Economics from the University of Tasmania. My research interests are the application of ABMs, network analysis, and machine learning to financial markets. My email address and website is [email protected] and www.aussiecas.com
I am interested in using agent-based model to understand the behavior of financial markets
I work in the field of complex adaptive systems, specializing in multi-agent systems, simulation, machine learning, collective intelligence, self-organization, and self-adaptation. I am interested in contributing to innovative projects and research in these domains.
My experience spans across multiple large-scale international research projects in areas such as green urban logistics, blockchain for nuclear applications, autonomous robotics systems and simulation of biological neural networks.
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