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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 work as a Senior Researcher at the Centre for Modeling Social Systems (CMSS) at the Norwegian Research Centre (NORCE) sinde 2023. Before, I worked as an Expert Research Engineer at the CEA LIST Institute, Paris-Saclay University in France from 2013 to 2023. I hold a PhD in Artificial Intelligence degree from the Paul Sabatier University (France) and a PhD in Computer Engineering degree from the Ege University (Turkey).
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.
I am a computational archaeologist with a strong background in humanities and social sciences, specialising in simulating socioecological systems from the past.
My main concern has been to tackle meaningful theoretical questions about human behaviour and social institutions and their role in the biosphere, as documented by history and archaeology. My research focuses specifically on how social behaviour reflects long-term historical processes, especially those concerning food systems in past small-scale societies. Among the aspects investigated are competition for land use between sedentary farmers and mobile herders (Angourakis et al. 2014; 2017), cooperation for food storage (Angourakis et al. 2015), origins of agriculture and domestication of plants (Angourakis et al. 2022), the sustainability of subsistence strategies and resilience to climate change (Angourakis et al. 2020, 2022). He has also been actively involved in advancing data science applications in archaeology, such as multivariate statistics on archaeometric data (Angourakis et al. 2018) and the use of computer vision and machine learning to photographs of human remains (Graham et al. 2020).
As a side, but not less important interest, I had the opportunity to learn about video game development and engage with professionals in Creative Industries. In one collaborative initiative, I was able to combine my know-how in both video games and simulation models (\href{https://doi.org/10.1007/978-3-030-92843-8_15}{Szczepanska et al. 2022}).
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
Lu Ping is a dedicated researcher in interdisciplinary fields including artificial intelligence (AI), digital economy, technological innovation, and industrial economics. Currently serving as an Associate Research Fellow at the China Academy of Information and Communications Technology (CAICT), Lu Ping focuses on examining the impacts of digital technologies (e.g., AI, big data, and IoT) on economic growth, industrial ecosystems, policy formulation, and societal ethics through multidimensional data modeling and empirical research.
Representative Academic Contributions:
1. AI Development and Societal Implications
A Brief History of Artificial Intelligence Development in China (2017): Explored the technological evolution and policy-driven pathways of China’s AI industry.
Ethical Dilemmas Faced by AI Algorithms (2018): Analyzed ethical challenges such as algorithmic bias and data privacy, proposing governance frameworks.
A Brief History of the Evolution of Smart Hardware in China (2018): Systematically reviewed the technological iterations and market dynamics of China’s smart hardware sector.
2.Technological Innovation and Industrial Economics
An Empirical Analysis of Technological Innovation Driving Growth in Internet Companies: Evidence from A-Share Listed Internet Firms in Shanghai and Shenzhen (2019).
Research on Competitiveness Measurement of Frontier Emerging Industries Based on Data Envelopment Analysis (DEA) Models (2019).
3.Digital Economy and Market Behavior
Correlation Analysis of Crowdfunding Behavior and Funding Performance for Internet Products: A Bayesian Approach Based on JD.com Crowdfunding Data (2018): Uncovered nonlinear relationships between user participation and project success rates using crowdfunding platform data.
Analyzing the Effects of Developer and User Behavior on Mobile App Downloads (2019): Built predictive models for app market performance based on user behavior data.
4.Policy Simulation
General Equilibrium Analysis of Beijing’s Water Supply and Consumption Policies: A Computable General Equilibrium (CGE) Model-Based Approach (2015).
Impact Analysis of EU Food Safety Standards on China’s Food Industry: A Dynamic Global Trade Analysis Project (GTAP) Model-Based Study (2015).
Academic Contributions:
Pioneered interdisciplinary paradigms in industrial economics research by integrating perspectives from econometrics, data science, and sociology. Published high-impact research in AI ethics, digital economy policies, and resource-environmental economics, providing decision-making references for academia and policymakers.
My research focuses on the interdisciplinary nexus of artificial intelligence (AI), digital economy, technological innovation, and industrial economics, with an emphasis on understanding how digital technologies reshape economic structures, policy frameworks, and societal norms. Key areas of interest include:
GIS enthusiast and ABM practitioner
Urban Mobility
Machine Learning
Social Network Analysis
Crime Simulation
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.
PhD student in economics
Machine Learning, Artifical Inteligence, Multi Agent Simulations
Furkan Gürsoy received the BS in Management Information Systems from Boğaziçi University, Turkey, and the MS in Data Science from İstanbul Şehir University, Turkey. He is currently a PhD Candidate at Boğaziçi University. He previously worked as an IS/IT Consultant and a Machine Learning Engineer with the industry for several years. He held a Visiting Researcher Position with IMT Atlantique, France, in 2020. His research interests include complex networks, machine learning, simulation, and broad data science.
network science, machine learning, simulation, data science.
Displaying 10 of 25 results machine learning clear search