Members

I am working on Theoretical ecology, Community ecology, Spatial scaling patterns of biodiversity, especially the changing of community biodiversity pattern under global changes (heterogeneous disturbance, habitat loss and fragmentation, climate change, biological invasion etc.). I am interested in exploring new idea and methods for the issues under the community ecology framework. It is also best to pursue my interest by embracing a theoretical and empirical compared way.

I am an environmental and resource economist specializing in fisheries resource management and fisheries economics. My research focuses on understanding how harvesters adapt their behavior in response to species range shifts driven by climate change. Specifically, I explore how fishers respond to evolving fishing opportunities, the implications of these responses for biological fish stocks, and the development of adaptive management strategies. To address these questions, I utilize game-theoretical models, socio-economic surveys, and observational data.

I hold a bachelor's degree in environmental science from Sun Yat-sen University in China, a master's degree in international environmental science from Lund University in Sweden and earned my PhD from the Norwegian School of Economics in 2012. Over the past decade, I have worked as a research fellow across several countries in Asia and Europe. After many years abroad, I am delighted to return to my "home region," the East China Sea region, to continue my fishery work.

I research the marine fishery system. I am interested in the integrative quantitative assessment of the fishery system. My work in OIST is to evaluate stakeholder satisfaction in fisheries through bio-socio-economic models. In the model, I will define utilities in the biological and socio-economic models for multiple stakeholder groups and evaluate different management options regarding the resulting stakeholder utilities. Before I came to OIST, my research focused on the socio-economic dimension of the fishery, such as the socio-economic status and occupational mobility of the fishers, text analysis of the fishery policy, and the well-being of the fishery communities. Now, I am trying to combine the biological and socio-economic dimensions in fishery research.

I graduated from the University of Tokyo with a bachelor’s degree in agriculture and　from Stockholm Resilience Centre, Stockholm University, with a master’s degree in biology. My specialty is sustainability science, particularly ocean governance, with a consideration of local communities’ values toward nature. Before joining OIST, I was a project assistant for SeaBOS (Seafood Business for Ocean Stewardship), a global initiative between scientists and the world’s largest seafood companies to transform the seafood industry into just and sustainable.

Since joining the CSE unit, I’ve been involved in two projects: the Okinawa fishers’ wellbeing project and the Bio-Socioeconomic Models project. My main tasks have been survey data analysis using factor analysis, structural equation modeling (SEM), and Bayesian network analysis. 

 I am interested in applying advanced machine learning and mathematical modeling to understand complex, data-driven systems. I hold dual bachelor's degrees in Economics and Applied Mathematics & Computer Science. My background combines computational social science research — using Natural Language Processing (NLP) for cybersecurity and hate speech analysis — with professional experience as a Data Scientist building Generative AI (GenAI, MLOps) and machine learning solutions. At OIST, I am continuing this focus on social systems by working on the 'Fear-Shame-Guilt' model. My research explores how social sentiments such as fear, shame, and guilt regulate selfish behavior and promote cooperation.

I began my journey in physics at Ferdowsi University of Mashhad, where I developed a strong interest in statistical mechanics and computational modeling. I continued my studies at Sharif University of Technology, focusing on data-driven tools and approaches to study complex systems, one our main tools being stochastic differential equations. I am now a Research Intern in the Complexity Science and Evolution Unit at OIST, working on linear Moran fitness waves, with a particular focus on understanding the coupled stochastic oscillations in skewness and variance in evolving trait distributions.

I obtained my Bachelor in Biology at the Ludwig-Maximilian University of Munich, where I did research on the population dynamics of invasive freshwater jellyfish. Pursuing this interest in aquatic systems and life, I graduated with a masters in Marine Biology at the University of Padova in Italy. There I first learned about and developed an interest in ecological modelling as a tool to guide sustainable policy decisions. Currently I am interning at OIST to explore that interest and am helping with the development of a R package that provides an integrative quantitative assessment of fishery systems while accounting for different shareholder objectives.

I am a research intern at OIST with a general fascination in how organizing principles give rise to rich, emergent phenomena in physical systems. I am drawn to both the interdisciplinary philosophy and the breadth of research opportunity in the complexity sciences, where tools uniquely developed within a specific discipline may become unexpectedly applicable and powerful in another. Currently, through agent-based modelling, I am investigating ways to promote social benefit through cooperation among players in what are called continuous public good games. I have a background in particle phenomenology at UC Santa Cruz, where I hold a bachelor’s in physics and computational mathematics.

I am currently a final-year student in the dual bachelor’s-master’s program at the Indian Institute of Science Education and Research Thiruvananthapuram, majoring in mathematics with a minor in biology. During my studies, I developed a strong interest in applying mathematical frameworks to biological questions, which piqued my interest in evolutionary epidemiology. Building on this interest, I am currently working at OIST, where I explore how evolutionary processes shape disease dynamics using mathematical modeling. Through this work, I aim to better understand how pathogens adapt and evolve under seasonal pressures.

Alex is a PhD candidate at the University of St Andrews, specialising in multisensory interactions and their behavioural benefits, employing EEG and computational modeling techniques. Currently, as a visiting research student at the CSE Unit, he is supporting research on enhancing the realism of game-theoretical models of corruption by considering the possibility of covert corruption.

I am a postgraduate researcher interested in understanding and modelling complex and multi-agent systems across a wide range of research domains. I graduated with a bachelor’s degree in Mechanical Engineering from Loughborough University, which included a year in industry working in Research and Development. Here I first developed my interest in complex systems, and in my final year of my bachelor’s I modelled drone swarms for use in reforestation. I then obtained a master’s degree in Biological Physics from the University of Manchester. My project involved using laser microscopy to track and simulate the anomalous transport of cells in living organisms.
I joined the Complexity Science and Evolution unit at OIST, applying similar methods in agent-based modelling to investigate the emergence of cooperation and institutions in human society within the context of evolutionary game theory. I moved back to undertake a PhD in Applied Mathematics at the University of Birmingham, where I investigate collective opinion dynamics with a focus on consensus, polarisation, and oscillations. I continue to collaborate with the CSE unit and I am currently at OIST as a visiting research student.

I am a researcher specializing in stochastic processes, with a focus on developing and analyzing probabilistic models in evolutionary dynamics. I enjoy exploring how mathematical tools can help us understand complex systems, from biological processes to computational algorithms.