Joel Nishimura

Joel Nishimura is an Associate Professor at Arizona State University's School of Mathematical and Natural Sciences. His research spans several interconnected fields including network science, dynamical systems, and mathematical biology. He focuses on how simple rules can create complex behaviors and structures, with applications in biological systems, network algorithms, and engineered protocols.

Dr. Nishimura received his Ph.D. from Cornell University in 2013. His dissertation explored decentralized coordination and how actors with limited information can coordinate their timing. His research interests include Network Science, Applied Dynamical Systems, Mathematical Biology, Mathematical Modeling, and Game Theory. A unifying feature of his work is examining how simple rules can create complex behaviors and structures. In biological settings, these simple models capture key qualitative features of observed behaviors, suggesting generalizable principles. In network settings, simple rules form the basis for predictive algorithms, while in engineered settings, they serve as backbones for robust decentralized protocols.

Analysis of Dr. Nishimura's recent publications reveals a strong focus on network science and its applications across multiple domains. His work spans theoretical foundations of graph theory and network models, applications to social and biological systems, and connections to decision-making processes and game theory. A notable trend is his consistent exploration of how simple rules or interactions can produce complex emergent phenomena, whether in network structures, biological behaviors, or decision-making processes.

Dr. Nishimura has supervised several undergraduate and graduate students, as indicated by the asterisks and other symbols in his publication record. His teaching includes courses such as Applied Linear Algebra (MAT 343), Individualized Instruction (MAT 499), Honors Directed Study (MAT 492), and Honors Thesis (MAT 493). His research has been supported by various grants, though specific details are not provided in the available information. His GitHub repository for the "Double Edge Swap MCMC Graph Sampler" suggests involvement in computational tools development for network science research.