Komatsuzaki Group

Sulimon Sattari

hteramoto Postdoctoral Research Fellow
  1. Molecule & Life Nonlinear Sciences Laboratory , Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science
Research Institute for Electronic Science, 5th floor, room 203
Mailing address:
Research Institute for Electronic Science, Hokkaido University, Kita 20 Nishi 10, Kita-ku, Sapporo 001-0020, Japan

Research Interests

The onset of supercomputing, availability of “big data”, and unprecedented resolution in experiments brings new challenges. On one hand, there is a new ability to generate unprecedented amounts of data in geophysical, molecular, nuclear, cosmological, biological, and social systems, to name a few. On the other hand, the ability to interpret such complex data has not caught up with the availability of large, complex, and often imperfect data sets. My research interests are at the meeting point between dynamical systems, information theory, data science, and supercomputing. The goal of my research is to use the lens of dynamical systems theory and information theory to extract relevant interpretations of complex data sets, whether the data came from supercomputer simulations or from empirical studies. Recently, I studied complex simulation data using a network representation known as symbolic dynamics (Sattari and Mitchell, CHAOS 2016). Some of my current work furthers this study by using different approaches to computing symbolic dynamics using CUDA, and analyzing community structures in the network. In another current project, I am applying information theory and dynamical systems techniques to study phase transitions in image data from biological cell colonies. The goal of this work is to study the onset of ”fingering” by leader cells, and to understand information passage between leader and follower cells, in hopes of better understanding biological phenomena such as cancer growth, wound healing, and organ formation.

Relevant publications



[1]Sulimon Sattari, Tamiki Komatsuzaki
Predicting Biological Cell Aggregation Using Scalable Random Forest Decision Trees
The 18th RIES-Hokudai International Symposium(11.30-12.1 ,2017), Chateraise Gateaux Kingdom Sapporo, Sapporo, Hokkaido 11, 30 (Poster)