We explore the universal properties underlying large-scale social systems through mathematical models derived by computing with big data obtained from large-scale resources. Using these models, we explore new ways of engineering to aid human social activities.
Month: April 2018
seisanken – omi
Takahiro Omi（近江 崇宏）
Associate ProfessorInstitute of Industrial Science, The University of Tokyo Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
Aihara lab., Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
Tel: +81-3-5452-6697 (Ext. 56697)
|March 2007||Bachelor of Science from Faculty of Science, Kyoto University|
|March 2009||Master of Science from Department of Physics, Graduate School of Science, Kyoto University|
|March 2012||Ph.D. in Science from Department of Physics, Graduate School of Science, Kyoto University|
|April 2012||Researcher, Japan Science and Technology|
|April 2013||Japan Society for the Promotion of Science Fellowship for Young Scientists|
|April 2016||Project Research Associate, Institute of Industrial Science, The University of Tokyo|
|April 2018||Project Associate Professor, Institute of Industrial Science, The University of Tokyo|
Our main topics is time-series analysis. We especially focus on the statistical analysis of point process data, which describe events that occur irregularly in time. Our research includes (1) the development of estimation and forecast method based on Bayesian statistics and (2) its application to earthquake, economic, and social data.
- T. Omi, Y. Hirata, and K. Aihara, “Hawkes process model with a time-dependent background rate and its application to high-frequency financial data”, Physical Review E 96, 012303 (2017).
- T. Omi, Y. Ogata, Y. Hirata, and K. Aihara, “Forecasting large aftershocks within one day after the main shock”, Scientific Reports 3, 2218 (2013).
- T. Omi and S. Shinomoto, “Optimizing time histograms for non-Poissonian spike trains”, Neural Computation 23, 3125 (2011).