マッテオ
Matteo Convertino
更新日: 2017/10/16
基本情報
- 所属
- 北海道大学 大学院情報科学研究科メディアネットワーク専攻
- University of Minnesota Twin-Cities Adjunct Faculty
- 学位
-
PhD(University of Padova, Italy)
- 研究者番号
- 10816143
- J-GLOBAL ID
- 201701001359045490
- researchmap会員ID
- B000282871
I am an Associate Professor at Hokkaido University, Graduate School of Information Science and Technology, Division of Media and Network Technologies, and the soon created Division of Frontier Science. I am the PI of the Complexity Group within the Information Communication Networks Lab. I also belong to the GI-CoRE Initiative in Big Data and Cybersecurity.
My area is complexity science broadly defined, with a particular focus on theoretical, computational and applied statistical physics/information theory, network science, and decision science. In the current time people would say I work in the area of ‘’data science’’ considering the broad applicability of my methods to a diverse set of fields where data (or better information) is pervasive: from biology, ecology, economics, engineering and health sciences. In a broader sense I am interested in collective phenomena and technology for the control of populations from which pattern emerge; phenomena of populations where a ''population'' is a collection of agents where information spreads over the domain of interest (you may think about news, diseases, or behaviors). It is really important to understand universal features of collective phenomena in order to understand how the underlying system works.
Considering my computational interests I am largely focused on model deign, analysis and visualization and I am relatively known for the theory of Optimal Information Networks, Information theoretic Fractal Analysis, integrated Portfolio Decision Modeling, and information-theoretic Global Uncertainty Evaluation.
More recently I have been working on topics related to pattern recognition (in images and texts), inverse modeling for source/shape detection, bio-inspired design of sensors (''Biomimetic Patterned Sensors''), Models on Sensors (MoS), AI and quantum information theory for making automated and more accurate forecasts of complex systems.
Till August 2017 I was Assistant Professor at the University of Minnesota, and before that I worked as a Research Scientist at the University of Florida and at a Department of Defense/USACE Risk and Decision Science Team in Boston. I graduated in 2010 from a joint University of Padova / Princeton University PhD program in Biocomplexity Engineering. My formal background is in Civil and Environmental Engineering Sciences.
My area is complexity science broadly defined, with a particular focus on theoretical, computational and applied statistical physics/information theory, network science, and decision science. In the current time people would say I work in the area of ‘’data science’’ considering the broad applicability of my methods to a diverse set of fields where data (or better information) is pervasive: from biology, ecology, economics, engineering and health sciences. In a broader sense I am interested in collective phenomena and technology for the control of populations from which pattern emerge; phenomena of populations where a ''population'' is a collection of agents where information spreads over the domain of interest (you may think about news, diseases, or behaviors). It is really important to understand universal features of collective phenomena in order to understand how the underlying system works.
Considering my computational interests I am largely focused on model deign, analysis and visualization and I am relatively known for the theory of Optimal Information Networks, Information theoretic Fractal Analysis, integrated Portfolio Decision Modeling, and information-theoretic Global Uncertainty Evaluation.
More recently I have been working on topics related to pattern recognition (in images and texts), inverse modeling for source/shape detection, bio-inspired design of sensors (''Biomimetic Patterned Sensors''), Models on Sensors (MoS), AI and quantum information theory for making automated and more accurate forecasts of complex systems.
Till August 2017 I was Assistant Professor at the University of Minnesota, and before that I worked as a Research Scientist at the University of Florida and at a Department of Defense/USACE Risk and Decision Science Team in Boston. I graduated in 2010 from a joint University of Padova / Princeton University PhD program in Biocomplexity Engineering. My formal background is in Civil and Environmental Engineering Sciences.