Difference between revisions of "Sean G. Carver's Research Interests"
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== Control Theory Where Agent(s) Rely on Noisy Clocks == | == Control Theory Where Agent(s) Rely on Noisy Clocks == | ||
| − | == Pedagogy for System Identification == | + | == Pedagogy for Training Students on System Identification == |
| − | I am interested in finding effective methods for training students in the art of system identification. | + | I am interested in finding effective methods for training students in the art of system identification. SysID has one huge advantage over other "laboratory" techniques: one can collect experimental data for SysID from ''simulations''. For training and testing purposes this is desired the experimenter knows the correct answer ahead of time allowing an assessment of the suitability and effectiveness of the methods. The instructor can give the students dataset What this means, however, is that students can design and execute many interesting computer based experiments without a large investment in laboratory techniques and training. |
Revision as of 19:31, 25 May 2013
Broadly, my interests involve system identification (SysID) applied to biological systems, especially:
- developing methods of SyID,
- applying methods of SysID to understand biological processes, and
- advancing methods for teaching SysID (pedagogy).
By system identification, I mean using statistics, derived from experimental data, to constrain models of the system.
System Identification of Cooperative Control
Control Theory Where Agent(s) Rely on Noisy Clocks
Pedagogy for Training Students on System Identification
I am interested in finding effective methods for training students in the art of system identification. SysID has one huge advantage over other "laboratory" techniques: one can collect experimental data for SysID from simulations. For training and testing purposes this is desired the experimenter knows the correct answer ahead of time allowing an assessment of the suitability and effectiveness of the methods. The instructor can give the students dataset What this means, however, is that students can design and execute many interesting computer based experiments without a large investment in laboratory techniques and training.
