Difference between revisions of "Sean G. Carver's Research Interests"

Revision as of 21:08, 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 dynamical models of a system. Typically, SysID involves parameter estimation, model selection, and model validation.

System Identification of Cooperative Control

When people cooperate to perform a task that requires that they stay in sync (e.g. salsa dancing, or chamber music), how do they maintain synchrony given that their biological clocks are noisy? I am currently involved in designing experiments (in collaboration with the LIMBS lab, Johns Hopkins) to collect and analyze data from humans performing simple sensorimotor tasks where they must cooperate to stay in sync. We plan to analyze the data with SysID. Please see a preprint of a relevant paper (click here).

Control Theory Where Agent(s) Rely on Noisy Clocks

No clock tells time perfectly [1]. In the past, when timekeeping was not good enough for a particular engineering application, the engineers invariably solved the problem by building better clocks. In solving the problem this way, they avoided needing to create control algorithms to deal with noisy clocks. Invariably, they managed to get away with assuming perfection in timekeeping. Unfortunately, evolution has different constraints, and it is apparent that to understand the sensorimotor control of behaviors such as dance, we need to extend control theory to deal with the case that internal clocks are noisy. To date, surprisingly little has been done in this regard, but the models I pose for the SysID of cooperative synchrony do involve noisy internal clocks.

Pedagogy for Training Students on System Identification

I am interested in finding effective methods for training students in the art of system identification. Training students to use SysID is easier than might first be thought, because one can collect the needed experimental data from simulations. For training and testing purposes, using a simulated system to generate the data is desired, because the experimenter then knows the correct model ahead of time. Knowing the correct model allows an assessment of the suitability and effectiveness of the SysID methods. Moreover, an instructor can create a dataset from simulation, but withhold from students the structure and parameterization of the generating model, giving students the more realistic experience of not already knowing the answer. In my mind, the primary advantage of this feature comes from the fact that no large investment in a laboratory equipment and training is needed to train students in an advanced laboratory technique.