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hcmi:lanny-lin [2015/03/19 22:44]
ryancha
hcmi:lanny-lin [2015/03/19 22:45] (current)
ryancha
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-[[media:​hcmi:​Lanny.jpg|The real Lanny, not a robot.]+[{{media:​hcmi:​Lanny.jpg|The real Lanny, not a robot.}}
 I am a PhD student in the [http://​cs.byu.edu Computer Science Department] at BYU. I received my undergraduate degrees from [http://​www.sou.edu/​ Southern Oregon University] in 1997. After working in the industry for 9 years, I decided to return to school to pursue something that I've always wanted to do, something I could enjoy for the rest of my life --- Artificial Intelligence and Robotics --- hence, here I am. I am a PhD student in the [http://​cs.byu.edu Computer Science Department] at BYU. I received my undergraduate degrees from [http://​www.sou.edu/​ Southern Oregon University] in 1997. After working in the industry for 9 years, I decided to return to school to pursue something that I've always wanted to do, something I could enjoy for the rest of my life --- Artificial Intelligence and Robotics --- hence, here I am.
  
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 == Research Projects == == Research Projects ==
  
-[[media:​hcmi:​400px-Priorvspost.jpg|'''​Upper:'''​ Prior predictive probability distribution suggested by the Bayesian model. '''​Lower:'''​ Posterior predictive probability distribution.]+[{{media:​hcmi:​400px-Priorvspost.jpg|'''​Upper:'''​ Prior predictive probability distribution suggested by the Bayesian model. '''​Lower:'''​ Posterior predictive probability distribution.}}
-[[media:​hcmi:​400px-maps.jpg|'''​Left:'''​ A sample multi-modal probability distribution of the likely places to find the missing person with desired starting point marked. '''​Right:'''​ Search path produced by the intelligent path planning algorithms.]+[{{media:​hcmi:​400px-maps.jpg|'''​Left:'''​ A sample multi-modal probability distribution of the likely places to find the missing person with desired starting point marked. '''​Right:'''​ Search path produced by the intelligent path planning algorithms.}}
-[[media:​hcmi:​300px-SlidingAutonomyE.PNG|A mock up UAV search path showing human planning strategically (by specifying way points) and intelligent algorithms planning tactically (maximize amount of probability collected given a probability distribution map).]]+[{{media:​hcmi:​300px-SlidingAutonomyE.PNG|A mock up UAV search path showing human planning strategically (by specifying way points) and intelligent algorithms planning tactically (maximize amount of probability collected given a probability distribution map).}}]
  
 * '''​Modeling Terrain-based Lost Person Behavior'''​ -- Using publicly available terrain data, we built a Bayesian model to predict how terrain features (such as topography, vegetation density and elevation difference) might affect a lost person'​s behavior. This model allows the searcher to incorporate his/her uncertainly in the prior estimates of lost person'​s transitional probabilities between two terrain features. It also allows the searcher to incorporate past human behavior data in the form of GPS track logs to generate posterior predictive probability distribution. We are presently working on extending this model to include intended destination and trail following factors and then use geocacher GPS track logs as observed human behavior data in our model. We are also developing user interfaces that allow the searcher to easily view and modify transitional probabilities in the form of Beta Distributions. * '''​Modeling Terrain-based Lost Person Behavior'''​ -- Using publicly available terrain data, we built a Bayesian model to predict how terrain features (such as topography, vegetation density and elevation difference) might affect a lost person'​s behavior. This model allows the searcher to incorporate his/her uncertainly in the prior estimates of lost person'​s transitional probabilities between two terrain features. It also allows the searcher to incorporate past human behavior data in the form of GPS track logs to generate posterior predictive probability distribution. We are presently working on extending this model to include intended destination and trail following factors and then use geocacher GPS track logs as observed human behavior data in our model. We are also developing user interfaces that allow the searcher to easily view and modify transitional probabilities in the form of Beta Distributions.
hcmi/lanny-lin.txt · Last modified: 2015/03/19 22:45 by ryancha
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