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Research


While advancing robots towards full autonomy, it is important to minimize deleterious effects on human and infrastructure. To achieve this, I have been developing data-efficient robotic mapping techniques that capture uncertainty in dynamic environments. By modeling the nonlinear spatiotemporal relationships, these techniques can characterize the uncertainty in long-term and short-term patterns of occupancy, speed, and directions. Since these maps represent uncertainty, they can then be used for robust decision-making.
As part of the SAIL-Toyota Center For AI Research, I work on safe interactions of autonomous systems with Mykel Kochenderfer, Mac Schwager, and Marco Pavone.

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Uncertainty quantification for perception


Directional Uncertaintly

Temporal variations of spatial processes exhibit highly nonlinear patterns and modeling them is vital in many disciplines. For instance, robots operating in dynamic environments demand richer information for safer and robust path planning. In order to model these spatiotemporal phenomena, I develop and utilize theory in reproducing kernel Hilbert space (RKHS) and deep learning. I am mainly interested in modeling and propagating the uncertainty of dynamic environments and therefore I frequently use Bayesian modeling techniques.

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Uncertainty quantification for perception


Directional Uncertaintly

Temporal variations of spatial processes exhibit highly nonlinear patterns and modeling them is vital in many disciplines. For instance, robots operating in dynamic environments demand richer information for safer and robust path planning. In order to model these spatiotemporal phenomena, I develop and utilize theory in reproducing kernel Hilbert space (RKHS) and deep learning. I am mainly interested in modeling and propagating the uncertainty of dynamic environments and therefore I frequently use Bayesian modeling techniques.

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Uncertainty quantification for perception1


Directional Uncertaintly

Temporal variations of spatial processes exhibit highly nonlinear patterns and modeling them is vital in many disciplines. For instance, robots operating in dynamic environments demand richer information for safer and robust path planning. In order to model these spatiotemporal phenomena, I develop and utilize theory in reproducing kernel Hilbert space (RKHS) and deep learning. I am mainly interested in modeling and propagating the uncertainty of dynamic environments and therefore I frequently use Bayesian modeling techniques.

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Uncertainty quantification for perception part2


Directional Uncertaintly

Temporal variations of spatial processes exhibit highly nonlinear patterns and modeling them is vital in many disciplines. For instance, robots operating in dynamic environments demand richer information for safer and robust path planning. In order to model these spatiotemporal phenomena, I develop and utilize theory in reproducing kernel Hilbert space (RKHS) and deep learning. I am mainly interested in modeling and propagating the uncertainty of dynamic environments and therefore I frequently use Bayesian modeling techniques.

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Uncertainty quantification for perception


Directional Uncertaintly

Temporal variations of spatial processes exhibit highly nonlinear patterns and modeling them is vital in many disciplines. For instance, robots operating in dynamic environments demand richer information for safer and robust path planning. In order to model these spatiotemporal phenomena, I develop and utilize theory in reproducing kernel Hilbert space (RKHS) and deep learning. I am mainly interested in modeling and propagating the uncertainty of dynamic environments and therefore I frequently use Bayesian modeling techniques.

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Uncertainty quantification for perception 2


Directional Uncertaintly

Temporal variations of spatial processes exhibit highly nonlinear patterns and modeling them is vital in many disciplines. For instance, robots operating in dynamic environments demand richer information for safer and robust path planning. In order to model these spatiotemporal phenomena, I develop and utilize theory in reproducing kernel Hilbert space (RKHS) and deep learning. I am mainly interested in modeling and propagating the uncertainty of dynamic environments and therefore I frequently use Bayesian modeling techniques.

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