I am excited to continue to develop 3D perception systems at Oculus Research. In spring 2017, I defended my PhD thesis on Nonparametric Directional Perception. My advisers at MIT within the CS and AI Laboratory (CSAIL) were John W. Fisher III and John Leonard. On my way to MIT, I graduated from the Technische Universit√§t M√ľnchen (TUM) with a Diplom and the Georgia Institute of Technology with a M.Sc.

My research interests in AI and robotics are in 3D perception [1, 3, 4, 8, 9, 10], modeling (directional data [1, 2] and Bayesian nonparametrics [5, 7]), and inference (sampling [1, 2, 4], optimization [6, 7] and low-variance asymptotics [3]).

MMF

MMF

The Mixture of Manhattan Frames (MMF) is a versatile model to capture man-made environments.

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DPvMF-means

DPvMF-means

Nonparametric clustering algorithms for batch and streaming directional data. GPU-enabled fast inference.

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DP-TGMM

DP-TGMM

Dirichlet process tangent space Gaussian mixture for Bayesian nonparametric inference on directional data.

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My interest in robotics and 3D perception originates from age 15, when I got my first micro-processor from my father as a present. Since then I have built seven robots (Plexa, Plicro, Roboking2005, Ca3505, Kno0Bot, Kno2Bot, Holomove) from scratch and worked in multiple teams on robotics related projects (KUKAyouBot, rEIzor).

Kno0Bot

Kno0Bot

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Plicro

Plicro

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Holonmove

Holomove

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