# Tom Bertalan

I am a Postdoctoral Associate in the Mechanical Engineering department at the Massachusetts Institute of Technology. I received my PhD from Princeton University's department of Chemical and Biological Engineering, with a Graduate Certificate in Computational and Information Science.

My research interests are in data mining, dimensionality reduction, and system identification for high-dimensional dynamical systems, with applications in robotic perception and planning, and computational neuroscience.

## Gudrun

Build an Ackermann robot with RGBD as its primary sense.

## Boston AV Group Robocar

Teach a one-week workshop to high school students on building and programming a small autonomous car.

## Equal Space

Use nonlinear manifold learning to discover automatically both the true dimensionality and the underlying spatial coordinates that define a high-dimensional simulation trajectory.

## Gunnar

Build a differential-drive robot with LIDAR as its primary sense.

## Circadian Rhythms

Simulate circadian rhythms in the suprachiasmatic nucleus of the hypothalamus.

## ANOVA and PCE for Biological Neural Networks

Use ANOVA to perform integrals for polynomial chaos expansions.

## Debriefing Neural Networks for Nonlinear Dynamics

Use neural networks as models for process dynamics, and manifold learning to understand and simplify the fitted networks.

## Next Task Decider

Process task list and decide what I should do next.