Tom Bertalan

I am a Postdoctoral Fellow in the Chemical and Biomolecular Engineering department at Johns Hopkins University. 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.


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.


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.