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. Before working at JHU, I was a Postdoctoral Associate in the MAE department at MIT.

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

Hamiltonian Neural Networks

Learn dynamics with constrained quantities.


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.

Parsing Dates from Emails

Use GPT3 to get a calendar event from an email.

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.

Cat Wrangler

A feline surveillance bot using the guts of an iRobot Braava.

Circadian Rhythms

Simulate circadian rhythms in the suprachiasmatic nucleus of the hypothalamus.

Representation Learning

Unsupervised learning methods to transform data into a form that's somehow more useful.

ANOVA and PCE for Biological Neural Networks

Use ANOVA to perform integrals for polynomial chaos expansions.

Next Task Decider

Process task list and decide what I should do next.

Project Opener Menu

A little TK menu for quickly getting to my project directories.

Hierarchy Formation

Simulate the formation of dominance hierarchies through social combat.