Tom Bertalan

I am a Research Software Engineer in the Chemical and Biomolecular Engineering department at The University of Massachusetts at Lowell. 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 UML, I was a postdoc at MIT and JHU.

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.

Visual Scene Grammars

Foundation models can analyze and generate both images and (noisy) grammars. Let's use that to do some visual scene understanding.


Build an Ackermann robot with RGBD as its primary sense.

Robotics Simulators

Mostly about using Farm Simulator, GTA V, and other games for training both perception and control systems.

Scan for SyncThing Conflicts

Cross-platform Python app to quickly compare conflict files created by SyncThing

Equal Space

Nature Comm. 2022. Use nonlinear manifold learning to discover automatically both the true dimensionality and the underlying spatial coordinates that define a high-dimensional simulation trajectory.

Local Neural Text-to-Speech App

A one-pyfile PyTorch+tk GUI for local neural text-to-speech synthesis.

Faster RNN warmup via manifold learning

Use diffusion maps to skip the warmup phase of RNN inference; demonstrated witha chemical model system.

Learning ODEs from Patchy Observations

Extract Neural ODEs from data whose channels are observed at different times and frequencies.

Certified Invertibility in NNs via MILP

Explore excessive NN invariance in various contexts, with methods for certifying invertibility pointwise across input space.

ANOVA and PCE for Biological Neural Networks

Use ANOVA to perform integrals for polynomial chaos expansions.

Representation Learning

PNAS 2020. Unsupervised learning methods to transform data into a form that's somehow more useful.

Iterative ANNs

Neural networks build on various numerical iterative algorithms.

CHO Neural ODEs

Fit a neural ordinary differential equation to Chinese hamster ovary metabolism data, with a grey-box structure including internal constraints.

Learning stochastic DEs from data

Suggest alterative methods for learning stochastic differential equations from data as neural networks.

Hamiltonian Neural Networks

Learn dynamics with constrained quantities.


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

Meta-learning of ODE integrators

Rather than learning the RHS of an ODE, learn the parameters of the integrator itself.

Learning for Multiphase Flow

After some dimension reduction by PCA and autoencoder, learn an ODE for the slow dynamics of the Navier-Stokes equations in a multiphase flow setup.

Project Opener Menu

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

GPT3 for Seminar Announcements

Use OpenAI's API to generate ics files from email text.

Next Task Decider

Process task list and decide what I should do next.

Cat Wrangler

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

Boston AV Group Robocar

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

Hierarchy Formation

Simulate the formation of dominance hierarchies through social combat.

Circadian Rhythms

Simulate circadian rhythms in the suprachiasmatic nucleus of the hypothalamus.