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.
Mostly about using Farm Simulator, GTA V, and other games for training both perception and control systems.
Build an Ackermann robot with RGBD as its primary sense.
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.
PNAS 2020. Unsupervised learning methods to transform data into a form that's somehow more useful.
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.
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.
Simulate the formation of dominance hierarchies through social combat.
Simulate circadian rhythms in the suprachiasmatic nucleus of the hypothalamus.