WE EXPLORE INTERDISCIPLINARY RESEARCH IN AI FOR COMPLEX SYSTEMS & MUSIC TECHNOLOGY

Data-Driven Modeling Lab

Our research explores the fascinating intersection of complex systems and artificial intelligence.

Music Intelligence Lab

A collective of engineers, musicians, scientists, and enthusiasts dedicated to creating innovative music within our socio-cultural context.

Snippets of some TOPICS we are working on

Dynamical Systems
Deep Learning
SINDy
System Identification
Data-driven Discovery of Physical Models
Can we automate the process of scientific discovery? Can we distill observations into physically meaningful and predictive variables? My research group tackles these questions using a combination of physics-informed machine learning techniques with applications to fluid dynamics, granular materials, and nonlinear chaotic systems.
Multiscale Modeling
Complex Systems
Granular Materials
Agent-based Modeling
Uncertainty Quantification
Multiscale modeling of complex systems
The world is made of things. And those things interact. Sometimes, we know the rules at one scale. But even when we do, it’s hard to bridge the scales. We develop mathematical and computational techniques for multiscale modeling, with a wide range of applications, from fluid dynamics and granular materials, to social dynamics and neuroscience.
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Music
Machine Learning
Human-Computer Interface
Music Technology and VR
Musical instruments shape musical expression and the compositions that are being written for them. We build electronic musical instruments that maximize the range of expression by combining soft electronics design, algorithmic composition and machine learning.
Visualization
Natural Language Processing
Graphs
LLM
Semantic Visualization and Dynamics
Words and their meanings evolve, morph and reproduce in symbiosis with the culture that defines them. We study the dynamics of words, the graphical relationships between them, and develop visualization methods for effective exploration and learning.

ADDITIONAL EDUCATIONAL Content

Introduction to Machine Learning

3 videos | 1 article
This series covers a brief introduction to machine learning and deep learning, with practical demonstrations in Python using packages like Scikit-learn and Tensorflow.
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Uncertainty Quantification

1 article
Most differential equations that describe the behavior of quantities of interest do not account for uncertainties. The PDF method is a powerful technique that transforms deterministic equations into probabilistic equations. This is tutorial introduces the basics.

SELECTED PUBLICATIONS

2023 - Proceedings of the Royal Society A
Discovering governing equations from partial measurements with deep delay autoencoders.
— Bakarji, J., Champion, K., Nathan Kutz, J., & Brunton, S. L.
2022 - Nature Computational Science
Dimensionally consistent learning with buckingham pi.
— Bakarji, J., Callaham, J., Brunton, S. L., & Kutz, J. N.
2021 - Journal of Computational Physics
Data-driven discovery of coarse-grained equations.
— Bakarji, J., & Tartakovsky, D. M.