About · Joseph Bakarji

Joseph Bakarji is an Assistant Professor in the Department of Mechanical Engineering, and the School of Computing and Data Sciences, at the American University of Beirut. He received his PhD at Stanford University in 2020, where he developed multiscale stochastic models for granular materials, receiving the Henry J. Ramey, Jr. and the Frank G. Miller fellowship awards. He then spent three years as a postdoctoral fellow at the AI Institute in Dynamic Systems, at the University of Washington, where he developed scientific machine learning methods for complex systems with Nathan Kutz and Steven Brunton. His current research spans a wide range of applications revolving around machine learning for scientific discovery, and most recently, algorithmic music composition and music interface design.

Download CV (PDF) Google Scholar GitHub jb50@aub.edu.lb

In a nutshell

Education

2017 – 2020 Ph.D., Granular Materials. Stanford University, Department of Energy Science and Engineering. Advisor: Daniel Tartakovsky. Thesis: Stochastic multiscale modeling of complex materials.
2013 – 2016 M.S., Fluid Mechanics. University of California San Diego, Department of Mechanical and Aerospace Engineering. Thesis: Discrete-to-continuum modeling with reverse Brownian motion.
2009 – 2013 B.Eng., Mechanical Engineering. American University of Beirut.

Positions

2024 – present Assistant Professor. Department of Mechanical Engineering, and the Artificial Intelligence, Data Science, and Computing Hub, American University of Beirut. Lead of the Bakarji Lab (Data-Driven Modeling and Music Intelligence).
2020 – 2023 Postdoctoral Fellow. AI Institute in Dynamic Systems, University of Washington. With Steve Brunton and Nathan Kutz.
Summers 2014, 2016 Research Intern. Los Alamos National Laboratory.
2015 – 2016 Researcher. UC San Diego Music Technology Lab.

Awards and honors

2022 Center for Advanced Mathematical Sciences Fellowship, AUB.
2020 Henry J. Ramey, Jr. Fellowship Award, Stanford University.
2018 Frank G. Miller Fellowship Award, Stanford University.

Teaching

See the teaching page for the full course list. Highlights:

Machine Learning for Science and Engineering, AUB, 2022 to present.
Introduction to Machine Learning, AUB, Fall 2024.
Deep Learning for Fluid Dynamics, University of Washington, Spring 2021.
Dynamics, AUB, Spring 2025.
Multiphase Flow in Porous Media, AUB, Summer 2018.

Themes across a decade of notes

While preparing this website, I decided to distill about 1,500 notes, transcribed voice memos, and writings into a graph using LLM-based tagging. It turns out, according to my LLM tags, I have strong philosophical leanings in everything I write. I must say, I'm not very surprised, and if you know me you wouldn't be either; but I was trying to keep it a secret.

Reading and writing

On the philosophical end of my writing, you can find some things on my Substack, The Latent Dimension, and shorter posts on my articles page. If you're curious about the longer story behind the research, the questions behind the science, and how music and instrument design became part of the same inquiry, I've written a companion piece: Why I do what I do.

Contact

Email: jb50 [at] aub.edu.lb
GitHub · Google Scholar · LinkedIn · Substack · SoundCloud