Hi, I'm Ali!

I am a graduate researcher at MIT working at the intersection of climate science, software engineering, and machine learning. I am in the Department of Earth, Atmospheric, and Planetary Sciences and the Center for Computational Science and Engineering. I am excited to apply my unique skill set to tackle challenges in weather forecasting and climate prediction.

As part of the Climate Modeling Alliance I have developed Oceananigans.jl, a fast and friendly ocean model written in Julia that runs on CPUs and GPUs with one heterogenous code base. I have used Oceananigans to develop data-driven models of oceanic turbulence and to study fluid dynamics on Earth and other planets.

You can play around with the sphere on the right to learn more about my research and Earth's climate!



Ocean model development

My work has involved developing ocean models for climate modeling. I am the original developer of Oceananigans.jl, a fast, friendly, and flexible Julia package for computational fluid dynamics on CPUs and GPUs (Ramadhan et al., 2020). Oceananigans.jl serves as the ocean component for the next-generation climate model being developed by the Climate Modeling Alliance. I led development of the first release and implemented features to turn it into a fully-fledged ocean model. I also worked on

  • implementing support for GPUs with CUDA and distributed computing with MPI,
  • supporting global ocean modeling on conformal cubed sphere grids,
  • collaborating with researchers to accelerate research at several institutions,
  • building comprehensive test suites with robust continuous integration pipelines,
  • and writing extensive documentation and tutorials.

Capturing missing physics in climate models using machine learning

Even with today's immense computational resources, climate models cannot resolve every cloud in the atmosphere or eddying swirl in the ocean. However, collectively these small-scale turbulent processes play a key role in setting Earth's climate. The problem is that they are not well-represented in climate models.
My research combines physical principles and machine learning to improve how turbulence is represented in climate models We take simple yet robust models of these turbulent processes and augment the partial differential equations they solve with neural networks. The augmented models are trained using resolved simulations of the turbulent process generated using Oceananigans.jl so the neural networks learn the missing physics (Ramadhan et al., 2023).

Fluid dynamics on Earth and other planets

In collaboration with oceanographers at MIT, we have leveraged Oceananigans.jl to tackle research problems requiring the speedup provided by GPUs. For example, we have fit oceanic convection models using Bayesian inference, accurately modeled meltwater from Antarctic ice shelves, and even investigated the circulation of subsurface oceans on icy moons such as Jupiter's Europa and Saturn's Enceladus.

Southern Ocean dynamics

I have done some work on bringing in a dynamical perspective to how the Southern Ocean's meridional overturning circulation interacts with the ocean surface and sea ice around Antarctica (Ramadhan et al., 2022). This was done by inferring surface stress patterns from decades of observational satellite data. We also investigated trends that may explain patterns and rates of Antarctic land ice loss and sea level rise.

Idealized modeling of the ecological dynamics of marine microbes

Marine microbial communities lie at the bottom of the oceanic food web sustaining all marine animal life. The geographical structure of these microbial communities, and thus oceanic biodiversity, is set by short-range ecological interactions. To investigate how these interactions affect biodiversity, we utilized an agent-based modeling approach in which millions of marine microbes are modeled as particles that are advected by surface ocean currents derived from satellite observations. The interaction was modeled using an imbalanced probabilistic rock-paper-scissors game leading to the reproduction of observed ecological phenomena.

Other projects

Project Lovelace: Developing computational thinking in science students

Project Lovelace is an open online platform for learning about science and developing computational thinking through programming and problem solving. It is a collection of computational science problems and tutorials taken from all branches of the natural, social, and mathematical sciences. Each problem teaches a scientific application (e.g. locating earthquakes, DNA splicing) and requires the use of scientific insight and some programming skills to solve. Tutorials teach computational methods that students and researchers may find useful (e.g. solving differential equations, Bayesian inference) and may be required knowledge for some problems.

While Project Lovelace is still in development, we are deploying the website and the problems one by one throughout the winter in preparation for a pilot run in April 2017. In addition to the website's recreational aspect we ultimately hope that the problems and tutorials may be used in undergraduate courses, especially to complement courses that lack a computational portion as computational methods have become ubiquitous in almost every field of science.

The name commemorates Ada Lovelace who proposed the first algorithm to be run on a computer in the 1840's.

Molecular movies and geometry reconstruction using Coulomb explosion imaging

Have you ever seen a molecule bend or participate in a chemical reaction? If so, probably not directly: single molecules are notoriously hard to observe for any length of time. For my MSc thesis I developed a rigorous computational framework to create movies of individual molecules undergoing chemical reactions using Coulomb explosion imaging (CEI), a technique of studying the ultrafast dynamics of smaller molecules in the gas phase. While CEI has always promised that atomic structures may be measured, in practice no rigorous method is available and instead the momentum vectors are studied. The momentum vectors tell a large part of the story but aren't as satisfying to study as the actual structure everyone seeks so a method of retrieving the structure is highly desirable.

The structure may be recovered by attempting to simulate the CEI experiment backwards in time; however, solving for the molecular geometries constitutes an ill-posed nonconvex optimization problem that is difficult to tackle computationally even for small molecules. I am actively trying different optimization approaches and also collaborating with the Department of Statistics and Actuarial Science on a fully statistical approach using Bayesian inference. So far the statistical approach seems more promising as it will allow for the inclusion of measurement uncertainty which every previous study has neglected and seems to scale well to larger molecules (Ramadhan, 2017).

Molecular movie of proton migration in acetylene imaged in momentum space (by other members of my group). Multiple (two) structures found in searching for molecular geometries showcasing the ill-posed nature of the optimization problem.

Ultrafast molecular dynamics probed by synchrotron radiation

The geometries and dynamics of small gas molecules may be studied by Coulomb explosion imaging (CEI), providing a means of directly probing the atomic structure and dynamics of smaller molecules in the gas phase, a regime where no method is viable. CEI is usually performed using ultrashort laser pulses (~10−15 s) with the goal of "blowing up" the molecule as fast as possible to minimize the disturbance to the molecule and ensure accurate imaging of the atomic structure.

As a proof of principle, we were able to use single X-ray photons from the Canadian Light Source synchrotron to study the dynamics of dissociative ionization in the OCS molecule using CEI. The use of single X-ray photons led to faster ionization and "blow up" compared to short laser pulses, showing promise for greater temporal precision in CEI experiments. It also allowed us to identify a surprisingly rich set of ultrafast molecular dynamics for the first time (Ramadhan et al., 2016).

Easy end-cap control in polyyne synthesis using ultrafast lasers

The traditional synthesis method for polyynes, an allotrope of carbon with chemical structure (−C≡C−)n, is a challenging and dangerous multistep procedure that provides little control over the their end-caps. Yet polyynes are of great interest in interstellar chemistry and especially in nanotechnology as potential elements for molecular machines and carbon cluster precursors. Their end-caps may endow them with extra functionality and so a safe and controllable synthesis procedure is highly desirable.

By irradiating different liquid solvents with short laser pulses, we are able to easily synthesize long-chain polyynes and demonstrate end-cap control for methyl caps. Using high-performance liquid chromatography (HPLC), we have confirmed the synthesis of polyynes up to C18H2 and methyl-capped polyynes up to HC14CH3. This opens the possibility for controlling the synthesis of other polyyne molecules and their efficient mass production (Ramadhan et al., 2016).

Synthesis of graphene oxide gels and thin films with tunable properties

Graphene has attracted an enormous amount of attention over the past decade owing to its peculiar properties and vast applicability. However, large-scale single layers of pristine graphene are difficult to obtain and thus much research has focused on graphene oxide gels which may be used to produce high-quality graphene. These gels have their own applications too, such as drug delivery and sensor engineering. To satisfy the large demand for graphene oxide gels, an efficient production method is highly desirable.

By irradiating aqueous graphene oxide with femtosecond laser pulses, we were able to convert the solution into a gel with physical and chemical properties comparable with those of a monolayer graphene sheet. We were also able to control the properties of the synthesized gel by simply tuning the laser pulse's properties allowing for the production of different gels suitable for building nano-sized graphene photodetectors and transistors (Ibhrahim et al., 2014).


  1. Capturing missing physics in climate model parameterizations using neural differential equations
    A. Ramadhan, J. C. Marshall, A. N. Souza, X. K. Lee, U. Piterbarg, A. Hillier, G. L. Wagner, C. Rackauckas, C. Hill, J.-M. Campin, R. Ferrari
    Submitted to Journal of Advances in Modeling Earth Systems (2023). ESSOAr pdf
  2. Divergent behavior of hydrothermal plumes in fresh versus salty icy ocean worlds
    S. Bire, T. Mittal, W. Kang, A. Ramadhan, P. Tuckman, C. R. German, A. M. Thurnherr, J. C. Marshall
    Submitted to Journal of Geophysical Research: Planets (2023). ESSOAr pdf
  3. TOI-1075 b: A Dense, Massive, Ultra-short-period Hot Super-Earth Straddling the Radius Gap
    Z. Essack, A. Shporer, J. A. Burt, et al. (including A. Ramadhan)
    The Astronomical Journal 165(2), 47 (2023). doi pdf
  4. Observations of Upwelling and Downwelling Around Antarctica Mediated by Sea Ice
    A. Ramadhan, J. Marshall, G. Meneghello, L. Illari, K. Speer
    Frontiers in Marine Science 9, 864808 (2022). doi pdf
  5. Exploring Ocean Circulation on Icy Moons Heated From Below
    S. Bire, W. Kang, A. Ramadhan, J.-M. Campin, J. Marshall
    Journal of Geophysical Research: Planets 127, e2021JE007025 (2022). doi pdf news
  6. On the Settling Depth of Meltwater Escaping from beneath Antarctic Ice Shelves
    C. W. Arnscheidt, J. Marshall, P. Dutrieux, C. D. Rye, A. Ramadhan
    Journal of Physical Oceanography 51(7), 2257–2270 (2021). doi pdf
  7. Near-Inertial Waves and Turbulence Driven by the Growth of Swell
    G. L. Wagner, G. P. Chini, A. Ramadhan, B. Gallet, R. Ferrari
    Journal of Physical Oceanography 51(5), 1337–1351 (2021). doi pdf
  8. Uncertainty Quantification of Ocean Parameterizations: Application to the K-Profile-Parameterization for Penetrative Convection
    A. N. Souza, G. L. Wagner, A. Ramadhan, B. Allen, V. Churavy, J. Schloss, J. Campin, C. Hill, A. Edelman, J. Marshall, G. Flierl, R. Ferrari
    Journal of Advances in Modeling Earth Systems 12, e2020MS002108 (2020). doi pdf
  9. Oceananigans.jl: Fast and friendly geophysical fluid dynamics on GPUs
    A. Ramadhan, G. L. Wagner, C. Hill, J.-M. Campin, V. Churavy, T. Besard, A. Souza, A. Edelman, R. Ferrari, J. Marshall
    Journal of Open Source Software 5(53) 2018 (2020). doi pdf
  10. Universal Differential Equations for Scientific Machine Learning
    C. Rackauckas, Y. Ma, J. Martensen, C. Warner, K. Zubov, R. Supekar, D. Skinner, A. Ramadhan, A. Edelman
    arXiv:2001.04385v4 [cs.LG] (2020). arXiv pdf
  11. Molecular movies and geometry reconstruction using Coulomb explosion imaging
    A. Ramadhan
    Master's thesis, University of Waterloo (2017). uri pdf
  12. X-Ray Dosimetry During Low-Intensity Femtosecond Laser Ablation of Molybdenum in Ambient Conditions
    M. J. Wesolowski, C. C. Scott, B. Wales, A. Ramadhan, S. Al-Tuairqi, S. N. Wanasundara, K. S. Karim, J. H. Sanderson, C. A. Wesolowski, P. S. Babyn
    IEEE Transactions on Nuclear Science 64(9), 2519–2522 (2017). doi pdf
  13. Synthesis of hydrogen- and methyl-capped long-chain polyynes by intense ultrashort laser pulse irradiation of toluene
    A. Ramadhan, M. Wesolowski, T. Wakabayashi, H. Shiromaru, T. Fujino, T. Kodama, W. Duley, J. Sanderson
    Carbon 118, 680–685 (2017). doi pdf
  14. Ultrafast molecular dynamics of dissociative ionization in OCS probed by soft X-ray synchrotron radiation
    A. Ramadhan, B. Wales, I. Gauthier, R. Karimi, M. MacDonald, L. Zuin, J. Sanderson
    Journal of Physics B: Atomic, Molecular, and Optical Physics 49, 215602 (2016). doi pdf
  15. A Novel Femtosecond Laser-Assisted Method for the Synthesis of Reduced Graphene Oxide Gels and Thin Films with Tunable Properties
    K. Ibrahim, M. Irannejad, M. Hajialamdari, A. Ramadhan, K. Musselman, J. Sanderson, M. Yavuz
    Advanced Materials Interfaces 3, 1500864 (2016). doi pdf
  16. Ultrafast Light Interaction with Graphene Oxide Aqueous Solution
    K. Ibrahim, M. Irannejad, A. Ramadhan, W. Alayak, J. Sanderson, B. Cui, A. Brzezinski, M. Yavuz
    Proceedings of the 14th IEEE International Conference on Nanotechnology, 830–831 (2014). doi pdf
  17. Welding of Au Microwires by Femtosecond Laser Irradiation
    N. Ly, M. Mayer, A. Ramadhan, and J. Sanderson
    Proceedings of the 14th IEEE International Conference on Nanotechnology, 146–149 (2014). doi pdf
  18. Coulomb imaging of the concerted and stepwise break up processes of OCS ions in intense femtosecond laser radiation
    B. Wales, É. Bisson, R. Karimi, S. Beaulieu, A. Ramadhan, M. Giguère, Z. Long, W. Liu, J. Kieffer, F. Légaré, J. Sanderson
    Journal of Electron Spectroscopy and Related Phenomena 195, 332–336 (2014). doi pdf


Climate and weather



  • Project Lovelace: Code for projectlovelace.net. A website with scientific programming problems with automatic judging of submissions.
    • Website: Backend written in Python using Django. Frontend built with HTML, CSS, and Javascript using the Bulma CSS framework.
    • Engine: Automated, secure testing of code submissions in Python, Javascript, Julia, and C.
    • Problems: Modules for generating test cases.
  • DocumenterCitations.jl: Add BibTeX citations and bibliographies to documentation for your Julia packages.
  • Micrograd.jl: A Julia implementation of Andrej Karpathy's micrograd, a tiny automatic differentiation package built from scratch.
  • Plotting location history using Python and Cartopy: Produce pretty maps of where you've been using location history data. Great for visualizing motorcycle road trips.