Rebecca Gjini

Graduate Student Researcher in Geophysics

I am a fourth year Ph.D. candidate in the Institute of Geophysics and Planetary Physics (IGPP) within Scripps Institution of Oceanography (SIO) at the Univesrity of California, San Diego (UCSD). I am currently working on feature-based data assimilation for cloud microphysics under the guidance of Matthias Morzfeld. More specifically, I explore the uses of mathematics, computation, and inverse modeling to build bridges between two different mathematical models of stratocumulus clouds. Before UCSD, I studied at Lehigh University, getting an undergraduate degree in mathematics. I then worked six months at the Argonne National Laboratory as an intern in both the Mathematics and Computer Science (MCS) and Environmental Science (EVS) divisions before coming to graduate school. My main research interests are in data assimilation, inverse modeling, derivative-free optimization, and cloud physics.

download cv

Announcements and Upcoming Events:

Attending the CRC 1294 Summer School (September 2024).

Teaching Assistant for SIO 25 during the Fall 2024 quarter (September 2024).

Education.

  • 2021-Present

    Institute of Geophysics and Planetary Physics at Scripps Institution of Oceanography, UC San Diego (La Jolla CA)

    Doctor of Philosophy in Earth Science (in progress)
    Advisor: Matthias Morzfeld

  • 2021-2023

    Institute of Geophysics and Planetary Physics at Scripps Institution of Oceanography, UC San Diego (La Jolla CA)

    Master of Science in Earth Science

  • 2017-2020

    Lehigh University (Bethlehem, PA)

    Bachelor of Science in Mathematics, with highest honors
    Minors in Computer Science and Envrionmental Studies

Research.

Stratocumulus Clouds

Stratocumulus clouds are low level clouds with cloud decks that cover immense stretches of subtropical oceans and provide a net cooling effect on the planet.

Stratocumulus clouds are low level clouds with cloud decks that cover immense stretches of subtropical oceans and provide a net cooling effect on the planet. Stratocumulus clouds are an important area of study because clouds and aerosols are one of the largest sources of uncertainty when calculating Earth’s energy budget. My research explores the use of mathematics, computation, and inverse modeling to better understand stratocumulus clouds. Specifically, I work with two very different stratocumulus cloud models and connect the two using feature-based inversion. The first model is known as a large-eddy simulation (LES), a computationally expensive, 3D and time simulation that describes the state of the atmosphere. The second model is a phenomenological model based on predator-prey dynamics. In this scenario, the rain is the predator of clouds. To learn more about how I utilize inverse modeling to compare these two stratocumulus models, check out my recent SIAM News article below.

Stratocumulus Clouds and Predator-prey Dynamics

Ensemble Kalman Inversion

Ensemble Kalman Inversion (EKI) is a derivative-free optimization method used to solve nonlinear least squares problems.

Ensemble Kalman Inversion (EKI) is a derivative-free optimization method used to solve nonlinear least squares problems. The algorithm is designed to solve for the minimizer of a cost function over multiple iterations. EKI relies on an ensemble to approximate the derivatives needed to iterate through an optimization problem. The derivative approximations are calculated from the ensemble itself using covariance matrices. During each iteration, the ensemble members are updated using the derivative approximation and eventually the ensemble will collapse onto the minimizer of the cost function.

Automatic Differentiation

Also known as algorithmic differentiation (AD), AD is a tool that is used to compute function derivatives in a methodological way.

Also known as algorithmic differentiation (AD), AD is a tool that is used to compute function derivatives in a methodological way. When a human programmer codes a function and its associated derivative, typically the derivative needs to be coded by hand. AD allows programmers to code up their original function and use the AD computer algorithm to solve for the derivative function. I implemented AD within a python package called PyDDA (Pythonic Direct Data Assimilation) during my internship at the Argonne National Laboratory. PyDDA uses data from multiple weather radars to get 3D wind field velocities based on the 3D variational technique. I implemented AD within all the gradient functions of the package used to perform the cost function minimization. For more information on PyDDA and how AD is used within the package check out the PyDDA documentation and github.

PyDDA documentation PyDDA github

Inverse Theory

Inverse problems are important scientific and mathematical problems because the solutions help us understand physical processes that are difficult to observe.

Inverse problems are important scientific and mathematical problems because the solutions help us understand physical processes that are difficult to observe. The opposite of a forward problem, inverse problems are used to determine the factors that cause a set of observations to occur. One method of solving inverse problems is to use a Bayesian approach. The idea behind Bayesian inversion is to better understand model parameters (and their associated model output) in relation to data. In my research, I specifically use feature-based Bayesian inversion and Markov-chain Monte Carlo (MCMC) to numerically solve an inverse problem. For more information on feature-based inversion and its applications to various numerical problems, below is a paper written by my advisor.

Feature-based data assimilation in geophysics

I am really interested in using different mathematical tools to improve understanding of the climate system.

Graph Scattering Transform

The scattering transform is a mathematical model of Convolutional Neural Networks (CNNs) allowing the use of predefined wavelet filters.

The scattering transform is a mathematical model of Convolutional Neural Networks (CNNs) allowing the use of predefined wavelet filters. Motivated by classification tasks, I worked on applying the scattering transform to graph structured data during an REU at Michigan State University with several other students. The scattering transform produces a sequence of coefficients at each layer of the network which can be used to classify different classes of graph data. During the REU, I, along with my research group, helped implement the scattering transform to improve upon results from previous studies, classify different models of random graphs, and explore the use of principal component analysis applied to the scattering coefficients. For more information on this work, below is a link to a virtual poster presentation my research group and I presented at an undergraduate conference.

Graph Scattering Transform

Publications.

Improving PyDDA’s atmospheric wind retrievals using automatic differentiation and Augmented Lagrangian method

2022 Proceedings of the 21st Python in Science Conference, 2022, pp. 210–216

R. Jackson, R. Gjini, S. H. K. Narayanan, M. Menickelly, P. Hovland, J. Hückelheim, and S. Collis

Read the article

TROPHY: Trust Region Optimization Using a Precision Hierarchy

International Conference on Computational Science, Springer International Publishing, 2022, pp. 445–459

R. J. Clancy, M. Menickelly, J. Hückelheim, P. Hovland, P. Nalluri, and R. Gjini

Read the article

Experience.

Contact.

rgjini@ucsd.edu
  • Institute of Geophysics and Planetary Physics,
  • Munk Building,
  • Scripps Institution of Oceanography,
  • UC San Diego