Ramiz A. Qudsi

I graduated from University of Delaware with a PhD. degree in Physics in 2021 under the guidance of Dr. Bennett Maruca. I am currently working as a Research Scientist at Center for Space Physics in Boston University. My current research focus is on space plasmas where I have published multiple papers on the interplay between microkinetic processes and turbulence. Check my Research page for more details on my work and my peer reviewed published papers.

I graduated from Indian Institute of Space Science and Technology in 2012 with a B. Tech degree in Physical Sciences and specialization in Astronomy and Astrophysics. I then worked at National Remote Sensing Center, Hyderabad (a center of Indian Space Research Organization) as Scientist 'C' for 3 years before leaving to pursue my PhD. in Physics from University of Delaware.

When not busy sciencing, I love to read and write. My favorite genre in literature is fantasy. J. R. R. Tolkien, G. N. U. Pratchett, and Brandon Sanderson are my absolute favorite authors. I like writing poems and micro-fictions. You can read some of them on my Blog. I am also an avid FC Barcelona and South Africa cricket team fan.

I am currently working as a Research Scientist at Boston University with Dr. Brian Walsh. At present, my main focus is on LEXI and the development of associated data-pipeline as well as the data analysis tools. LEXI is a soft X-ray imager developed to provide wide field-of-view images of the interaction between the solar wind and Earth’s magnetosphere. The telescope was launched in January 2025 and landed on the Moon in March where it successfully completed its science objectives.

I am also working on studying magnetic reconnections in Earth's magnetopause where I am refining predictions of reconnection X-lines in the Earth's magnetosphere using data from Magnetospheric Multiscale (MMS) Mission.

I am also involved in retrograde collisional analysis in solar wind, where I study how the VDF of protons and heavier ions change because of collision as they travel from the Sun to 1 au.

I am also working on development of a sophisticated machine learning algorithm which will help in full 3-D reconstruction of interplanetary magnetic field using observation from 4 or more spacecraft in solar wind. The algorithm will work for an arbitrary number of spacecraft in a random orientation. Another machine learning project I am involved in attempts to find large-scale average trends in plasma parameters across the Heliosphere using data from 12-15 spacecraft.

Space plasmas in the inner heliosphere exist in a weakly collisional and turbulent state. Though energy transfer from large scales to smaller scales by turbulent cascade is widely accepted as an important feature of space plasmas, details of its exact dissipation process are lacking. Features arising because of turbulence, such as intermittency and temperature anisotropy, play important roles in the dynamics of space plasmas. Microkinetic linear instabilities induced by temperature anisotropy have been shown to change the statistical characteristics of plasma in a significant way.

Since the two processes, turbulence cascade and microkinetic instabilities, occur in the same physical and phase space, there is an interplay at work. In this study we investigated this interplay and the subsequent competition arising between the two, linear and nonlinear, processes. We found an explicit connection between intermittency and linear instability growth rates. We also showed localization of temperature enhancement regions along the intermittent structures, which in turn can trigger linear instabilities. Investigation of the two processes shed light on why linear theory works as well as it does, and shows the complicated nature of their interplay.

Information related to the exact spatial structure of the interplanetary magnetic field is vital to our understanding of the type of turbulence active in the space plasmas and the mechanism of turbulence cascade. This will help us discern the interplay between the two processes. We thus also report on a proof of concept study of magnetic field topology reconstruction using Gaussian Processes in machine learning.

Read Full Thesis

Real-time Solar Wind parameters

27 Days solar wind parameters derived from DSCOVR dataset. Data is downloaded from NOAA website and processed further to give an hourly averaged data for the entire period. Magnetic field is in GSM coordinate system. The video is updated every 6 hours.

Codes used to produce the figures are available on GitHub.