Ramiz A. Qudsi

Ramiz Qudsi graduated from University of Delaware with a PhD. degree in Physics in 2021 under the guidance of Dr. Bennett Maruca. He is currently working as a Postdoctoral Associate at Center for Space Physics in Boston University. His research area focusses on space plasmas where he has published multiple papers on the interplay between microkinetic processes and turbulence. Check his Research page for more details on his work and his peer reviewed published papers.

He 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. He then worked at National Remote Sensing Center, Hyderabad as Scientist 'C' for 3 years before leaving to pursue his PhD. in Physics from University of Delaware.

When not busy sciencing, he loves to read and write. His favorite genre in literature is fantasy. J. R. R. Tolkien, Neil Gaiman and Brandon Sanderson are his absolute favorite authors. He likes writing poems and tiny tales. You can read some of them on his Blog. He is also an avid FC Barcelona and Indian cricket team fan.

Ramiz is currently doing research as a Postdoctoral Associate at Boston University with Dr. Brian Walsh. Main focus of his work is studying magnetic reconnections in Earth's magnetopause where he is working on refining predictions of reconnection X-lines in the Earth's magnetosphere using data from Magnetospheric Multiscale (MMS) Mission.

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

Ramiz is 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 he is 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.

Link to the other blog