What science questions do you investigate?
My research interests span a wide area of astrophysics, including high energy astrophysics, multi-messenger astrophysics and computational astrophysics. Starting with high energy astrophysics, my primary work uses data from the Fermi Gamma-ray Space Telescope (Fermi Large Array Telescope; Fermi-LAT) to study the processes in distant galaxies hosting actively accreting supermassive black holes (active galactic nuclei; AGN). AGN comprise the most numerous class of gamma-ray sources observed over time, due to the relativistic radiation emitted from their center.
In addition to gamma-rays and photons from other wavelengths (like X-rays and radio waves), I am also interested in studying another kind of emission from AGN: neutrinos! Neutrinos are extremely elusive particles that are detected by large scale observatories like the IceCube detector at the South Pole. This forms the basis of my multi-messenger astrophysics research, which combines neutrino and photon data to understand particle processes inside an AGN.
Over the course of my research, I have found that astrophysics research has a very close connection to computational techniques, such as advanced data science and machine learning (ML). Building on this connection, I am currently using ML and artificial intelligence (AI) techniques to expand the scientific return of archival data collected by NASA missions. These methods enable us to advance our understanding of topics that were previously not explored either due to time or due to computational constraints. To briefly mention my current research project, I am working with my greatest collaborator in science and life, my wife who is also a computational astrophysicist, on developing an AI tool. This tool will help in the simulation of high energy (X-ray or Gamma-ray) data emitted from astrophysical objects, namely distant extragalactic Gamma-ray bright sources like AGN or Gamma-ray Bursts (GRBs). The simulations will be performed after training the model on observed data from the Fermi LAT mission along with additional simulations created using theoretical models of the sources. These simulations will, in turn, support the planning of future NASA astrophysics missions, like the proposed AMEGO-X (All-sky Medium-Energy Gamma-ray Observatory eXplorer) mission which will bridge the “sensitivity gap” between hard X-rays and high-energy gamma rays. Additionally, the tool will also be made flexible enough to be used for simulations and observation planning of future high-energy NASA missions.
Did you always know that you wanted to study astrophysics?
When I was a kid, I, like most people, wanted to be an astronaut. I later realized that this dream was stemming from my love of understanding the universe and solving its mysteries. I also realized that while being an astronaut is extremely thrilling, being an astrophysicist allowed me to get a scientific view of not only close-by objects, but also extremely distant objects, all from the safety of Earth. This made me focus on astrophysics, and I started to plan a career path to become an astrophysics research scientist with the support of my dad, who was also my most trusted career consultant. As my undergraduate studies were conducted in India, we decided to keep another possible career path open and pursue electrical engineering, which also builds a strong scientific base. I then got my specialization in Physics and Astronomy from my Master’s degree at California State University (CSU) Fullerton and my PhD at Clemson University, where my passion for science was firmly established. In terms of sub-fields, I have also worked on observational astronomy and stellar astrophysics, but high energy astrophysics, and later multi-messenger and computational astrophysics, became my true scientific interests.
How did you end up working at NASA Goddard?
When I was a graduate student, I came across the NASA Postdoctoral Program (or NPP) at a conference, and I was very interested in the opportunity based on the great research done at NASA Goddard. I applied to the program after completing my PhD, however my proposal was not funded at that time. Later, after my first postdoctoral fellowship at the University of Wisconsin Madison, I used the knowledge I gained as a postdoc to develop a new research project along with my collaborators. This project focused on observing the multi-messenger signals emitted from distant AGN using a combination of NASA missions observing in the X-ray regime, specifically NICER, NuSTAR and Chandra, along with gamma-ray data collected by the Fermi-LAT telescope and neutrino detections from the IceCube Observatory. This project was funded by the NPP program and led to my NPP fellowship at Goddard. The resulting work was published in The Astrophysical Journal in late 2025. Following this, my current contract (which started in February 2026) as an assistant research scientist, focuses on supporting science operations and research analysis tasks for the Fermi-LAT mission while working on my ongoing research projects involving AI and ML.
What is one research project that you are particularly excited about, and why?
I am particularly excited about research that applies modern AI techniques to advance astrophysical discovery. This is especially interesting in the current time due to the boom in the AI industry and its transformative impact across many disciplines. While most of the focus of these tools is understandably concentrated in industry applications, it is important to understand their capacity to be used for astrophysics research. In fact, one of the primary uses of ML comes from astrophysics, where these techniques are used to clean, interpret and analyze billions of data points collected by telescopes or particle detectors. However, there is a clear need to update and expand our data-analysis pipelines by integrating more advanced AI models that can handle increasing data volume, complexity, and time sensitivity. Beyond large-scale data analysis, generative AI offers powerful new opportunities. For example, these models can be used to read text (like alerts from missions) and make rapid decisions about potential follow-up observations. In such cases, the shorter the response time, the better, and current AI tools have the capacity to deliver that. My research focuses on developing generative AI models for such real time follow-up applications, enabling faster and efficient scientific decision making.
What are your future research interests and goals?
My interests and goals naturally evolve alongside advances in science and technology. My current and future focus is increasingly centered on the development of scientific software, and its use in advancing astrophysics research. My upcoming duties as an assistant research scientist at GSFC include helping with the Fermi-LAT science operations in addition to working on my ongoing AI/ML projects. The Fermi-LAT science operations duties include working on the detector calibration software and helping migrate the code to a new cluster making it more accessible for users. Looking ahead, I aim to build on these experiences to contribute to the development of reliable, scalable software tools that support scientific analysis, particularly those that make effective use of NASA data. In parallel, I am continuing to pursue research projects that apply AI methods to astrophysical problems, as well as interpreting generative AI models. An example of this is using generative AI models on astrophysics alerts to reduce the time for follow-up activities with particle detectors. At the same time, it is essential to understand how these models produce their results and to evaluate and explain their responses, ensuring they are used responsibly and effectively in scientific research. My goal is, like always, the advancement of science and technology and having fun while working on it.
What early career advice do you have for those looking to do what you do?
My main advice for early career scientists is to “keep going and do not give up”. I had my fair share of setbacks on my path, but my family and friends gave me the support to keep going. Based on what I was taught growing up and what I learned from watching anime, I feel that problems or hurdles do not stop us, but they instead give us opportunities to grow and improve. I would advise early career scientists to take every failed proposal, critical comment, bad presentation, etc. as learning opportunities to make sure the work you do is the best it can be, especially in your eyes! Additionally, I would suggest networking with people outside your field of research, as they can help you gain new skills and improve your work in ways you would not otherwise have imagined. These also include your friends and family members, including those not familiar with your work. I have found that they sometimes give the best advice. And finally, something my mom and dad always say: just give your best and leave the rest, and don’t forget to have fun on the way!
What is a fun fact about you?
I absolutely love to travel and go on long drives to new destinations with my wife. In the future I aim to adopt a dog and a cat (yes, both) and train them to go on long drives to new places with us.
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Hometown:
Vadodara, Gujarat, India
Undergraduate Degree:
BE Electrical Engineering, Sardar Vallabhbhai Patel Institute of Technology, Vasad, Gujarat, India
Post-graduate Degrees:
M.S. Physics from California State University Fullerton, CAPh.D. Physics and Astronomy from Clemson University, SC