Summary
Summary
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My resume is available here and details on my work and skills are further down on this page and on the Research page. But here is brief summary of my applied skills:
My resume is available here and details on my work and skills are further down on this page and on the Research page. But here is brief summary of my applied skills:
In this project I used YOLO Image segmentation to distinguish between signals and noise
In this project I applied Machine Learning to find a new source of noise in gravitational wave data
In this project I performed noise modeling that led to noise reduction in the gravitational wave data
These projects show my knowledge of software tool development: Event Validation, Coherence Monitor
And in this project I performed modeling of LIGO 4 stage Suspension
Work experience
Work experience
Sept 2025 - Now
Assistant Project Scientist at UC Riverside
Aug 2021 - Aug 2025
Postdoctoral Associate at LIGO, Massachusetts Institute of Technology
Jun 2018 - May 2021
Graduate Research Assistant at Dept of Physics, Louisiana State University
Jan 2016 - June 2018
Graduate Teaching Assistant at Dept of Physics, Louisiana State University
Education
Education
PhD in Physics from Louisiana State UniversityJan 2016 - May 2021
B.Tech in Mechanical Engineering from NIT Bhopal
Skills
Skills
Data Analysis: Proficient in Python and ML libraries
Mathematics: Linear Algebra, Calculus, Probability and Statistics
Coding: Python, SQL, Bash, HTML
Machine Learning: Sound knowledge of regression and classification techniques
Software Development Work
Software Development Work
Co-developed and maintain Event Validation infrastructure that allows faster validation of gravitational wave events.
Developed a pipeline to transfer data between Event Validation and several different software tools, leading to faster analysis of the data.
Developed a python based coherence-monitoring tool that led to 50% reduction in noise in certain frequency bands.
Developed a python based tool to identify noise near gravitational wave events, leading to 40% reduction in false alerts. [1]
Developed a LIGO suspension model using Python Sympy
Have contributed to large scale code bases such as Hveto, Gwdetchar, GravitySpy and to a lot of internal LIGO code bases
First author Publications
First author Publications
GW-YOLO: Multi-transient segmentation in LIGO using computer vision https://arxiv.org/abs/2508.17399
Reducing Scattered Light in LIGO's third Observing Run arxiv.org/abs/2007.14876
Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning arxiv.org/abs/2103.12104
Modeling and reduction of high frequency scatter noise at LIGO Livingston arxiv.org/abs/2311.05730
LIGO Detector Characterization in the first half of the fourth Observing run arxiv.org/abs/2409.02831
QoQ: a q-transform based test for gravitational wave transient events arxiv.org/abs/2305.08257
A more comprehensive list of my publications is available on my google scholar profile here
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