About me!
Hi, I am a Ph.D. student at Purdue University and cuuently based at Fermilab as LPC G&V Fellow, working on the CMS experiment. My current work focuses specifically on Di-Higgs searches, developing ML solution for real-time detector readout and Outer tracker upgrades.
I am also interested in Theoratical Astrophysics & Cosmology and High-energy physics.
Education
Ph.D. in Physics
Aug 2023 - Present
Purdue University
CGPA: 3.98/4.0
Master of Science (Physics)
Aug 2022 - Jul 2023
Indian Institute of Science (IISc)
CGPA: 9.5/10.0
Bachelor of Science (Research)
Aug 2018 - Jul 2022
Indian Institute of Science (IISc)
CGPA: 9.4/10.0
Interests
Particle Physics (CMS Collaboration)
Machine Learning for Physics
Real-time Detector Readout (FPGA/ASIC)
Astrophysics & Cosmology
My Research
Unlocking the Higgs Potential: The discovery of the Higgs boson was just the beginning. My primary goal is to probe the fundamental structure of the vacuum by measuring the Higgs self-coupling ($\kappa_\lambda$) with the search for rare Di-Higgs events at LHC with CMS detector.
Smart-Detectors: As LHC luminosity increases, detectors must process vast data volumes in real time. I work in the SmartPixels collaboration to design Hardware-Aware Machine Learning (HAML) algorithms that operate directly on detector electronics (FPGAs/ASICs). These ultra-fast models are capable of making real-time trigger decisions within nanoseconds.
To achieve this, I focus on efficient ML techniques.I am currently developing PQuantML, a hardware-efficient pruning and quantization framework for optimized AI deployments.
Please reach out to collaborate!!
Selected Projects
Smart Pixels
Smart Pixels is a project focused on implementing machine learning models directly on silicon pixels or future detectors to enhance the inference of charged particle track …
•
1 min read
Di-Higgs (bbττ) Analysis
Di-Higgs (bbττ) is a search for two boosted (high transverse momentum) Higgs bosons decaying into two beauty quarks ($b$) and two tau leptons ($\tau$). This analysis focuses on Run …
•
1 min read
Featured Publications
QPOs in Compact Sources as a Nonlinear Hydrodynamical Resonance: Determining Spin of Compact Objects
Nonlinear hydrodynamical resonance model with Euler Fluid for modified QPOs, enabling spin determination of NS & BH.
Arghya ranjan das
Asymptotically flat vacuum solution for a rotating black hole in a modified gravity theory
Vacuum rotating black-hole solution in an asymptotically flat modified gravity theory.
Arghya ranjan das
Recent Publications
(2025).
In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors.
arXiv.
(2025).
Performance measurements of the electromagnetic calorimeter and readout electronics system for the DarkQuest experiment.
NIM A.
Recent & Upcoming Talks
HL-LHC Regression Model Deep Dive
A deep dive into the regression models being developed for the HL-LHC era as part of the SmartPixels collaboration.
PQuant: Streamlining ML Model Compression to Deployment for Next-Gen Detector Systems
PQuant/PQuantML: config-driven pruning + quantization pipeline (PyTorch/TensorFlow) with hardware-aware MDMM optimization; aimed at FPGA/ASIC deployment workflows (hls4ml …
Recent News
HL-LHC Regression Model Deep Dive
A deep dive into the regression models being developed for the HL-LHC era as part of the SmartPixels collaboration.
Smart Pixels
Smart Pixels is a project focused on implementing machine learning models directly on silicon pixels or future detectors to enhance the inference of charged particle track …
•
1 min read
PQuant: Streamlining ML Model Compression to Deployment for Next-Gen Detector Systems
PQuant/PQuantML: config-driven pruning + quantization pipeline (PyTorch/TensorFlow) with hardware-aware MDMM optimization; aimed at FPGA/ASIC deployment workflows (hls4ml …
In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors
In-pixel signal processing with integrated AI-based filtering for real-time data reduction in tracking detectors.
Benjamin parpillon
Sensor Co-design for smartpixels
Sensor/algorithm co-design studies for smart pixel filtering: geometry, magnetic field, radiation damage, and noise.
Danush shekar