Arghya Ranjan Das

Arghya Ranjan Das

(he/him)

Ph.D. Student | LPC G&V Fellow

Purdue University

About

Hi, I am a Ph.D. student at Purdue University and currently based at Fermilab as an LPC G&V Fellow, working on the CMS experiment. My current work focuses on Di-Higgs searches, machine-learning solutions for real-time detector readout, and Outer Tracker upgrades. I am also interested in theoretical astrophysics, cosmology, and high-energy physics.

Honors & Awards

2025

LPC Guests and Visitors (G&V) Program Award

U.S. CMS Operations / Fermilab

2024

Dr. Rolf Scharenberg Summer Graduate Fellowship

Purdue University

2018

KVPY Fellowship

Government of India

Education

  1. Physics & Astronomy

    Purdue University

    West Lafayette, United States of America

    August 2023 - Present

    Advisor: Miaoyuan Liu
  2. Master of Science (Physics)

    Indian Institute of Science (IISc)

    Bangalore, India

    August 2022 - July 2023

    Advisor: Prof. Banibrata Mukhopadhyay
  3. Bachelor of Science (Research)

    Indian Institute of Science (IISc)

    Bangalore, India

    August 2018 - July 2022

    Advisor: Prof. Banibrata Mukhopadhyay

Interests

Particle Physics (CMS Collaboration) Machine Learning for Physics Real-time Detector Readout (FPGA/ASIC) Astrophysics & Cosmology
Featured Publications

Browse Publications

On-chip probabilistic inference for charged-particle tracking at the sensor edge featured image

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Tracking charged-particles at the sensor edge, using on-chip AI inference, under strict latency, precision, and area constraints.

avatar
Arghya Ranjan Das
QPOs in Compact Sources as a Nonlinear Hydrodynamical Resonance: Determining Spin of Compact Objects featured image

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.

avatar
Arghya Ranjan Das
Asymptotically flat vacuum solution for a rotating black hole in a modified gravity theory featured image

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.

avatar
Arghya Ranjan Das
Selected Projects
PQuantML featured image

PQuantML

PQuant is a library for training compressed machine learning models, developed at CERN as part of the Next Generation Triggers project. It is designed to bridge the gap between …

Smart Pixels featured image

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 …

Di-Higgs (bbττ) Analysis featured image

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 …

Recent & Upcoming Talks

Browse 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.

avatar
Arghya Ranjan Das
PQuant: Streamlining ML Model Compression to Deployment for Next-Gen Detector Systems featured image

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 …

avatar
Arghya Ranjan Das
Latest Updates

Browse News

On-chip probabilistic inference for charged-particle tracking at the sensor edge featured image

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Tracking charged-particles at the sensor edge, using on-chip AI inference, under strict latency, precision, and area constraints.

avatar
Arghya Ranjan Das
PQuantML featured image

PQuantML

PQuant is a library for training compressed machine learning models, developed at CERN as part of the Next Generation Triggers project. It is designed to bridge the gap between …

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.

avatar
Arghya Ranjan Das
Smart Pixels featured image

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 …

PQuant: Streamlining ML Model Compression to Deployment for Next-Gen Detector Systems featured image

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 …

avatar
Arghya Ranjan Das

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