Current deep learning models for CMS particle reconstruction are often task-specific. This project aims to develop versatile foundation models by...

This project develops an open-source, diffusion-based super-resolution (SR) pipeline for enhancing the resolution of gravitational lensing images....

This proposal aims to build upon and expand the progress made in recent years on Quantum Vision Transformers (QViTs) for High Energy Physics (HEP)...

This project tackles the challenge of learning hidden symmetries in complex, high-dimensional physics data using modern machine learning techniques....

Machine learning based analyses are proving to be very potent in understanding complex physical systems. This project aims to develop a reinforcement...

This project focuses on developing a new machine learning approach called Quantum Model-Agnostic Meta-Learning (Q-MAML) specifically for high-energy...

The project aims to develop a physics-informed neural network framework to infer the properties and distribution of dark matter in the lensing galaxy...

Because of its cylindrical shape, the Compact Muon Solenoid (CMS) detector has intrinsic rotational symmetries. Data analysis can be greatly improved...

In high-energy physics (HEP), automating the symbolic computation of squared amplitudes is essential for predicting cross-sections and validating...

This project proposes ProtoDiff, a diffusion-based deep learning model designed to denoise astronomical observations of protoplanetary disks from...

This project addresses the challenge of data drift in high-energy physics, where evolving detector conditions cause AI models for data quality...

This project aims to develop and optimize deep learning algorithms for identifying strong gravitational lenses within wide-field surveys such as the...

This project intends to develop cutting-edge machine-learning tools for spectral analysis to characterize the atmospheres of exoplanets. The project...

This project aims to uncover fundamental symmetries and conserved laws hidden within complex high-energy physics data (specifically CMS datasets). It...

My project focuses on developing diffusion models for gravitational lensing simulations. I will implement and evaluate various diffusion-based...

This project aims to develop a vision foundation model for strong gravitational lensing by comparing various self-supervised learning techniques,...

This project aims to develop a cutting-edge computational pipeline—"Latent Neural Signatures in Clinical vs. Neurotypical Dyads: A CEBRA Pipeline"—to...

The project, "Data Processing Pipeline for the LSST," is designed to bridge the gap between the LSST data ecosystem and DeepLense's deep learning...

Problem: Symbolic regression is crucial for discovering underlying physical laws from data, but traditional methods are often computationally...

This project aims to decode the neural dynamics of social interactions by adapting the CEBRA framework to analyze dyadic EEG data. Focusing on...

Diffusion models have experienced rapid growth in usability, availability, and research. The classical algorithm consists of two main parts: a...

This project aims to implement a Quantum Generative Adversarial Network (QGAN) using the Pennylane framework to explore the advantages of quantum...

This project proposes the development of a foundational model for symbolic regression tailored to high energy physics (HEP). Symbolic regression can...

One of the most important physical quantities in particle physics is the cross section, or a probability that a particular process takes place in the...

This project explores Quantum Kolmogorov–Arnold Networks (QKANs) as a novel and interpretable architecture for analyzing collider data from the...

In particle physics, a cross section is a measure of the likelihood that particles will interact or scatter with one another when they collide. It is...

High-fidelity detector simulations are critical for accurate analysis in particle physics, but traditional Monte Carlo-based methods are...

This project aims at incorporating Physics informed neural network (PINN) based ODE solvers into the Diffusion probabilistic models(DPM) , to build a...

This project investigates the potential of quantum machine learning methods, such as quantum feature encoding and quantum neural networks (QNNs), for...

The project aims at building foundational models suitable for characterizing the vast astronomical data and emphasizing their use case for various...

Accurately classifying particle collisions—whether distinguishing quark- from gluon-initiated jets or isolating Higgs events from complex...