Projects #

1- Benchmarking Large Language Models(LLMs) for Scientific Computing- July, 2025 #

Working on a collaborative Agentic LLM research project that involves improving LLMs for Material Science, Computational Chemistry and Climate modelling. Benchmarking LLMs to produce better code to empower scientific research. This includes teaching LLMs different scientific disciplines like Physics, Chemistry and Math equations. Creating benchmark dataset for the Agentic system and comparing the generated output with the true values.

2- Exoplanet Atmospheric Characteristics- July, 2025 #

Researching exoplanets by applying Unsupervised Machine Learning Techniques like K-Means Clustering. Working with publicly available protoplanetary disk dataset by ALMA Observatory.

3- Multi Class classification of Gravitational Lensing- June, 2025 #

Physics guided Machine Learning classification of Gravitational Lensing. This serves as a nice introduction to Physical AI [PINN(Physics Informed Neural Nets)].

4- BinaryBrain-fork(Binary Neural Net framework)- May, 2025 #

Contributed to open source BinaryBrain framework- C++ framework based on LUTNet architecture for real time fast edge Inference on FPGAs. A training infrastructure for Binary Neural Networks. Github

5- Difflogic- A library for Differentiable Logic Gate Networks- March, 2025 #

Contributed to open-source logic gate based neural network library- difflogic(Based on the paper "Convolutional Differentiable Logic Gate Networks"- https://arxiv.org/abs/2411.04732. Github

6- Hardcoded Dataflow style Inference of Logic Gate Networks on FPGA(Part of Bachelors Thesis)- Jan, 2025 #

Designed a low-bit deep neural network Inference engine pipeline that uses UART-based RX/TX to send MNIST image inputs(28x28 and 20x20) to Efinix based FPGA and receive predictions within milliseconds via trained hardcoded Logic Gate Networks [Implementation of the paper "Differentiable Logic Gate Networks"(NeurIPS 2024)] Github

7- Machine Learning for Fundamental Physics- August, 2024 #

This repository contains all the notebooks used for the Machine Learning for Fundamental Physics (ML4FP) School 2024 at Lawrence Berkeley National Laboratory(LBNL). Topics covered are higgs classification, generative AI, introduction to binary classifier, anomaly detection, unfolding, etc. Github

8- Accelerating Deep Neural Networks(DNNs) Papers on FPGA #

Curated list of Deep Neural Networks on FPGAs research papers, covers Binary Neural Nets(BNNs), Logic Gate Networks(LGNs), and Look-Up Table Networks(LUTNets). Github