Research Projects
Ongoing and completed research work in Graph Neural Networks and their applications
simplicial-sindy: Higher-Order Structure Discovery from Time Series
Completed
PyPI
A Python package for recovering simplicial complexes — nodes, edges, and triangles — from multivariate time series. Classical network inference returns only pairwise edges; simplicial-sindy fits a sparse polynomial dynamical model (SINDy) with an ADMM group-lasso under a second-order-cone simplicial hierarchy constraint, so the recovered structure is a valid simplicial complex in which every triangle’s three boundary edges are guaranteed present. This exposes genuine higher-order (triadic) interactions rather than pairwise correlations alone, with sliding-window analysis and an optional CUDA backend.
Graph Representation Learning for Fast Detector Simulation
Completed
Quark and gluon jets from high-energy particle collisions produce distinct patterns in jet images, but distinguishing
them remains a core challenge in particle physics. This project applies machine learning, especially GNNs, VAEs, and GVAEs, to detect patterns that differentiate
quark- from gluon-origin jets.
Graph Neural Network for Weather Data Prediction
Active
Developing GNN-based models for predicting weather statistics using spatial-temporal graph representations. The project addresses challenges in streaming graph learning, including catastrophic forgetting and memory consumption optimization. We model weather stations as nodes with geographical connections to capture spatial dependencies in weather patterns.
Joint Entity Recognition and Relation Extraction
Active
Developing GNN-based approaches for simultaneous entity recognition and relation extraction from natural language text. The project aims to construct knowledge graphs by leveraging graph dependencies between words in sentences. Our model uses syntactic and semantic relationships to improve both entity identification and relation classification accuracy.
Study of Social Networks using GNN
Completed
Applied Graph Neural Networks to predict various social network properties including average node degree, clustering coefficients, and average path lengths. The study analyzed how node and edge additions/deletions affect graph structure and compared GNN predictions with existing theoretical models and baseline approaches.
MedEx - Pharmacy Database Software
Completed
Developed a comprehensive software solution for online pharmaceutical database management. Implemented SRS documentation and prototype code for transaction management, including features for customer account management, digital transactions, credit payment systems, and transaction history tracking. Collaborated with team members as part of IACS Computer Science Unit project.