About Me
Open-source contributor with experience building LLM pipelines, time-series forecasting models, and applied deep learning projects.
Open Source Contributions
Selected work across time-series and ML tooling
PyTorch Forecasting
Led a major refactor introducing LightningDataModule for scalable dataset pipelines, reducing coupling and enabling faster onboarding for contributors. Also, added the test framework for testing new models. And added models like xLSTMTime
![[WIP] PyQit - Quantum Computing Framework](/quantum.jpeg)
[WIP] PyQit - Quantum Computing Framework
A high-level quantum machine learning framework built on PennyLane. It aims to make quantum machine learning more accessible by reducing the steep learning curve, streamlining the boilerplate code required for training, and providing mathematically rigorous diagnostics.
xLSTMTime – Time-Series Forecasting
Implemented the xLSTMTime architecture combining sLSTM and mLSTM with exponential gating and enhanced memory for long-term multivariate forecasting. Benchmarked on electricity demand and traffic datasets with 5–10% lower MSE/MAE than standard LSTM baselines and outperforming transformers on select horizons.
