Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series.
Under Review, 2026.
Publications
You can also find my articles on my Google Scholar profile.
Journal Articles
Neural ARFIMA model for forecasting BRIC exchange rates with long memory under oil shocks and policy uncertainties.
Under Review, 2026.
ESTGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting.
Journal of the Royal Statistical Society: Series A, 2026.
Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak.
Machine Learning, 2025.
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers.
Chaos, 2025.
Skew Probabilistic Neural Networks for Learning from Imbalanced Data.
Pattern Recognition, 2025.
Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention.
IEEE Access, 2024.
Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art.
Machine Learning: Science and Technology, 2024.
Epicasting: An Ensemble Wavelet Neural Network for Forecasting Epidemics.
Neural Networks, 2023.
An ensemble neural network approach to forecast Dengue outbreak based on climatic condition.
Chaos, Solitons and Fractals, 2023.
Conference Papers
EnTransformer: A Deep Generative Transformer for Multivariate Probabilistic Forecasting.
Under Review, 2026.
Zero-shot forecasting of epidemics.
Advances in Neural Information Processing Systems (NeurIPS) - BERT2S, 2025.
Analysis of Regional Air Quality Data using Extreme Value Theory and Deep Learning Models.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2025.
Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events.
Advances in Neural Information Processing Systems (NeurIPS) - AI4Science, 2023.
Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting.
International Conference on Neural Information Processing, 2023.