Posts by Collection

Research Interests

, 1900.

Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting.

Panja, M., Chakraborty, T., Kumar, U., and Hadid, A.

International Conference on Neural Information Processing, 2023.

An ensemble neural network approach to forecast Dengue outbreak based on climatic condition.

Panja, M., Chakraborty, T., Nadim. Sk., Ghosh. I., Kumar, U., and Liu, N.

Chaos, Solitons and Fractals, 2023.

Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events.

Dutta, A., Panja, M., Kumar, U., Hens, C., and Chakraborty, T.

Advances in Neural Information Processing Systems (NeurIPS) - AI4Science, 2023.

Epicasting: An Ensemble Wavelet Neural Network for Forecasting Epidemics.

Panja, M., Chakraborty, T., Kumar, U., and Liu, N.

Neural Networks, 2023.

Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art.

Chakraborty, T., Reddy U., Naik, S.M., Panja, M., and Manvitha, B.

Machine Learning: Science and Technology, 2024.

Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention.

Jakhmola, Y., Panja, M., Mishra, N.K., Ghosh, K., Kumar, U., and Chakraborty, T.

IEEE Access, 2024.

Analysis of Regional Air Quality Data using Extreme Value Theory and Deep Learning Models.

Thottolil, R., Panja, M., and Kumar, U.

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2025.

Zero-shot forecasting of epidemics.

Panja, M., Modak, O., Younes, G., and Chakraborty, T.

Advances in Neural Information Processing Systems (NeurIPS) - BERT2S, 2025.

Skew Probabilistic Neural Networks for Learning from Imbalanced Data.

Naik, S.M., Chakraborty, T., Panja, M., Hadid, A., and Chakraborty, B.

Pattern Recognition, 2025.

Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers.

Panja, M., Das, D., Ray, A., Chakraborty, T., Athulya, R., Hens, C., Dana, S. K., Murukesh, N., and Ghosh, D.

Chaos, 2025.

Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak.

Barman, M., Panja, M., Mishra, N., and Chakraborty, T.

Machine Learning, 2025.

ESTGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting.

Panja, M., Chakraborty, T., Biswas, A., and Deb, S.

Journal of the Royal Statistical Society: Series A, 2026.

Neural ARFIMA model for forecasting BRIC exchange rates with long memory under oil shocks and policy uncertainties.

Chakraborty, T., Besher, D., Panja, M., and Sengupta, S.

Under Review, 2026.

EnTransformer: A Deep Generative Transformer for Multivariate Probabilistic Forecasting.

Pathak, R., Goswami, R., Panja, M., Ghosh, P., and Chakraborty, T.

Under Review, 2026.

Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series.

Panja, M., Younes, G., and Chakraborty, T.

Under Review, 2026.

Talk 1 on Relevant Topic in Your Field

UC San Francisco, Department of Testing, 2012.

Tutorial 1 on Relevant Topic in Your Field

UC-Berkeley Institute for Testing Science, 2013.

Talk 2 on Relevant Topic in Your Field

London School of Testing, 2014.

Conference Proceeding talk 3 on Relevant Topic in Your Field

Testing Institute of America 2014 Annual Conference, 2014.

Teaching experience 1

University 1, Department, 2014.

Teaching experience 2

University 1, Department, 2015.