2023
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
Neural Networks, 2023.
Research group
ForeML Lab
Fusing statistics and machine learning to forecast complex spatiotemporal systems
ForeML Lab is a non-profit research group led by Tanujit Chakraborty, developing statistically principled machine-learning methods for forecasting, health, climate and complex dynamical systems.
Research Areas
We develop statistical and machine-learning methods for real-world problems in forecasting, health, climate and complex systems.
Spatiotemporal Forecasting
Forecasting complex spatial and temporal processes in climate, epidemiology, and environmental systems.
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Health AI
Statistical and AI methods for digital health, epidemiology, and healthcare decision-making.
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Statistical Machine Learning
Statistically principled learning methods for inference, prediction, and uncertainty quantification.
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Generative AI
Generative models and modern AI methods for scientific and application-driven problems.
Explore researchFeatured Research
Selected work from the group spanning forecasting, spatiotemporal modeling, imbalanced learning and probabilistic generative methods.
2026
Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics
Transactions on Machine Learning Research, 2026.
People
Investigators
Tanujit Chakraborty
Associate Professor of Statistics and Data Science
Sorbonne Université Abu Dhabi
Health AI
Statistical Machine Learning
Generative Deep Learning
Spatiotemporal Forecasting
Current Members
Madhurima Panja
Co-PI, Researcher
SAFIR, Sorbonne Université Abu Dhabi
Time Series Forecasting
Deep Learning
Nilasha Majumdar
Research Assistant
SAFIR, Sorbonne Université Abu Dhabi.
Aug 2026 - present
Spatiotemporal Deep Learning
Muhsina V S
Research Assistant
SAFIR, Sorbonne Université Abu Dhabi.
Oct 2026 – present
Probabilistic Forecasting
Join the lab
Join ForeML Lab
Work with us on forecasting, statistical machine learning, health AI and complex dynamical systems. Prospective Master's and PhD students should review our research areas and preparation guidelines before contacting us.