TANUJIT CHAKRABORTY

About

Dr. Tanujit Chakraborty is an Associate Professor of Statistics and Data Science at Sorbonne University, Abu Dhabi and Sorbonne Center for AI, Paris. He has obtained his PhD degree from the Indian Statistical Institute, Kolkata, India. His research interests include Machine Learning, Time Series Forecasting, Econometrics, and Health Data Sciences.

Employment

• Sorbonne University Abu Dhabi

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (54)

• Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting
  Communications in Computer and Information Science 2024 DOI: 10.1007/978-981-99-8178-6_35
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• An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
  Madhurima Panja, Tanujit Chakraborty, Sk Shahid Nadim

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  • Indrajit Ghosh
  • Uttam Kumar
  • Nan Liu
  Chaos, Solitons and Fractals 2023 DOI: 10.1016/j.chaos.2023.113124
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• Bayesian neural tree models for nonparametric regression
  Australian and New Zealand Journal of Statistics 2023 DOI: 10.1111/anzs.12386
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• Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics
  Neural Networks 2023 DOI: 10.1016/j.neunet.2023.05.049
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• Forecasting CPI inflation under economic policy and geo-political uncertainties
  arXiv 2023 DOI: 10.48550/arXiv.2401.00249
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• Learning Patterns from Biological Networks: A Compounded Burr Probability Model
  Research Square 2023 DOI: 10.21203/rs.3.rs-3281304
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• Pattern change of precipitation extremes in Bear Island
  arXiv 2023 DOI: 10.48550/arXiv.2312.04502
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• Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model
  arXiv 2023
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• Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model
  Scientific Reports 2023 DOI: 10.1038/s41598-023-49343-3
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• Skew Probabilistic Neural Networks for Learning from Imbalanced Data
  arXiv 2023 DOI: 10.48550/arXiv.2312.05878
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• THOMPSON SAMPLING FOR ZERO-INFLATED COUNT OUTCOMES WITH AN APPLICATION TO THE DRINK LESS MOBILE HEALTH STUDY
  arXiv 2023 DOI: 10.48550/arXiv.2311.14359
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• Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art
  arXiv 2023 DOI: 10.48550/arXiv.2308.16316
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• A New Method for Generalizing Burr and Related Distributions
  Mathematica Slovaca 2022 DOI: 10.1515/ms-2022-0016
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• An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
  arXiv 2022 DOI: 10.48550/arXiv.2212.08323
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• Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
  arXiv 2022 DOI: 10.48550/arXiv.2206.10696
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• Knowledge-based Deep Learning for Modeling Chaotic Systems
  Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 2022 DOI: 10.1109/ICMLA55696.2022.00194
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• Knowledge-based Deep Learning for Modeling Chaotic Systems
  arXiv 2022 DOI: 10.48550/arXiv.2209.04259
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• Nowcasting of COVID-19 Confirmed Cases: Foundations, Trends, and Challenges
  Studies in Systems, Decision and Control 2022 DOI: 10.1007/978-3-030-72834-2_29
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• Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting
  arXiv 2022
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• Searching for Heavy-Tailed Probability Distributions for Modeling Real-World Complex Networks
  IEEE Access 2022 DOI: 10.1109/ACCESS.2022.3218631
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