Journal Publications
Please refer to Google Scholar Profile & RGate Profile for the complete list (including conferences & book chapters).
Please check the GitHub repository for the code and dataset. 
* A PhD student/post-doctoral fellow/mentee of Dr. Chakraborty during the time of the work.

​Submitted:
  1. Pathak, R.*, Roy, A., Chakraborty, T. ANGLE: Angular Neural Generative Learning via Engression. [Preprint | Code | Package]
  2. ​Goswami, R.*, Paul, S.*, Ghosh, P., Chakraborty, T. Long-Memory Reservoir Computing for Data-Scarce Dengue Forecasting. [Preprint | Code | Python Package]
  3. ​Panja, M., D'Agostino, D., Li, H, Chakraborty, T., Liu, N. EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting. [Preprint | Data | Code]
  4. Shukla, I., Panja, M., Chakraborty, T., Hens, C. TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting. [Preprint | Code]
  5. Panja, M., Younes, G., Chakraborty, T. Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series. [Preprint | Code] 
  6. Besher, D.*, Panja, M., Sengupta, S., Chakraborty, T. Neural ARFIMA model for forecasting BRIC exchange rates with long memory. [Preprint | Code | R Package]​
  7. Pathak, R.*, Goswami, R.*, Panja, M., Ghosh, P., Chakraborty, T. Deep Generative Transformers for Probabilistic Time Series and Spatiotemporal Forecasting. [Preprint | Code] 
  8. Sadhukhan, P., Gupta, S., Ghosh, S., Chakraborty, T. Demystifying the trend of the healthcare index: Is historical price a key driver? [Preprint]​ [Best Paper Award at AIRC Conference]​
  9. ​​Sengupta, S.*, Singh, S.K., Chakraborty, T. Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks. [Preprint]
  10. Jahan, N.*, Navas, M., Chakraborty, T., Panja, M. GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting. [Preprint | Code]​​
  11. Mukhiya, K., Luwang, SR., Sharma, B., Hens, C., Nurujjaman, M., Saha, S., Chakraborty, T. Early-Warning Signals of Political Risk in Stablecoin Markets: Human and Algorithmic Behavior Around the 2024 U.S. Election​. [Preprint]​​​​​

Under Revision:

  1. Besher, D.*, Sengupta, A., Chakraborty, T. Modeling US Climate Policy Uncertainty: From Causal Identification to Probabilistic Forecasting. [Preprint | Code]
  2. Nareti, U.K., Gupta, D., Adak, C., Chattopadhyay, S., Riese, E., Chakraborty, T., Agarwal, M., Kumar, S. Assessing Engineering Student Perceptions of Introductory CS Courses in an Indian Context​. [Preprint]
  3. Mukhiya, K., Luwang, SR., Nurujjaman, M., Chakraborty, T., Saha, S., Hens, C. Universal Patterns in the Blockchain: Analysis of EOAs and Smart Contracts in ERC20 Token Networks. [Preprint]​​​​​

Published Papers (Journal):​

  1. Pathak, R.*, Chakraborty, T. (2026) Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics. Transactions on Machine Learning Research. [Paper | Code | Python Package] [Best Student Presentation Award at 46th ISF, Montreal, Canada]
  2. ​Wang, X., Chakraborty, T., Chakraborty, B. (2026) Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes with Ordinal Outcomes. Statistics in Medicine. [Paper | Code]​
  3. Panja, M., Chakraborty, T., Biswas, A.*, Deb, S. (2026) E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting. Journal of the Royal Statistical Society: Series A. [Paper | Code]
  4. Wang, Z., Lee, J. W., Chakraborty, T., Ning, Y., Liu, M., Feng, X., Ong, M. E. H., Liu, N. (2026) Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after admission. Health Data Science. ​[Paper | Code]​
  5. Ghosh, P. Karmakar, B., Jain E., Neeraja J., Banerjee B., Chakraborty, T. (2026) MDAS: A Diagnostic Approach to Assess the Quality of Data Splitting in Machine Learning. IEEE Access. [Paper | Code]​​
  6. Liu, X., Deliu, N., Chakraborty, T., Bell, L., Chakraborty, B. (2025) Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. Annals of Applied Statistics, Vol. 19, pg. 1403-1425. [Paper | R Package] ​
  7. Barman, M.*, Panja, M., Mishra, N., Chakraborty, T. (2025) Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak. Machine Learning, Vol. 114. [Paper | Code]
  8. Naik, S.M.*, Chakraborty, T., Panja, M., Hadid, A., Chakraborty, B. (2025) Skew Probabilistic Neural Networks for Learning from Imbalanced Data. Pattern Recognition, Vol. 165. [Paper | Code]​​
  9. Sadhukhan, P., Chakraborty, T. (2025) Footprints of Data in a Classifier: Understanding the Privacy Risks and Solution Strategies. Information Systems Frontiers [Paper]​ ​
  10. Banerjee, A., Gupta, S., Priyanshu, P., Kar, A., Saha, R., Chakraborty, T., Ghosh, D., Kurths, J., Hens, C. (2025) ​Recent Changes in Spatiotemporal Patterns of Heat Extremes in South Asia. npj Climate and Atmospheric Science, Vol. 8. [Paper]
  11. Ghosh, A., Das, P., Chakraborty, T., Das, P., Ghosh, D. (2025) Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study. Journal of Theoretical Biology, Vol. 605. [Paper | Code]
  12. Panja, M., Das, D., Chakraborty, T., Ray, A., Radhakrishnan, A., Hens, C., Dana, S.K., Murukesh, N., Ghosh, D. (2025) Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers. Chaos, Vol. 35. [Paper | Code]
  13. Shukla, P.K., Chakraborty, T., Sari, M., Sarout, J., Mandal, P.P. (2025) Salt rock creep deformation forecasting using deep neural networks and analytical models for subsurface energy storage applications. Scientific Reports, Vol. 15. [Paper]​
  14. Chakraborty, T., Chattopadhyay, S., Das, S., Naik, S.M., Hens, C. (2025) A Compounded Burr Probability Distribution for Fitting Heavy-Tailed Data with Applications to Biological Networks. Chaos, Vol. 35. [Paper | Code]​
  15. Goswami, R.*, Garai, A.*, Sadhukhan, P., Ghosh, P., Chakraborty, T. (2025) Shape Penalized Decision Forests for Imbalanced Data Classification. IEEE Access, Vol. 13, pg. 86380-86395. [Paper | Python Package] 
  16. Das, D., Radhakrishnan, A., Chakraborty, T., Ray, A., Hens, C., Dana, S.K., Ghosh, D., Murukesh, N. (2025) Pattern change of precipitation extremes in Svalbard. Scientific Reports, Vol. 15, pg. 1-13. [Paper] ​​
  17. Sengupta, S.*, Chakraborty, T., Singh, S.K. (2024) Forecasting CPI inflation under economic policy and geo-political uncertainty. International Journal of Forecasting, Vol. 41, pg. 953-981. [Paper | Code]
  18. Borah, J., Chakraborty, T., Nadzir, M., Cayetano, M., Benedetto, F., Majumdar, S. (2024). A Novel Hybrid Approach For Efficiently Forecasting Air Quality Data. IEEE Sensor Letters, Vol. 9, pg. 1-4.  [Paper | Code]​​
  19. Chakraborty, T., Reddy, U., Naik, S.M.*, Panja, M., Manvitha, B. (2024). Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art. Machine Learning: Science and Technology, Vol. 5, pg. 1-35. [Paper] [Most Cited Paper]
  20. Jakhmola, Y.*, Panja, M., Mishra, N.K., Ghosh, K., Kumar, U., Chakraborty, T. (2024). Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention. IEEE Access, Vol. 12, pg. 188797-188812. [Paper | Code]
  21. Hadid, A., Chakraborty, T., Busby, D. (2024). When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges. Expert Systems, Vol. 41, pg. 1-16. [Paper] [Top Cited Paper Award 2025]
  22. ​Panja, M., Chakraborty, T.*, Kumar, U., Liu, N. (2023). Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics. Neural Networks, Vol. 165, pg. 185-212.  ​[Paper | Code | Medium Post | R Package]
  23. Chakraborty, T., Kamat, G.*, Chakraborty, A. K. (2023). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Vol. 65, pg. 1-26. [Paper | Code] ​
  24. Panja, M., Chakraborty, T., Nadim. Sk., Ghosh, I., Kumar, U., Liu, N. (2023). An ensemble neural network approach to forecast Dengue outbreak based on climatic condition. Chaos, Solitons & Fractals, Vol. 167, pg. 1-14. [Paper | Code]
  25. Thottolil, R., Kumar, U., Chakraborty, T. (2023). Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model. Scientific Reports, Vol. 13, pg. 1-18. [Paper | Code] 
  26. Bhattacharyya, A., Chakraborty, T., Rai, S. N. (2022). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Nonlinear Dynamics, Vol. 107, pg. 3025-3040. ​[Paper | Code]
  27. Chakraborty, T., Chattopadhyay, S., Das, S., Kumar, U., Jayavelu, S. (2022). Searching for heavy-tailed probability distributions for modeling real-world complex networks. IEEE Access, Vol. 10, 115092-115107. ​[Paper | Code]  
  28. Chakraborty, T., Das, S., Chattopadhyay, S. (2022). A New Method for Generalizing Burr and Related Distributions. Mathematica Slovaca, Vol. 72, pg. 241-264. [Paper]
  29. Ghosh, I., and Chakraborty, T. (2021). An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19. International Journal of Modeling, Simulation, and Scientific Computing, Vol. 12 [Paper] 
  30. Ray, A., Chakraborty, T., Ghosh, D. (2021). Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events​. Chaos, Vol. 31, pg. 1-15.​ [Paper | Code]
  31. Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Modified Lomax Model: A heavy-tailed distribution for fitting large-scale real-world complex networks.  Social Network Analysis and Mining, Vol. 11, pg. 11-43. [Paper] 
  32. Chakraborty, T., Chakraborty, A. K., Biswas, M., Banerjee, S., Bhattacharya, S. (2021). Unemployment Rate Forecasting: A Hybrid Approach. Computational Economics, Vol. 57, pg. 183-201. [Paper] 
  33. Chakraborty, T., Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals, Vol. 135, pg. 1-10. [Paper | Code] [Most Cited Paper between 2021-2023]
  34. Chakraborty, T., Chakraborty, A. K. (2020). Hellinger Net: A Hybrid Imbalance Learning Model to Improve Software Defect Prediction. IEEE Transactions on Reliability, Vol. 70, pg. 481-494. [Paper]
  35. Chakraborty, T., & Chattopadhyay, S., Chakraborty, A. K. (2020). Radial basis neural tree model for improving waste recovery process in a paper industry. Applied Stochastic Models in Business and Industry, Vol. 36, pg. 49-61. [Paper | Code]
  36. Chakraborty, T., Chakraborty, A. K. (2020). Superensemble classifier for improving predictions in imbalanced data sets. Communications in Statistics - Case Studies and Data Analysis, Vol. 6, pg. 123-141. [Paper]
  37. Chakraborty, T., Chakraborty, A. K., Mansoor, Z. (2019). A hybrid regression model for water quality prediction. Opsearch, Vol. 56, pg. 1167-1178. [Paper]
  38. Chakraborty, T., Chattopadhyay, S., Ghosh, I. (2019). Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications, Vol. 527, pg. 1-8. [Paper]
  39. Chakraborty, T., Chakraborty, A. K., Chattopadhyay, S. (2019). A novel distribution-free hybrid regression model for manufacturing process efficiency improvement. Journal of Computational and Applied Mathematics​, Vol. 362, pg. 130-142. [Paper] ​[Best Paper Award]
  40. Chakraborty, T., Chakraborty, A. K., Murthy, C. A. (2019). A nonparametric ensemble binary classifier and its statistical properties. Statistics & Probability Letters, Vol. 149, pg. 16-23. ​[Paper | Code]​
  41. Chakraborty, T., Chattopadhyay, S., Chakraborty, A. K. (2018). A novel hybridization of classification trees and artificial neural networks for selection of students in a business school. Opsearch, Vol. 55, pg. 434-446. [Paper] [Best Paper Award]

Published Papers (Conference):​

  1. Panja, M., Modak, O.*, Younes, G., Chakraborty, T. (2025). Zero-shot forecasting of epidemics. NeurIPS - BERT2S. [Paper]
  2. Dey, M.*, Chakrabartty, A., Sarkar, D., Chakraborty, T. (2024). Do we really need Foundation Models for multi-step-ahead Epidemic Forecasting? NeurIPS - TSALM. ​[Paper]
  3. Sadhukhan, P., Sengupta, K., Palit, S., Chakraborty, T. (2024). Knowing the class distinguishing abilities of the features, to build better decision-making models. AMCIS. [Paper]
  4. Sadhukhan, P., Chakraborty, T., Sengupta, K. (2024). Deploying model obfuscation: towards the privacy of decision-making models on shared platforms. AMCIS. [Paper]
  5. Panja, M., Chakraborty, T., Kumar, U., Hadid, A. (2023). Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting. ICONIP. ​[Paper | Code]
  6. Dutta, A.*, Panja, M., Kumar, U., Hens, C., Chakraborty, T. (2023). Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events. NeurIPS - AI4Science. ​[Paper | Code]
  7. Sasal, L.*, Chakraborty, T., Hadid, A. (2022). W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting. ICMLA.​ [Paper | Code]
  8. Elabid, Z.*, Chakraborty, T., Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. ICMLA.​ [Paper | Code]
  9. Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., ​Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. IJCNN. [Paper | Code]
  10. Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Uncovering patterns in heavy-tailed networks: A journey beyond scale-free. ACM IKDD. [Paper] ​​[Best Paper Award Winner at ACM IKDD]​

Book Chapters

  1. Chakraborty, T., Ghosh, I., Mahajan, T., Arora, T. (2022). Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges. In Modeling, Control and Drug Development for COVID-19 Outbreak Prevention, Vol. 366, pg. 1023-1064, Springer. ​​​[Paper | Code]
  2. Chakraborty, T., Kumar, U. (2022).​ Loss Function. In Encyclopedia of Mathematical Geosciences, Springer. ​​​[Paper | Code]
  3. Panja, M., Chakraborty, T., Kumar, U. (2022). Data Dictionary. In Encyclopedia of Mathematical Geosciences, Springer. [Paper]

​​© Tanujit Chakraborty