Dr Tanujit Chakraborty

Research Interests

• Data Science
• Machine Learning and Deep Learning
• Time Series Forecasting and Econometrics
• Generative AI in Geoscience and Climate

PhD in

• Quality, Reliability & Operations Research (QROR)

Eduation

• Doctor of Philosophy from Indian Statistical Institute, Kolkata, India (Date of Defense: November 27, 2020).

Awards and Grants

• May, 2021 - April, 2024 : Mphasis Research Grant in Cognitive Computing; Role: Co-PI, Funding: 30 lakhs INR, Duration: 3 years.
• Best Paper Award Winner at ACM International Conference on Data Sciences and Management of Data (CODSCOMAD) in Jan, 2021.
• Best Student Paper Award (Application Category) at the International Conference held at IIM Ahmedabad in December 2019.
• B.G. Raghavendra Memorial Award from Operational Research Society of India (ORSI) in December 2017

Publications Books

• Chakraborty, Tanujit. “Some Nonparametric Hybrid Predictive Models: Asymptotic Properties and Applications.” (2020), Link: http://library.isical.ac.in:8080/jspui/handle/10263/7076

Book chapters

• Chakraborty, T., Ghosh, I., Mahajan, T., Arora, T. (2021). Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges. In Modelling, Control and Drug Development for COVID-19 Outbreak Prevention, Springer, Link: https://link.springer.com/chapter/10.1007/978-3-030-72834-2_29

Journal Articles

• Chakraborty, T., Reddy KS, U., Naik, S. M., Panja, M., & Manvitha, B. (2023). Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art. Machine Learning: Science and Technology. Link: https://iopscience.iop.org/article/10.1088/2632-2153/ad1f77
• Chakraborty, T., Kamat, G., Chakraborty, A. K. (2023). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Vol. 65. Link: https://onlinelibrary.wiley.com/doi/10.1111/anzs.12386
• Panja, M., Chakraborty, T., Kumar, U., & Liu, N. (2023). Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemic. Neural Networks, Vol. 165, pg. 185-212. Link: https://www.sciencedirect.com/science/article/abs/pii/S0893608023002939
• 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. Link: https://www.nature.com/articles/s41598-023-49343-3
• 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. Link: https://www.sciencedirect.com/science/article/abs/pii/S0960077923000255
• Chakraborty, T., Kamat, G., Chakraborty, A. K. (2022). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Accepted for Publication.
• 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, Accepted for Publication.
• Chakraborty, T., Das, S., & Chattopadhyay, S. (2022). A New Method for Generalising Burr and Related Distributions. Mathematica Slovaca, Accepted for Publication.
• Ray, A., Chakraborty, T., & Ghosh, D. (2021). Optimised ensemble deep learning framework for scalable forecasting of dynamics containing extreme events. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31, 111105, Link: https://doi.org/10.1063/5.0074213
• 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. 1-24, Link: https://doi.org/10.1007/s13278-021-00751-1
• 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, Link: https://doi.org/10.1007/s10614-020-10040-2
• Ghosh, I., 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, pg. 1-15, Link: https://doi.org/10.1142/S1793962321410014
• 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, Link: https://ieeexplore.ieee.org/document/9194340
• 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, Link: https://doi.org/10.1002/asmb.2473
• 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, Link: https://doi.org/10.1080/23737484.2020.1740065
• 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, Link: https://doi.org/10.1016/j.chaos.2020.109850
• 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, Link: https://doi.org/10.1016/j.cam.2019.05.013
• 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, Link: https://doi.org/10.1016/j.spl.2019.01.021
• Chakraborty, T., Chakraborty, A. K., & Mansoor, Z. (2019). A hybrid regression model for water quality prediction. Opsearch, Vol. 56, pg. 1167-1178, Link: https://doi.org/10.1007/s12597-019-00386-z
• 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, Link: https://doi.org/10.1016/j.physa.2019.121266
• Chakraborty, T., Chattopadhyay, S., & Chakraborty, A. K. (2018). A novel hybridisation of classification trees and artificial neural networks for selection of students in a business school. Opsearch, Vol. 55, pg. 434-446, Link: https://doi.org/10.1007/s12597-017-0329-2

Conference Articles

• Panja, M., Chakraborty, T., Kumar, U., & Hadid, A. (2023). Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting. International Conference on Neural Information Processing. Link: https://link.springer.com/chapter/10.1007/978-981-99-8178-6_35
• 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 AI for Science. Link: https://openreview.net/pdf?id=OQXCc21rgM
• Elabid, Z., Chakraborty, T., & Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE. Link: https://ieeexplore.ieee.org/abstract/document/10069213
• Sasal, L., Chakraborty, T., & Hadid, A. (2022). W-Transformers: A Wavelet-based Transformer Framework for Univariate Time Series Forecasting. IEEE International Conference on Machine Learning and Applications (ICMLA). Link: https://ieeexplore.ieee.org/abstract/document/10069146
• Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., & Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. IEEE International Joint Conference on Neural Networks (IJCNN). Link: https://ieeexplore.ieee.org/document/9533747
• Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Uncovering patterns in heavy-tailed networks : A journey beyond scale-free. In 8th ACM IKDD CODS and 26th COMAD. Link: https://dl.acm.org/doi/10.1145/3430984.3431021

Refereed for Journals and Conferences

I have worked as a reviewer for the following Journals: Journal of the American Statistical Association, IEEE TNNLS, Neural Networks, SIAM Journal on Applied Dynamical Systems (SIADS), Reliability Engineering & System Safety, Journal of Applied Statistics, Journal of Computational and Applied Mathematics, Expert Systems with Applications, Scientific Reports, Stochastic Environmental Research and Risk Assessment, Computational Economics, Computer Networks, Engineering Applications of Artificial Intelligence (EAAI), IEEE Journal of Biomedical and Health Informatics, PLOS Digital Medicine, The American Journal of Tropical Medicine and Hygiene, Evolutionary Intelligence, Neuroscience Informatics, Sankhya Series A, Opsearch, Sadhana, Heliyon, Tropical Medicine and International Health, Economies, Clinical Epidemiology, Frontiers in Medicine.