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Short Biography

I am Dr. Sanjit Kumar, an assistant professor in the Department of Computer Science Engineering of Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat, India. My current research interests are image processing, computer vision, machine learning, deep learning, and time series forecasting. Application of hybrid, ensemble, standalone machine learning and deep learning models and developed tools in inter-disciplinary areas like hydrology and sediment transport. Attention gate and Recurrent Residual Convolutional Neural Network-based UNet deep learning model are used in flood area segmentation. Image outpainting for extrapolation and image aesthetics to recommend lens among triplet lens cameras. Image inpainting model using GAN model with accurate text mask ROI generation and inpainting of the text ROIs for seamless overlay of the target text. In 2024, I received my Ph.D. from the Computer Science and Engineering Department at the Indian Institute of Technology Patna, India. In 2019, I received my M.Tech. in Computer Science and Engineering from the National Institute of Technology, Agartala. In 2017, I received my B.Tech. in Computer Science and Engineering from Motihari College of Engineering.

Publication

Google Scholar id: QdYtAXwAAAAJ Scopus id: 57415947500 Orcid id: 0000-0001-6000-6365

Research gate: https://www.researchgate.net/profile/Sanjit-Kumar-4

Journals

Sanjit Kumar, M Agarwal, U Rathnayake, Hybrid Machine Learning for Predicting Particle Froude Number in Auto-Washout Drainage Systems with Sedimented Beds, Journal of Data Science and Intelligent Systems (2026). https://doi.org/10.47852/bonviewJDSIS62028290
C Suman, K Rana, Sanjit Kumar, GV Singh, Knowing When Not to Answer: Mitigating Social Bias in LLMs via Epistemic Abstention, Transactions on Machine Learning Research (2026). https://openreview.net/pdf?id=UT5E31pYob
Sanjit Kumar, M Agarwal, U Rathnayake, Standalone and Novel Hybrid ML Models for Estimating Stream Velocities in a River Channel with Vegetation, Evolving Earth (2026). https://doi.org/10.1016/j.eve.2026.100115
Sanjit Kumar, V Deshpande, M Agarwal, Standalone and Hybrid machine learning approaches to predict sediment load in an alluvial channel, Engineering Applications of Artificial Intelligence (2025). https://doi.org/10.1016/j.engappai.2025.110578
Sanjit Kumar, B Kirar, M Agarwal, V Deshpande, U Rathnayake, Efficient Functioning of a Sewer System: Application of Novel Hybrid Machine Learning Methods for the Prediction of Particle Froude Number, Journal of Hydroinformatics (2024). https://doi.org/10.2166/hydro.2024.065
Sanjit Kumar, G Oliveto, V Deshpande, M Agarwal, U Rathnayake, Forecasting of Time-Dependent Scour Depth based on Bagging and Boosting Machine Learning Approaches, Journal of Hydroinformatics (2024). https://doi.org/10.2166/hydro.2024.047
Sanjit Kumar, M Agarwal, V Deshpande, J R Cooper, K Khosravi, N Rathnayake, Y Hoshino, K Kantamaneni, U Rathnayake, AI-Driven Predictions of Geophysical River Flows with Vegetation, Scientific Reports Nature Springer (2024). https://doi.org/10.1038/s41598-024-67269-2
Sanjit Kumar, V Deshpande, M Agarwal, U Rathnayake, Forecasting Particle Froude Number in Non-Deposition Scenarios within Sewer Pipes through Hybrid Machine Learning Approaches, Results in Engineering Elsevier (2024). https://doi.org/10.1016/j.rineng.2024.102320
Sanjit Kumar, B Kumar, V Deshpande, M Agarwal, Predicting flow velocity in a vegetative alluvial channel using standalone and hybrid machine learning techniques, Expert Systems with Applications Elsevier (2023). https://doi.org/10.1016/j.eswa.2023.120885
Sanjit Kumar, MK Goyal, V Deshpande, M Agarwal, Estimation of Time Dependent Scour Depth around Circular Bridge Piers: Application of Ensemble Machine Learning Methods, Ocean Engineering Elsevier (2023). https://doi.org/10.1016/j.oceaneng.2022.113611
Sanjit Kumar, B Kirar, M Agarwal, V Deshpande, Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes, Natural Hazards Springer (2022). https://doi.org/10.1007/s11069-022-05786-x
ML Pattanaik, Sanjit Kumar, R Choudhary, M Agarwal, B Kumar, Predicting the abrasion loss of open-graded friction course mixes with EAF steel slag aggregates using machine learning algorithms, Construction and Building Materials Elsevier (2022). https://doi.org/10.1016/j.conbuildmat.2022.126408

Conferences

L Kumari, G Srivastava, Sanjit Kumar, Optimizing Intrusion Detection Systems Using Machine Learning for Enhanced Cybersecurity and Threat Prevention, International Conference on Advancement In Cyber Security and Digital Forensics (11 - 12 April 2025), University of Petroleum and Energy Studies (UPES), Dehradun, India. https://doi.org/10.1007/978-981-95-3701-3_8
L Kumari, Sanjit Kumar, S Chandra, G Srivastava, Anaphora Resolution through Salience in Magahi: A Syntactic and Semantic Analysis, ICIMMI '24: Proceedings of the 6th International Conference on Information Management & Machine Intelligence (23 - 24 December 2024), Poornima Institute of Engineering and Technology, Sitapura, Jaipur, Rajasthan, India. https://doi.org/10.1145/3745812.3745898
L Kumari, Sanjit Kumar, K Bhattacharjee, G Srivastava, The Power of AI in Banking 4.0: Enhancing Customer Experience and Operational Efficiency, ICIMMI '24: Proceedings of the 6th International Conference on Information Management & Machine Intelligence (23 - 24 December 2024), Poornima Institute of Engineering and Technology, Sitapura, Jaipur, Rajasthan, India. https://doi.org/10.1145/3745812.3745899
Sanjit Kumar, M Agarwal, V Deshpande, Estimation of particle Froude Number in Deposited Bed Condition using Hybrid Machine Learning Models, ADCIS 2023 - International Conference on Advances in Data-driven Computing and Intelligent (21-23 September 2023), BITS Pilani, K K Birla Goa Campus, India. https://doi.org/10.1007/978-981-99-9518-9_14
Sanjit Kumar, M Agarwal, V Deshpande, Predict Total Sediment Load using Standalone and Ensemble Machine Learning Models, ADCIS 2023 - International Conference on Advances in Data-driven Computing and Intelligent (21-23 September 2023), BITS Pilani, K K Birla Goa Campus, India. https://doi.org/10.1007/978-981-99-9518-9_29
Sanjit Kumar, M Agarwal, V Deshpande, Radial Basis Function Regression (RBFR), AR-RBFR models for estimation of Particle Froude Number in sewer pipes under deposited conditions, ISCON 2023 - International Conference on Information Systems and Computer Networks (03-04 March 2023), GLA University, Mathura, Uttar Pradesh. https://doi.org/10.1109/ISCON57294.2023.10112031
Sanjit Kumar, M Agarwal, V Deshpande, MK Goyal, Estimation of time-dependent pier scour depth using Ensemble and Boosting based Data Driven Approaches, HYDRO 2021- International Conference (Hydraulics, Water Resources and Coastal Engineering)(23-25 December 2021), SVNIT Surat. https://doi.org/10.1007/978-981-99-1901-7_48

Activity

Presentations

Sanjit Kumar, M Agarwal, V Deshpande, MK Goyal, Estimation of time-dependent pier scour depth using Ensemble based Data Driven Approaches, RSD 2022- Research Scholars' Day (23-24 March 2022) IIT Patna.
Sanjit Kumar, M Agarwal, V Deshpande, MK Goyal, A Comparison between ensemble learning and support vector machine applied as regressor on scour depth prediction, INDICON 2021- IEEE Indian Council Annual International Conference (19-21 December 2021) IIT Guwahati.
N Kumari, D Bhattacharya, Sanjit Kumar, Application of Machine learning Algorithm for Financial Trading (An empirical approach for financial time-series prediction), International Conference on Emergent Research in Mathematics and Engineering (ICERME-2019)(17-18 May 2019) NIT Agartala.

Workshops / Training

Attended workshop on Theory and Application of Machine Learning, IEEE (17 October 2020) IIT Patna.
Attended workshop on Artificial Intelligence (AI), AICTE Training And Learning (ATAL) Academy (22-26 February 2019) NIT Agartala.
Attended workshop on Designing Smart Cities Based on Internet of Things (IoT), Global Initiative for Academic Networks (GIAN) (11-15 December 2017) NIT Agartala.
Attended training on Computer Network (Hardware) at BIHAR STATE ELECTRONICS DEVELOPMENT CORPORATION LIMITED (A Govt. of Bihar).